[ { "term": "Common Errors", "definition": "ClinicalTrials.gov “Basic Results” Database 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "DRAFT", "definition": "60 60 • Report two different tables – Serious and Other – Do not report any serious adverse events in the Other Adverse", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Results Database", "definition": "• Submitted data are used to develop basic tables for the public display • Tables must be interpretable by people not familiar with each particular study • Labels for rows, columns, and units of measure must be meaningful and precise 2 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Measure Type", "definition": "• In general, spell out symbols such as – “Percentage” rather than “%” – “Number” rather than “No.” or “#” • Use decimal points (not commas) for the “decimal separator” and commas (not periods) for the “thousands separator” 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Participant Flow", "definition": "• Number STARTED should be consistent with “Enrollment, Actual” in protocol section – Correct “Enrollment, Actual” (or explain inconsistencies in Pre- Assignment Details) • If more than one Period, number COMPLETED for each Period should equal number STARTED for next Period (or explain loss or addition of participants) • If “Milestones” are defined, number for each “Milestone”", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "must be", "definition": "– Less than or equal to number STARTED Period (or number that achieved previous Milestone) – Greater than or equal to number COMPLETED Period (or number that achieved subsequent Milestone) 7 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "June 2006", "definition": "Study Completion Date: October 2007 Primary Completion Date: October 2007 (Final data collection date for primary outcome measure) Basic Results Section: Summary Protocol Section:", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "NOT COMPLETED", "definition": "0 0 0 0 Participant Flow: Overall Study EXAMPLE: Dose Escalation – Different Participants Receive Each Dose (Public View) Arms/Groups", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "level cohort", "definition": "[1] [2] [3] [1] Dose level given only after lower dose was successfully administered [2] Dose level given only after lower dose was successfully administered [3] 2 participants were paired with each dose level of Drug X 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "STARTED", "definition": "4 2 Low Dose (5 mg) 4 2 Medium Dose (50 mg) 4 2 High Dose (100 mg) 4 2", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Helpful Hints", "definition": "• Measure Title • Specific name of scale • Spell out acronym, add acronym in parentheses • Measure Description • Construct/Domain if not clear from Measure Title • e.g., pain, quality of life • Range and direction of scores (e.g., 0 is best; 10 is worst) • Optional: Type of scale • e.g., continuous, ordinal • Unit of Measure • Use “participants,” if applicable (i.e., for categorical data) • Use “units on a scale” or “scores on a scale,” if no other units (i.e., for continuous data) 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "about these values", "definition": "(e.g., is “0” better or worse than “2”?) Correct: Values within each scale category represent number of “participants”", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "BEFORE Revision", "definition": "Brief description added to indicate “directionality”", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "AFTER Revision", "definition": "BEFORE & AFTER Revision (Data Entry View)", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Walking", "definition": "4 3 – Limited Self-Care, Partly Confined to Bed 0 4 – Completely Disabled, No", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Self-Care", "definition": "0 AFTER Revision (Public View)", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Baseline Measures", "definition": "Invalid Data in Total Column 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "possible results", "definition": "• For categories based on continuous measures, provide thresholds when possible – Especially for 2 categories (i.e., dichotomous measures) 22 How to Define a Category:", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Drug X", "definition": "Number of Participants 100 100 Number of participants improved on nausea scale [units: participants]", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Nausea", "definition": "[units: Improved] 40 70 Need to explain the scale: • Range • Directionality “Improved” is not a", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "measurable unit", "definition": "Report both possible outcomes as dichotomous categories: “improved” and “not improved” BEFORE Revision (Public View) 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "values represent", "definition": "number of participants who “improved” Specified: • Range (1-10) • Directionality (1 = severe) • Algorithm (score at 8", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "score and defined", "definition": "“improved” as greater than a 3-point difference) 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Measure Name", "definition": "Assessment of Safety of 10 Dose Levels of Drug X Following 5 Cycles, Consisting of a 2- Week Exposure Period Followed by a 1-Week Rest Period, as Measured by Severe Toxicity and Disease Progression", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Measure Description", "definition": "MTD, as measured by unacceptable toxicity, is exceeded if >33% participants experienced Dose Limiting Toxicities (DLT)", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Data", "definition": "AFTER Revision (Public View) Outcome Measure Name and", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "No", "definition": "Secondary Outcome Measure: Use of Community Health Resources", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "High Dose", "definition": "Number of Participants Analyzed 35 34 Frequency and Magnitude of Antibody Response [units: participants] 17 21 May mean “three-fold or greater increase” BEFORE Revision (Public View)", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Updated Time Frame", "definition": "31 31 Secondary Outcome Measure: Pain Assessment by Patient", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Time Frame", "definition": "Any time during 5 cycles and 30 days", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Measured Values", "definition": "BEFORE Revision (Public View) Reported Statistical Test not directly related to reported Outcome Measure Additional details about the analysis, such as null hypothesis and power calculation: [1] [2] [1] Effect onset is defined as half the time between initial assessment time indicating statistical significance and the previous assessment time. Additional information, such as whether or not the p-value is adjusted for multiple comparisons and the a priori threshold of significance: [2] 2-sided statistical tests at 0.05 significance level [3] 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "needs to be numerical", "definition": "(cannot include “+”) BEFORE Revision (Public View) 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "assessment", "definition": "Population Description AFTER Revision (Public View) 36 of the 48 total participants had documented tumor progression by the 36-month assessment. Analysis Population Description describes results at 36 months Created categories for progression-free survival", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Placebo", "definition": "Number of Participants Analyzed 125 120 Visual Analogue Scale (VAS) Pain Assessment at 1.5 Hours [units: scores on a scale] Least Squares Mean ± Standard Error 0.57 ± 0.08 1.12 ± 0.10 Statistical Analysis 1 for Visual Analogue Scale (VAS) Pain Score at 1.5 Hours 58", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Invalid entry", "definition": "BEFORE Revision (Public View) 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Heart Rate at Rest", "definition": "[units: beats per minute] Mean ± Standard Deviation 72.3 ± 2.7 71.9 ± 3.1 0 ± 0 BEFORE Revision (Public View)", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "provide values for", "definition": "the “mean” and “standard deviation”", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "consistent", "definition": "• Cells (data) represent measures or counts derived from participants within arms or groups – Measure Type (and Measure of Dispersion) needs to be consistent with data being reported – Unit of Measure must be consistent with values – Absolute values are preferable to percentages 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Week 10 to 18", "definition": "Number of Participants Analyzed 88 80 Treatment Satisfaction Questionnaire After 18 Weeks of Treatment [units: scores on a scale] Mean ± Standard Deviation 81 ± 17.46 7.9 ± 12.16 Statistical Analysis 1 for Treatment Satisfaction Questionnaire After 18 Weeks Groups compared (“week 10” vs. “change from week 10 to 18”) not a logical t-test Confidence Interval is not meaningful without an Estimation Parameter (e.g., mean difference, hazard ratio) BEFORE Revision (Public View) 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Pharmacokinetics", "definition": "[units: weeks] 6 BEFORE Revision (Public View) Not clear how to interpret this", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Outcome Measure table", "definition": "• Time Frame: 6 Weeks • Units: Weeks • Outcome Data: 6", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Control", "definition": "Number of Participants Analyzed 28 27 Hours Per Day of Sleep [units: average hours per day] Mean ± Standard Deviation 823 ± 92 864 ± 106 Inconsistency between Units of Measure, “average hours per day,” and Measure Data: value provided is greater than the total number of hours in a day BEFORE Revision (Public View) 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Inconsistent units", "definition": "– should be “Percentage” 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Relapse Rate", "definition": "[units: number of relapses] 86 91 BEFORE Revision (Public View) Incorrect Outcome Measure Title: Units and Measure Data provide values for “number of relapses,” not “rate” (or a quantity in relation to another unit, e.g., “relapses per unit time”) Alternatively, if Outcome Measure Title and Measure Data provide values for numbers of participants that “relapsed,” then the Units should be “participants” 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Information", "definition": "• Outcome Measure Title, Description – Name and description of measure must be informative to people not familiar with study – If categorized, need description of categories – Use neutral words in Title (e.g., “treatment response” rather than “improvement” or “increased response”) • Units should directly reflect data in the table • Viewers of the table should be able to understand what the numbers represent 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Yes", "definition": "Primary Outcome Measure: Maximum Tolerated Dose (MTD)", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Indicates measure is", "definition": "“number of alerts” BEFORE Revision (Public View) 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Discharge", "definition": "Number of Participants Analyzed 90 86 Use of Community Health Resources [units: participants] 4 9 Indicates 4 participants (of 90 or 4.4%) in the “Early Discharge” group used the specified level", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "health resources", "definition": "used – how was it measured? 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "represent measures", "definition": "of “frequency” and “magnitude” “Participants” is not a unit of measure for “frequency” or “magnitude” Best to provide both categories for a dichotomous measure: • < 3x increase • ≥3x increase Best to provide both categories for a dichotomous measure: • < 3x increase • ≥3x increase 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Level 5", "definition": "Number of Participants Analyzed 9 4 9 9 9 Maximum Tolerated Dose (MTD) [units: participants]", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Experienced DLT", "definition": "1 0 3 2 5 Dose Level MTD 0 0 9 0 9 BEFORE Revision (Public View) Mismatch among Measure Name, Description, and Data 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Overall Response Rate", "definition": "0.21 95% Confidence Interval 0.12 to 0.33", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Unevaluable", "definition": "6 Statistical Analysis 1 for Response to Drug X BEFORE Revision (Public View) Outcome Measure reported as categorical data (five categories of “response”) but Statistical Analysis provided as dichotomous data (“Overall Response Rate = Number Responded / Total Participants”) Need information on how the 5 categories were “collapsed” into 2 (i.e., Which of 5 response categories were used in calculating the “Overall Response Rate”?). 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Groups", "definition": "Early Discharge vs. Standard Discharge", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "na", "definition": "4.684 95% Confidence Interval 2.080 to 7.730", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "P-Value", "definition": "0.05 Mean Difference (Net) 9 Statistical Analysis 1 for Parental Stress", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Standard Discharge", "definition": "Number of Participants Analyzed 100 100", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Parental Stress", "definition": "[units: points on a Likert scale] Mean ± Standard Deviation 9.3 ± 1.2 7.8 ± 2.1 Inconsistency between Measure Data and Method of Estimation • Reported Mean Difference: “9” • By Inspection: 9.3 – 7.8 = 1.5 BEFORE Revision (Public View) 1-09-09", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "Events table", "definition": "– Note that a single type of Adverse Event Term (e.g., “asthma”) may appear in both the Serious and Other tables • If possible indicate the level of severity to distinguish “serious” from “other” adverse events (e.g., “asthma – mild and moderate” in the Other table; “asthma – severe” in the Serious table) • If no adverse events occurred, enter “0” for the Total Number Affected data elements – Do not enter 0 if you do not mean to imply that no adverse events", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "occurred", "definition": "60 How to Report Adverse Events:", "sources": [ "FDAAA_CommonErrors.pdf" ], "file": "FDAAA_CommonErrors.pdf", "type": "pdf" }, { "term": "DRAFT – Helpful Hints: Basic Results", "definition": "9-28-09 ClinicalTrials.gov “Basic Results” Database", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "HELPFUL HINTS", "definition": "1. COMMON STUDY MODELS a. Parallel Design (see example, pp. 5-11) The Protocol Registration System (PRS) defaults generally accommodate simple parallel design studies. The Arms information from the protocol section will be the default column headings for all tables in the results section (e.g., “Participant Flow: Overall Study” table on p. 6), although these can be changed, if appropriate (see “b. Crossover Design,” below). b. Crossover Design (see example, pp. 12-20) Crossover studies generally require a few modifications to the default settings. For example, the column headings may not be the same for all tables. The attached example uses the randomized groups as the column headings for Participant Flow (pp. 12-13), but uses the overall group as a single column heading for the Baseline Characteristics (p. 14), and each separate intervention as column headings for the Outcome Measures (pp. 15-18). In addition, the Participant Flow is divided into three Periods to accurately reflect the different phases of the crossover study (p. 13). c. Diagnostic Accuracy Studies (see example, pp. 21-29) Diagnostic accuracy studies are studies in which the results are generally displayed in a “2 x 2 table,” in which columns are displayed as “with disease” and “without disease” based on a reference standard; rows are “test positive” and “test negative” based on the experimental diagnostic test. The system can be used to create 2 x 2 tables, as illustrated in the attached example (e.g., “Measured Values” table on p. 25). In addition, the Participant Flow (p. 21) and the Baseline Characteristics (p. 22) may be reported for one group representing the entire study. Sensitivity (e.g., “Statistical Analysis 1…Using Threshold A” on p. 23) and specificity (e.g., “Statistical Analysis 2…Using Threshold A” on p. 24) can be entered as statistical analyses, based on each Outcome Measure (e.g., “Diagnostic Test for Disease Using Threshold A”). Separate Outcome Measures, with associated tables, can be defined based on the use of different thresholds (or positivity criteria) in order to display data that would underlie a ROC curve (e.g., “Threshold B” beginning on p. 24). The area under the curve can be reported as a statistical analysis after the last relevant 2 x 2 table, as illustrated (e.g., “Statistical Analysis 3…Using Threshold C” on p. 27). d. Studies with Pharmacokinetic Outcome Measures (Bioequivalence Studies) Bioequivalence and other study types include Outcome Measures to assess the pharmacokinetics of an intervention. The system can accommodate pharmacokinetic outcome measures and specific examples are provided (pp. 30- 1 DRAFT – Helpful Hints: Basic Results 9-28-09 35). The Outcome Measure should be fully spelled out and any relevant description should be provided in the Outcome Measure Description. Generally, plasma blood samples are taken at regular time points to assess pharmacokinetics and the Time Frame data element should accurately reflect these time points. Many bioequivalence studies use a cross-over study design and it is recommended that the Crossover Design example (pp.12-20) be reviewed, if it applies. 2. MEASURES a. Measure Type i. Categorical Measures Most categorical measures will use the number of participants as the unit. (However, it is possible that a different unit, such as the number of knees examined, can be used.) The user can define the number of categories (two or more), and should use the data entry screens to fully characterize the categories and the measures that will be entered. Sometimes a dichotomous category is presented with only one of the two categories displayed (e.g., number improved). It is preferable to report both categories explicitly (e.g., number improved and number not improved). Note that it is possible to have a categorical measure with continuous data in each cell, such as mean blood pressure and standard deviation [SD] of participants in each of three baseline diagnostic categories (e.g., “Diastolic Blood Pressure” and “Systolic Blood Pressure” baseline measures on p. 14). In this situation, the unit of measurement will typically not be number of participants, but will be whatever units are used for the measurement (e.g., mm Hg for blood pressure). ii. Continuous Measures Continuous measures require a measure of central tendency (e.g., mean) and a measure of dispersion/uncertainty (e.g., standard deviation). These are selected from the pull down menus that are provided in the results section of the PRS. Note that confidence interval and standard error are measures of dispersion/uncertainty for Outcome Measures, but not for Baseline Measures. iii. Time to Event Measures At this time, time to event measures must be represented as either categorical measures (e.g., 5 year survival) or continuous measures (e.g., mean time to death) (e.g., “Time to Disease Progression” Outcome Measure on p. 7). If desired, a series of categories can be defined to represent time points on a survival curve. 2 DRAFT – Helpful Hints: Basic Results 9-28-09 b. Specific Measure Issues", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Scales", "definition": "Outcomes may be evaluated and reported with a specific scale. In order for the measure and the outcome to be easily understood, users should describe the scale in the Outcome Measure Title, Description, and Units of Measure fields (e.g., “Mean score on the National Library of Medicine (NLM) Pain Scale” Outcome Measure below). Specific items to describe include the following:  Outcome Measure Title: Name of scale (e.g., mean score on NLM Pain Scale)  Outcome Measure Description: o What the scale measures (e.g., severity of pain) o Range and direction (e.g., 0 is no pain and 20 is severe pain) o Other information as appropriate (e.g., whether the scale is ordinal or continuous).  Units of Measure: expressed as “units on a scale,” “scores on a scale,” or “points on a scale” 3 DRAFT – Helpful Hints: Basic Results 9-28-09 4 3. STATISTICAL ANALYSES Statistical analyses are tied to a specific Outcome Measure. The system allows for the entry of p-values and/or confidence intervals. There is no limit to the number of analyses that can be entered for a given Outcome Measure (e.g., four statistical analyses are associated with the Primary Outcome Measure on pp. 7-9). If a p- value is entered, the test used must be specified. Similarly, if a confidence interval is entered, the estimated parameter must be specified. Users are encouraged to use the free text boxes to provide more complete explanations of their analyses. 4. ADVERSE EVENTS The Adverse Event module is optional (until Sept 27, 2009). However, if one chooses to use the module, the required data elements must be provided (e.g., pp. 10-11). There are separate tables for Serious Adverse Events, and for Other Adverse Events (based on frequency). The same event(s) involving the same participants should not be listed in both tables. DRAFT – Parallel Design Example – Public Display 9-28-09 Parallel Design Example This study has been completed. Information provided by Test Organization Study Type: Interventional Study Design: Randomized, Double Blind (Subject, Investigator, Outcomes Assessor),", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Parallel Assignment", "definition": "Interventions: Drug: Drug A Drug: Drug B Drug: Placebo", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Recruitment Details", "definition": "Key information relevant to the recruitment process for the overall study, such as dates of the recruitment", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "and January 2006", "definition": "Pre-Assignment Details Significant events and approaches for the overall study following participant enrollment, but prior to group assignment Participants screened over 3 week period.", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Description", "definition": "Total Number of Participants All participants received the reference test (i.e. the gold standard). Serious Adverse Events Total Number of Participants Total # participants affected/at risk 0/2500", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "NOT COMPLETED", "definition": "2 1 3 Lost to Follow-up 1 0 2", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Adverse Event", "definition": "1 1 1 Baseline Characteristics", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Total", "definition": "Number of Participants [units: participants] 50 50 50 150", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Age", "definition": "[units: years] Mean ± Standard Deviation 57 ± 6", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "years", "definition": "50 50 50 150 >=65 years 0 0 0 0", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Male", "definition": "70 diastolic blood pressure [units: mm Hg] Mean ± Standard Deviation", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Outcome Measures", "definition": "1. Primary Outcome Measure: Maximum Observed Plasma Concentration (Cmax)", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Measure Name", "definition": "Plasma Decay Half-Life (t1/2) Measure Description Plasma decay half-life is the time measured for the plasma concentration to decrease by one half.", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Hide Details", "definition": "Population Description Explanation of how the number of participants for analysis was determined. Includes whether analysis was per protocol, intention to treat, or another method. Also provides relevant details such as imputation technique, as appropriate. Each participant received reference and test drug and is, therefore, included in the analysis population for both the reference and test drug.", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Placebo", "definition": "Total # participants affected/at risk 16/50 4/50 13/50 Gastrointestinal disorders Nausea † # participants affected/at risk # events 4/50 (8%) 4 2/50 (4%) 2 2/50 (4%) 2 Nervous system disorders Headache † # participants affected/at risk", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "No", "definition": "22 DRAFT – Diagnostic Test Accuracy Example – Public Display 9-28-09", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Time Frame", "definition": "0, 1, 2, 3, 4, 6, 8, 12, 24, 48, 72, 96 hours post-dose", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Other Adverse Events", "definition": "Frequency Threshold Above Which Other Adverse Events are Reported: 5% Total Number of Participants Total # participants affected/at risk 128/2500 Gastrointestinal disorders", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "number of events", "definition": "12/50 (24%) 12 2/50 (4%) 2 11/50 (22%) 11 † Indicates events were collected by systematic assessment 10 DRAFT – Parallel Design Example – Public Display 9-28-09 11", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "More Information", "definition": "Certain Agreements: Principal Investigators (PIs) are NOT employed by the organization sponsoring the study. There is NOT an agreement between Principal Investigators and the Sponsor (or its agents) that restricts the PI’s rights to discuss or publish trial results after the trial is completed. 28 DRAFT – Diagnostic Test Accuracy Example – Public Display 9-28-09 29 Limitations and Caveats Limitations of the study, such as early termination leading to small numbers of participants analyzed and technical problems with measurement leading to unreliable or uninterpretable data Only the most experienced technologists participated and were asked to read the test results in this study. Results may not be applicable to those centers without technologists with extensive related experience. Results Point of Contact: Name/Title: Dr. Y Organization: Test Coop phone: 123-457-9087 ext 1234 e-mail: abc@xyz.inc U.S. National Library of Medicine, Contact Help Desk U.S. National Institutes of Health, U.S. Department of Health & Human Services, USA.gov, Copyright, Privacy, Accessibility, Freedom of Information Act DRAFT – Bioequivalence Study Example – Public Display 9-28-09 Pharmacokinetic Outcome Measures (Bioequivalence Study) Example This study has been completed. Information provided by Test Organization", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "then Drug A", "definition": "Placebo twice daily in first intervention period and Drug A 25 mg twice daily in second intervention period (after washout period). Drug A First,", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "then Placebo", "definition": "Drug A 25 mg twice daily in first intervention period and Placebo twice daily in second intervention period (after washout period). 12 DRAFT – Crossover Study Example – Public Display 9-28-09 Participant Flow for 3 periods Period: First Intervention Placebo First,", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "STARTED", "definition": "65 65 Received at Least One Dose of Drug 65 64", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Withdrawal by Subject", "definition": "0 1 Period: Washout Period of 2 Weeks Placebo First,", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Disease relapse", "definition": "2 1 Period: Second Intervention Placebo First,", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Lost to Follow-up", "definition": "1 0 13 DRAFT – Crossover Study Example – Public Display 9-28-09 Baseline Characteristics", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Baseline Measures", "definition": "Total Number of Participants Number of Participants [units: participants] 2600", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "At enrollment", "definition": "138 ± 21.2 Beginning of Placebo treatment 138 ± 18.6 Beginning of Drug A treatment 136 ± 19.7", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "weight", "definition": "[units: kg] Mean ± Standard Deviation 65 ± 11.2 [1] Measurements were taken at baseline, at beginning of 1st and 2nd intervention periods, and end of 1st and 2nd intervention periods. Yielding baseline measurements for treatment with Placebo and Drug A. 14 DRAFT – Crossover Study Example – Public Display 9-28-09", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Drug A", "definition": "Total # participants affected/at risk 5/127 10/127 Gastrointestinal disorders Nausea ‡ # participants affected/at risk # events 5/127 (3.94%) 7 10/127 (7.87%) 12 ‡ Indicates events were collected by non-systematic methods. 19 DRAFT – Crossover Study Example – Public Display 9-28-09 20", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Two-sided", "definition": "2. Primary Outcome Measure: Change from Baseline in Systolic Blood Pressure at 3", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "period and locations", "definition": "2700 participants were selected from multiple primary care sites across the country and all were healthy at baseline without symptoms of disease. Pre-Assignment Details Significant events and approaches for the overall study following participant enrollment, but prior to group", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "assignment", "definition": "100 participants were excluded because they did not properly observe the required pre-diagnostic test routine.", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Protocol Violation", "definition": "100 21 DRAFT – Diagnostic Test Accuracy Example – Public Display 9-28-09 Baseline Characteristics", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Disease", "definition": "Number of Participants Analyzed [units: Participants] 450 2050 Diagnostic Test Data for Disease Using Threshold B [units: participants] Positive diagnostic test for disease using threshold B 400 150 Negative diagnostic test for disease using threshold B 50 1900 Statistical Analysis 1 for Diagnostic Test Data for Disease Using Threshold B Groups [1] Participants With Disease Sensitivity [2] 0.89 95% Confidence Interval ( 0.84 to 0.95 ) [1] Additional details about the analysis, such as null hypothesis and power calculation: No text entered. [2] Other relevant estimation information: No text entered. Statistical Analysis 2 for Diagnostic Test Data for Disease Using Threshold B Groups [1] Participants Without Disease Specificity [2] 0.93 95% Confidence Interval ( 0.87 to 0.99 ) [1] Additional details about the analysis, such as null hypothesis and power calculation: No text entered. [2] Other relevant estimation information: No text entered. 25 DRAFT – Diagnostic Test Accuracy Example – Public Display 9-28-09 3. Primary Outcome Measure: Diagnostic Test Data for Disease Using Threshold C", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Without Disease", "definition": "Number of Participants Analyzed [units: Participants] 450 2050 Diagnostic Test Data for Disease Using Threshold C [units: participants] Positive diagnostic test for disease using threshold C 380 125 Negative diagnostic test for disease using threshold C 70 1925 26 DRAFT – Diagnostic Test Accuracy Example – Public Display 9-28-09 Statistical Analysis 1 for Diagnostic Test Data for Disease Using Threshold C Groups [1] Participants With Disease Sensitivity [2] 0.84 95% Confidence Interval ( 0.80 to 0.88 ) [1] Additional details about the analysis, such as null hypothesis and power calculation: No text entered. [2] Other relevant estimation information: No text entered. Statistical Analysis 2 for Diagnostic Test Data for Disease Using Threshold C Groups [1] Participants Without Disease Specificity [2] 0.94 95% Confidence Interval ( 0.89 to 0.99 ) [1] Additional details about the analysis, such as null hypothesis and power calculation: No text entered. [2] Other relevant estimation information: No text entered. Statistical Analysis 3 for Diagnostic Test Data for Disease Using Threshold C Groups [1]", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "All groups", "definition": "Area Under the Curve [2] 0.91 95% Confidence Interval ( 0.89 to 0.95 ) [1] Additional details about the analysis, such as null hypothesis and power calculation: The Area Under the Curve was estimated based on the sensitivity and specificity measures for each of three thresholds (A, B, and C) [2] Other relevant estimation information: No text entered. 27 DRAFT – Diagnostic Test Accuracy Example – Public Display 9-28-09 Reported Adverse Events", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "nausea", "definition": "# participants affected/at risk # events 128/2500 (5.12%) 130", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Measured Values", "definition": "Reference Drug 150 mgTest Drug 150 mg Number of Participants [units: Participants] 14 14 Plasma Decay Half-Life (t1/2) [units: hours] Least Squares Mean ± Standard Deviation 29.99 ± 4.84 29.99 ± 4.34 U.S. National Library of Medicine, Contact Help Desk U.S. National Institutes of Health, U.S. Department of Health & Human Services, USA.gov, Copyright, Privacy, Accessibility, Freedom of Information Act", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "ANOVA", "definition": "P-Value [3] 0.784755 Other Estimated Parameter [Ratio of AUC (0 - ∞) values] [4] 100.73 90% Confidence Interval (97.96 to 103.36) [1] Additional details about the analysis, such as null hypothesis and power calculation:", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "No text entered", "definition": "[2] Additional information, such as whether or not the p-value is adjusted for multiple comparisons and the a priori threshold for statistical significance: No text entered. [3] Other relevant information, such as adjustments or degrees of freedom: No text entered. [4] Other relevant estimation information: Ratio of AUC (0 - ∞) values = Test Drug 150mg/Reference Drug 150mg 34 DRAFT – Bioequivalence Study Example – Public Display 9-28-09 35 5. Primary Outcome Measure: Plasma Decay Half-Life (t1/2)", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "Test Drug", "definition": "150 mg Number of Participants [units: Participants] 14 14 Time to Reach Maximum Observed Plasma Concentration (Tmax) [units: hours] Mean ± Standard Deviation 2.96 ± 1.00 2.79 ± 1.26 3. Primary Outcome Measure: Area Under the Curve From Time Zero to Last Quantifiable Concentration [AUC (0-t)]", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "mg", "definition": "Number of Participants [units: Participants] 14 14 Area Under the Curve From Time Zero to Extrapolated Infinite Time [AUC (0 - ∞)] [units: mcg*h/mL] Mean ± Standard Deviation 153.33 ± 35.96 154.45 ± 36.81 Statistical Analysis 1 for Area Under the Curve From Time Zero to Extrapolated Infinite Time [AUC (0 - ∞)] Groups [1] Reference Drug 150 mg, Test", "sources": [ "FDAAA_Helpful_Hints_ResultsExamples.pdf" ], "file": "FDAAA_Helpful_Hints_ResultsExamples.pdf", "type": "pdf" }, { "term": "access control", "definition": "Policy and procedure that defines accessibility to a physical space or electronic source of information. The policy usually includes the concept of audit trails, either paper (e.g., signature log) or electronic. adverse drug reaction (ADR) In the pre-approval clinical experience with a new medicinal product or with its new usage (particularly as the therapeutic dose[s] may not be established), all noxious and unintended responses to a medicinal product related to any dose should be considered adverse drug reactions. The phrase “responses to a medicinal product” means that a causal relationship between a medicinal product and an adverse event is at least a reasonable possibility (i.e., the relationship cannot be ruled out). Regarding marketed medicinal products, and ADR is a response to a drug which is noxious and unintended and which occurs at doses normally used in man for prophylaxis, diagnosis, or therapy of diseases or for modification of physiological function (see ICH Guideline for Clinical Safety Data Management: Definitions and Standards for Expedited Reporting2). See also MRCT Center Clinical Research Glossary definition. adverse event (AE) In a subject or clinical-investigation subject administered a pharmaceutical product, any untoward medical occurrence which does not necessarily have a causal relationship with the treatment. An adverse event (AE) can therefore be any unfavorable and unintended sign (including an abnormal laboratory finding), symptom, or disease temporally associated with the use of a medicinal (investigational) product, whether or not related to the medicinal (investigational) product (see the ICH Guideline for Clinical Safety Data Management: Definitions and Standards for Expedited Reporting). See also MRCT Center Clinical Research Glossary definition. amendment (to the protocol) See protocol amendment.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "analysis dataset", "definition": "The final data set, including derived items and excluding redundant data points, which is used to perform the analyses required for safety assessment, efficacy assessment, submission to regulatory authorities, or other review. Can be comprised of one or more data files.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "analysis file", "definition": "Same as analysis dataset in the context of the GCDMP.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "annotated crf", "definition": "A document that maps the names of the collected items to their corresponding database tables, variable item names, forms, visits and any other objects needed for a person to correctly analyze data collected in a clinical trial. Annotated collection documents are required so that any person can understand where variables for analysis originate. applicable regulatory requirement(s) Any law(s) and regulation(s) addressing the conduct of clinical trials of investigational products. Application Service provider (ASP) An application service provider is a vendor who provides, manages and distributes software- based services to customers over a network. approval (in relation to institutional review boards) The affirmative decision of the institutional review board (IRB) that the clinical trial has been reviewed and may be conducted at the institution site within the constraints set forth by the IRB, the institution, Good Clinical Practice (GCP), and the applicable regulatory requirements.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "audit", "definition": "A systematic and independent examination of trial-related activities and documents to determine whether the trial-related activities being evaluated were conducted and the data were recorded, analyzed and accurately reported according to the protocol, the sponsor’s standard operating procedures (SOPs), GCP, and the applicable regulatory requirement(s).", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "audit certificate", "definition": "A declaration of confirmation by the auditor that an audit has taken place.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "audit report", "definition": "A written evaluation by the sponsor’s auditor of the results of the audit.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "audit trail", "definition": "Documentation that allows reconstruction of the course of events.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "batch job", "definition": "A series of processes run in an electronic system that perform specific tasks, such as data validation, query generation, external data upload, or lab reference range normalization.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "biologics", "definition": "A biological product (as a vaccine or blood serum) used in medicine. blinding/masking A procedure in which one or more parties to the trial is kept unaware of the treatment assignment(s). Single-blinding usually refers to the subject(s) being unaware, and double- blinding usually refers to the subject(s), investigator(s), monitor, and, in some cases, data analyst(s) being unaware of the treatment assignment(s).", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "case report form (CRF)", "definition": "A printed, optical, or electronic document designed to record all of the protocol-required information to be reported to the sponsor on each trial subject.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "CDISC", "definition": "Acronym for the Clinical Data Interchange Standards Consortium.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "central lab", "definition": "A vendor contracted for a clinical trial that processes samples collected from subjects and provides the results of laboratory tests or other medical analyses (e.g., ECG results, pathology results) to the sponsor. Refer to the Laboratory Data Handling chapter.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "change control", "definition": "A procedure that defines how planned changes to any part of a computer system are handled in a manner as to maintain compliance with required functionality of that system. The procedure ensures that changes applied to the system do not unexpectedly impact the functionality of the system in question, or any other computer systems. The procedure should also define how unexpected changes to a system are prevented and managed.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "checklist", "definition": "(ASQ) A tool used to ensure that all important steps or actions in an operation have been taken. Checklists contain items that are important or relevant to an issue or situation. Checklists are often confused with check sheets and data sheets.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "CLIA", "definition": "See Clinical Laboratory Improvement Amendments. Clinical Laboratory Improvement Amendments (CLIA) Congress passed the Clinical Laboratory Improvement Amendments (CLIA) in 1988 establishing quality standards for all laboratory testing to ensure the accuracy, reliability and timeliness of patient test results regardless of where the test was performed. See www.fda.gov/medicaldevices/deviceregulationandguidance/ for more information. clinical trial/study Any investigation using human subjects that is intended to discover or verify the clinical, pharmacological, and/or other pharmacodynamic effects of an investigational product(s); and/or to identify any adverse reactions to an investigational product(s); and/or to study absorption, distribution, metabolism, and excretion of an investigational product(s) for the purpose of ascertaining its safety and/or efficacy. The terms “clinical trial” and “clinical study” are synonymous. See also MRCT Center Clinical Research Glossary definition. clinical trial/study report A written description of a trial/study of any therapeutic, prophylactic, or diagnostic agent conducted in human subjects, in which the clinical and statistical description, presentations, and analyses are fully integrated into a single report (see the ICH Guideline for Structure and Content of Clinical Study Reports). See also MRCT Center Clinical Research Glossary definition", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "code libraries", "definition": "A repository of validated programming logic that can be used during the programming of edit checks or other programs used in the collection, review, or analysis of clinical trial data.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "common causes", "definition": "(ASQ) Causes of variation that are inherent in a process over time. They affect every outcome of the process and everyone working in the process. See also special causes. comparator (product) An investigational or marketed product (i.e., active control) or placebo used as a reference in a clinical trial. compliance (in relation to trials) Adherence to all the trial-related requirements, GCP requirements, and the applicable regulatory requirements. See also MRCT Center Clinical Research Glossary definition.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "composite endpoint", "definition": "Overall outcome that the protocol is designed to evaluate based on more than one common endpoint such as myocardial infarction plus repeat intervention.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "compound", "definition": "A chemical molecule with potential pharmacological activity.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "confidentiality", "definition": "Prevention of disclosure of a sponsor’s proprietary information or of a subject’s identity to unauthorized individuals. See also MRCT Center Clinical Research Glossary definition.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "conformance", "definition": "(ASQ) An affirmative indication or judgment that a product or service has met the requirements of a relevant specification, contract, or regulation.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "contract", "definition": "A written, dated, and signed agreement that sets out any arrangements on delegation and distribution of tasks and obligations and, if appropriate, on financial matters between two or more involved parties. The protocol may serve as the basis of a contract. coordinating committee A committee that a sponsor may organize to coordinate the conduct of a multi-center trial. coordinating investigator An investigator assigned responsibility for the coordination of investigators at different centers that are participating in a multi-center trial. contract research organization (CRO) A person or an organization (e.g., commercial, academic, or otherwise) contracted by the sponsor to perform one or more of a sponsor’s trial-related duties and functions. See also MRCT Center Clinical Research Glossary definition.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "control chart", "definition": "(ASQ) A chart with upper and lower control limits on which values of some statistical measure for a series of samples or subgroups are plotted. The chart frequently shows a central line to help detect a trend of plotted values toward either control limit. corrective action (CA) (ASQ) The implementation of solutions that lead to the reduction or elimination of an identified problem.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "CS", "definition": "Clinically Significant. See also MRCT Center Clinical Research Glossary definition.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "data cleaning", "definition": "The process of collecting, reviewing, and confirming modifications to clinical data in such a way that data provided for statistical analysis is complete, accurate, and consistent with other data points.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "data module", "definition": "A category of a type of data, such as CRF.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "database backup", "definition": "A duplicate copy of all electronic data and metadata that can be retrieved in the event of system failure or data corruption.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "database lock", "definition": "The closing of a database after all clinical trial data has been reviewed, queries resolved and issues addressed, such that the database cannot be altered in any way. development/test environment Computer system instances that are used for study build and test, prior to release to the production instance. Defined quality procedures and documentation allow transition of programming code from one instance to another.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "device", "definition": "I. A means of data collection such as a paper CRF, Personal Digital Assistant, or medical instrumentation. II. An instrument, apparatus, implement, machine, contrivance, implant, in vitro reagent, or other similar or related article, including a component part, or accessory which is: recognized in the official National Formulary, or the United States Pharmacopoeia, or any supplement to them, intended for use in the diagnosis of disease or other conditions, or in the cure, mitigation, treatment, or prevention of disease, in man or other animals, or intended to affect the structure or any function of the body of man or other animals, and which does not achieve any of its primary intended purposes through chemical action within or on the body of man or other animals and which is not dependent upon being metabolized for the achievement of any of its primary intended purposes.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "direct access", "definition": "Permission to examine, analyze, verify, and reproduce any records and reports that are important to evaluation of a clinical trial. Any party (e.g., domestic and foreign regulatory authorities, sponsor’s monitors and auditors) with direct access should take all reasonable precautions within the constraints of the applicable regulatory requirement(s) to maintain the confidentiality of subjects’ identities and sponsor’s proprietary information. disaster recovery plan A disaster recovery plan is a comprehensive statement of consistent actions to be taken before, during and after a disaster. The plan should be documented and tested to ensure the continuity of operations and availability of critical resources in the event of a disaster. (www.drj.com)", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "discrepancy", "definition": "Inconsistency in two or more data points collected in a clinical trial that must be addressed prior to database lock.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "documentation", "definition": "All records, in any form (including, but not limited to, written, electronic, magnetic, and optical records and scans, x-rays, and electrocardiograms) that describe or record the methods, conduct, or results of a trial; the factors affecting a trial; and the actions taken.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "double data entry", "definition": "The process of purposely entering clinical trial data twice for studies with paper collection media. The two entries are done independently. The goal is to ensure entry into the electronic system is completed without transcription errors.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "e-CRF", "definition": "Acronym for electronic case report form. An auditable electronic record designed to record information to be reported to the sponsor on each trial subject, as required by the clinical trial protocol. See also case report form. edits - hard and soft edit Programmed or manual verifications performed on a clinical database for the purpose of ensuring a quality final analysis set for analysis. Hard edits refer to verifications that require a data change or entry in order to resolve it while Soft edits also accept a confirmation of the existing data.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "electronic record", "definition": "Electronic record means any combination of text, graphics, data, audio, pictorial, or other information representation in digital form that is created, modified, maintained, archived, retrieved, or distributed by a computer system.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "electronic signature", "definition": "Electronic signature means a computer data compilation of any symbol or series of symbols executed, adopted, or authorized by an individual to be the legally binding equivalent of the individual's handwritten signature.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "electronic submission", "definition": "The set of required documents for a submission, rendered in an acceptable electronic format that is transmitted to a regulatory agency in lieu of paper documents for review and approval.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "endpoint", "definition": "Overall outcome that the protocol is designed to evaluate. Common endpoints are severe toxicity, disease progression, or death. See also MRCT Center Clinical Research Glossary definition.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "essential documents", "definition": "Documents which individually and collectively permit evaluation of the conduct of a study and the quality of the data produced (see ICH E6, Section 8. “Essential Documents for the Conduct of a Clinical Trial”).", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "exposure", "definition": "The condition of being subject to some effect or influence; in context of a clinical trial this generally refers to exposure to the test article/drug.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "external data", "definition": "Data that are collected externally and merged in the CDMS or analyzed together with data collected on the e/CRF.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "false negative", "definition": "A test result that is erroneously classified in a negative category (as of diagnosis) because of imperfect testing methods or procedures. In statistics a Type II error.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "false positive", "definition": "A test result that shows evidence of a result or condition although it is not actually present. In statistics, a Type I error.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "field", "definition": "A particular area (as of a record in a database) in which the same type of information is regularly recorded.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "flag", "definition": "A tag placed on a data point that defines a status (e.g., discrepant, closed, or other status) that indicates an action is required. flow diagram, flow chart A graphic means for depicting the steps or activities that constitute a process. The flow diagram (flow chart) is constructed from standard symbols (the delay and database symbols have been added to Juran’s list4). The activity symbol is a rectangle that designates an activity. Within the rectangle is a brief description of that activity. The decision symbol is a diamond that designates a decision point from which the process branches into two or more paths. The path taken depends on the answer to the question that appears within the diamond. Each path is labeled to correspond to an answer to the question. The terminal symbol is a rounded rectangle that unambiguously identifies the beginning or end of a process. “Start” or “begin” is used to designate the starting point of a process flow. “Stop” or “end” is used to designate the end of process flow. The document symbol is a document pertinent to the process. The flow line represents a process path that connects process elements. The arrowhead indicates the direction of the flow. The connector is a circle that is used to indicate a continuation of the flow diagram. The delay symbol is a rectangle rounded on one side that identifies a waiting point or delay in the process flow. The database symbol is a cylinder that represents a database application and the contained data.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "frozen", "definition": "A temporary locked state for data that allows the generation of queries but does not allow a change to data points.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "global library", "definition": "In a Clinical Data Management System, the superset of all standard objects (e.g., CRF modules, edit checks, fields, etc.). Good Clinical Practice (GCP) A standard for the design, conduct, performance, monitoring, auditing, recording, analyses, and reporting of clinical trials that provides assurance that the data and reported results are credible and accurate, and that the rights, integrity, and confidentiality of trial subjects are protected.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "hard coding", "definition": "Computer programs utilize logic and hardware to allow dynamic responses based on user input. For example, Web site can be programmed to tabulate the total bill when books are selected for purchase on-line or the average weight of the patients in the active treatment arm each time a program is run on a dataset. “Hard coding” is the limiting of the dynamic response by actually typing the data in the computer program itself rather than letting the data come from a dataset or the user. This approach can be dangerous because it is not visible in the analysis tables and listings or to the regulatory authorities and because it is easily forgotten once typed into the computer program.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "hard lock", "definition": "The final state of the database where no changes are permitted and all user access is removed.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "impartial witness", "definition": "A person who is independent of the trial, who cannot be unfairly influenced by people involved with the trial, who attends the informed consent process if the subject or the subject’s legally acceptable representative cannot read, and who reads the informed consent form and any other written information supplied to the subject.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "in-control process", "definition": "(ASQ) A process in which the statistical measure being evaluated is in a state of statistical control (i.e., the variations among the observed sampling results can be attributed to a constant system of chance causes). See also out-of-control process. independent data-monitoring committee (IDMC) (data and safety monitoring board, monitoring committee, data monitoring committee) An independent data-monitoring committee that may be established by the sponsor to assess at intervals the progress of a clinical trial, the safety data, and the critical efficacy endpoints. Such a committee may also recommend to the sponsor whether to continue, modify, or stop a trial. independent ethics committee (IEC) An independent body—i.e., a review board or a committee, whether institutional, regional, national, or supranational, constituted of medical professionals and non-medical members—that is responsible for ensuring the protection of the rights, safety, and well-being of human subjects involved in a trial and to provide public assurance of that protection. These responsibilities are accomplished by, among other things, reviewing and approving/providing favorable opinion on the trial protocol, the suitability of the investigator(s), facilities, and the methods and material to be used in obtaining and documenting informed consent of the trial subjects. The legal status, composition, function, operations, and regulatory requirements pertaining to IECs may differ among countries but should allow the IEC to act in agreement with GCP, as described in this guideline.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "informed consent", "definition": "A process by which a subject voluntarily confirms his or her willingness to participate in a particular trial after having been informed of all aspects of the trial that are relevant to the subject’s decision to participate. Informed consent is documented by means of a written, signed, and dated informed-consent form. See also MRCT Center Clinical Research Glossary definition.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "inspection", "definition": "I. (ICH) The act by a regulatory authority (or authorities) of conducting an official review of documents, facilities, records, and any other resources that are deemed by the authority to be related to the clinical trial and that may be located at the site of the trial, at the sponsor’s and/or contract research organization’s (CRO’s) facilities, or at other establishments deemed appropriate by the regulatory authority. II. (ASQ) Measuring, examining, testing, and gauging one or more characteristics of a product or service and comparing the results with specified requirements to determine whether conformity is achieved for each characteristic. institution (medical) Any public or private entity or agency or medical or dental facility where clinical trials are conducted. institutional review board (IRB) An independent body—constituted of medical, scientific, and non-scientific members—that is responsible for ensuring the protection of the rights, safety, and well-being of human subjects involved in a trial by, among other things, reviewing, approving, and providing continuing review of trial protocol and amendments and of the methods and material to be used in obtaining and documenting informed consent of the trial subjects. See also MRCT Center Clinical Research Glossary definition.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "instrument", "definition": "A device for capturing or measuring the present value of a quantity under observation. interim clinical trial/study report A report of intermediate results and their evaluation based on analyses performed during the course of a trial.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "intervention", "definition": "A method of interfering with the outcome or course, especially of a condition or process. See also MRCT Center Clinical Research Glossary definition. Investigational New Drug application (IND) An IND application is submitted to the FDA when a sponsor or investigator wishes to initiate trials with human subjects. The IND regulations can be found at the following link: https:// www.fda.gov/cber/ind/ind.htm . “ND” is synonymous with “Notice of Claimed Investigational Exemption for a New Drug.” See also MRCT Center Clinical Research Glossary definition. investigational product A pharmaceutical form of an active ingredient or placebo that is being tested or used as a reference in a clinical trial, including a product with a marketing authorization when used or assembled (formulated or packaged) in a way different from the approved form, for an unapproved indication, or to gain further information about an approved use. See also MRCT Center Clinical Research Glossary definition.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "investigator", "definition": "A person responsible for the conduct of the clinical trial at a trial site. If a trial is conducted by a team of individuals at a trial site, the investigator is the responsible leader of the team and may be called the principal investigator. See also subinvestigator. See also MRCT Center Clinical Research Glossary definition. investigator/institution An expression meaning “the investigator and/or institution, where required by the applicable regulatory requirements.”", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "investigator meeting", "definition": "The kickoff meeting for an upcoming trial where the participating investigators review and provide feedback on the protocol or procedures in a protocol. Training of the principal investigator or other site staff on protocol procedures and/or EDC system entry is conducted at the investigator meeting as well. investigator’s brochure A compilation of the clinical and non-clinical data on the investigational product(s) that is relevant to the study of the investigational product(s) in human subjects (see ICH E6, Section 7. “Investigator’s Brochure”).", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "ISO", "definition": "(ASQ) English acronym for International Organization for Standardization. ISO 9000 series standards (ASQ) A set of five individual, but related, international standards on quality management and quality assurance developed to help companies effectively document the elements that should be implemented to maintain an efficient quality system. Initially published in 1987, the standards are not specific to any particular industry, product, or service. The standards were developed by the International Organization for Standardization (ISO), a specialized international agency for standardization that is composed of the national standards bodies of 91 countries.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "legacy system", "definition": "An electronic system previously in production, but no longer actively used, that may contain data needed for current analysis or other use and therefore must be maintained by the sponsor organization. legally acceptable representative An individual, juridical, or other type of body that is authorized under applicable law to consent, on behalf of a prospective subject, to the subject’s participation in the clinical trial.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "local lab", "definition": "Local labs are labs in close proximity to individual clinical study sites or patients and are most often used when timely results are needed.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "MedDRA", "definition": "Medical Dictionary for Regulatory Activities is a medical terminology used to classify adverse event information associated with the use of biopharmaceuticals and other medical products. See www.meddra.org for additional information.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "medical monitor", "definition": "An individual, other than the principle investigator, who evaluates clinical trial data from a safety perspective.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "medical monitoring", "definition": "The act of evaluating the clinical trial data from a safety perspective.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "monitoring", "definition": "The act of overseeing the progress of a clinical trial and of ensuring that it is conducted, recorded, and reported in accordance with the protocol, standard operating procedures (SOPs), Good Clinical Practice (GCP), and the applicable regulatory requirement(s). See also MRCT Center Clinical Research Glossary definition.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "monitoring report", "definition": "A written report to the sponsor that is produced by the monitor after each site visit and/or other trial-related communication, as specified by the sponsor’s SOPs.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "multi-center trial", "definition": "A clinical trial that is conducted according to a single protocol but at more than one site and therefore is carried out by more than one investigator. See also MRCT Center Clinical Research Glossary definition.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "NCS", "definition": "Non Clinically Significant. new drug application (NDA) The documentation submitted to the U.S. Food and Drug Administration. As described by the FDA: The goals of the NDA are to provide enough information to permit FDA reviewer to reach the following key decisions: Whether the drug is safe and effective in its proposed use(s), and whether the benefits of the drug outweigh the risks. Whether the drug’s proposed labeling (package insert) is appropriate, and what it should contain. Whether the methods used in manufacturing the drug and the controls used to maintain the drug’s quality are adequate to preserve the drug’s identity, strength, quality, and purity. The documentation required in an NDA is supposed to tell the drug's whole story, including what happened during the clinical tests, what the ingredients of the drug are, the results of the animal studies, how the drug behaves in the body, and how it is manufactured, processed and packaged.5 The NDA regulations are 21 CFR 314.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "non-clinical study", "definition": "Biomedical studies that are not performed on human subjects.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "open access", "definition": "See National Cancer Institute’s cancer Biomedical Informatics Grid (caBIG®) for additional details.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "open development", "definition": "See National Cancer Institute’s cancer Biomedical Informatics Grid (caBIG®) for additional details.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "open source", "definition": "See National Cancer Institute’s cancer Biomedical Informatics Grid (caBIG®) for additional details. opinion (in relation to an independent ethics committee) The judgment and/or the advice provided by an independent ethics committee (IEC). See also independent ethics committee. out-of-control process (ASQ) A process in which the statistical measure being evaluated is not in a state of statistical control (i.e., the variations among the observed sampling results can be attributed to a constant system of chance causes). See also in-control process. original medical record See source documents.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "Pareto Principle / 80-20 rule", "definition": "An observation that 20% of the input creates 80% of the result.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "phase I - IV", "definition": "Refer to the FDA glossary (clinicaltrials.gov). See also MRCT Center Clinical Research Glossary definition.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "predicate rule", "definition": "The overreaching regulations that the industry must follow for GxP (Good “Anything” Practice or any collection of quality guidelines). production environment The location (e.g., website, server, EDC) where real clinical data is entered and stored.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "protocol", "definition": "A document that describes the objective(s), design, methodology, statistical considerations, and organization of a trial. The protocol usually also gives the background and rationale for the trial, but these details could be provided in other protocol-referenced documents. Throughout the ICH GCP Guideline, the term “protocol” refers to protocol and protocol amendments. See also MRCT Center Clinical Research Glossary definition.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "protocol amendment", "definition": "A written description of a change (or changes) to, or formal clarification of, a protocol.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "protocol deviation", "definition": "Any alteration/modification to the IRB-approved protocol. The protocol includes the detailed protocol, protocol summary, consent form, recruitment materials, questionnaires, and any other information relating to the research study. (Partners Human Research Committee; http://healthcare.partners.org)", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "protocol violation", "definition": "Any protocol deviation that is not approved by the IRB prior to its initiation or implementation. (Partners Human Research Committee; http://healthcare.partners.org )", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "quality assurance (QA)", "definition": "All those planned and systematic actions that are established to ensure that the trial is performed and the data are generated, documented (recorded), and reported in compliance with Good Clinical Practice (GCP) and with the applicable regulatory requirement(s). quality control (QC) The operational techniques and activities undertaken within the quality assurance system to verify that the requirements for quality of the trial-related activities have been fulfilled. quality assurance/quality control (ASQ) Two terms with many interpretations because of the multiple definitions for the words “assurance” and “control.” For example, “assurance” can mean the act of giving confidence, the state of being certain, or the act of making certain. “Control” can mean an evaluation to indicate needed corrective responses, the act of guiding, or the state of a process in which the variability is attributable to a constant system of chance causes (for a detailed discussion on the multiple definitions, see ANSI/ISO/aSQC a35342, Statistics—Vocabulary and Symbols—Statistical Quality Control). One definition of quality assurance includes the following: all the planned and systematic activities implemented within the quality system that can be demonstrated to provide confidence that a product or service will fulfill requirements for quality. One definition for quality control includes the following: the operational techniques and activities used to fulfill requirements for quality. Often, however, “quality assurance” and “quality control” are used interchangeably to discuss the actions that ensure the quality of a product, service, or process.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "quality audit", "definition": "(ASQ) A systematic, independent examination and review to determine whether quality activities and related results comply with planned arrangements and whether these arrangements are implemented effectively and are suitable to achieve the objectives.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "random sampling", "definition": "(ASQ) A commonly used sampling technique in which sample units are selected in such a manner that all combinations of n units under consideration have an equal chance of being selected as the sample.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "randomization", "definition": "The process of assigning trial subjects to treatment or control groups using an element of chance to determine the assignments. Used to reduce bias. See also MRCT Center Clinical Research Glossary definition. regulatory authorities Bodies having the power to regulate. In the ICH GCP Guideline, the expression “regulatory authorities” includes the authorities that review submitted clinical data and the authorities that conduct inspections (see Section 1.291). These bodies are sometimes referred to as “competent authorities.”", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "research misconduct", "definition": "Falsification of data in proposing, designing, performing, recording, supervising, or reviewing research or in reporting research results. Falsification includes acts of omission and commission. Deliberate noncompliance with the regulations can be considered misconduct but is secondary to falsification of data. Research misconduct does not include honest error or differences of opinion.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "safety database", "definition": "A database typically used by Drug Safety or Pharmacovigilence departments to collect adverse event data.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "SAS transport file", "definition": "A machine-independent file that allows you to move a SAS data set from one operation system to another. ( http://kb.iu.edu/data/aevb.html) serious adverse event (SAE); serious adverse drug reaction (serious ADR) Any untoward medical occurrence that at any dose:  Results in death;  Is life-threatening;  Requires hospitalization or prolongs hospitalization of a subject;  Results in persistent or significant disability/incapacity; or  Is a congenital anomaly/birth defect. Service Level Agreement (SLA) - from the Vendor chapter An SLA is part of a service contract where the level of service is formally defined.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "source data", "definition": "All information that is necessary for the reconstruction and evaluation of the trial, including information about clinical findings, observations, or other activities in a clinical trial. Source data are contained in source documents such as original records or certified copies of original records.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "source documents", "definition": "Original documents, data, and records (e.g., hospital records, clinical and office charts, laboratory notes, memoranda, subjects’ diaries or evaluation checklists, pharmacy dispensing records, recorded data from automated instruments, copies or transcriptions certified after verification as being accurate copies, microfiches, photographic negatives, microfilm or magnetic media, x-rays, subject files, and records kept at the pharmacy, at the laboratories, and at medico-technical departments involved in the clinical trial).", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "special causes", "definition": "(ASQ) Causes of variation that arise because of special circumstances. These causes are not an inherent part of a process. Special causes are also referred to as assignable causes. See also common causes.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "specification", "definition": "(ASQ) A document that states the requirements to which a given product or service must conform.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "sponsor", "definition": "An individual, company, institution, or organization that takes responsibility for the initiation, management, and/or financing of a clinical trial. See also MRCT Center Clinical Research Glossary definition.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "sponsor-investigator", "definition": "An individual who both initiates and conducts, alone or with others, a clinical trial, and under whose immediate direction the investigational product is administered to, dispensed to, or used by a subject. The term does not include any person other than an individual (e.g., it does not include a corporation or an agency). A sponsor-investigator must fulfill the obligations of both a sponsor and an investigator. standard operating procedures (SOPs) Detailed instructions written to achieve uniformity of the performance of a specific function. statistical process control (SPC) (ASQ) The application of statistical techniques to control a process. Often the term “statistical quality control” is used interchangeably with “statistical process control.” statistical quality control (SQC) (ASQ) The application of statistical techniques to control quality. Often the term “statistical process control” is used interchangeably with “statistical quality control,” although statistical quality control includes acceptance sampling as well as statistical process control.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "sub-investigator", "definition": "Any individual member of the clinical trial team designated and supervised by the investigator at a trial site to perform critical trial-related procedures and/or to make important trial-related decisions (e.g., associates, residents, research fellows). See also investigator. subject/trial subject An individual who participates in a clinical trial, either as a recipient of the investigational product(s) or as a control. See also MRCT Center Clinical Research Glossary definition. subject identification code A unique identifier assigned by the investigator to each trial subject to protect the subject’s identity and to be used in lieu of the subject’s name when the investigator reports adverse events and/or other trial related data.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "trial site", "definition": "The location(s) where trial-related activities are actually conducted.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "trigger", "definition": "An event that precipitates other events.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "Type I error", "definition": "(ASQ) An incorrect decision to reject something that is acceptable, such as a statistical hypothesis or a lot of products.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "Type II error", "definition": "(ASQ) An incorrect decision to accept something that is unacceptable.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "UAT", "definition": "User Acceptance Testing. unexpected adverse drug reaction An adverse reaction, the nature or severity of which is not consistent with the applicable product information (e.g., investigator’s brochure for an unapproved investigational product or package insert/summary of product characteristics for an approved product). See the ICH Guideline for Clinical Safety Data Management: Definitions and Standards for Expedited Reporting.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "vulnerable subjects", "definition": "Individuals whose willingness to volunteer in a clinical trial may be unduly influenced by the expectation, whether justified or not, of benefits associated with participation, or of a retaliatory response from senior members of a hierarchy in case of refusal to participate. Examples are members of a group with a hierarchical structure, such as medical, pharmacy, dental, and nursing students, subordinate hospital and laboratory personnel, employees of the pharmaceutical industry, members of the armed forces, and persons kept in detention. Other vulnerable subjects include subjects with incurable diseases, persons in nursing homes, unemployed or impoverished persons, subjects in emergency situations, ethnic minority groups, homeless persons, nomads, refugees, minors, and those incapable of giving consent.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "well-being (of the trial subjects)", "definition": "The physical and mental integrity of the subjects participating in a clinical trial.", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "WHOdrug", "definition": "WHO Drug is a dictionary of medicinal product information. It is used to identify drug names and provides information about a drug's active ingredients and its therapeutic use(s).", "sources": [ "GCDMP_Glossary.pdf" ], "file": "GCDMP_Glossary.pdf", "type": "pdf" }, { "term": "INSPECTION", "definition": "CENTRAL DRUGS STANDARD CONTROL ORGANIZATION DIRECTORATE GENERAL OF HEALTH SERVICES MINISTRY OF HEALTH & FAMILY WELFARE GOVT. OF INDIA", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "November-2010", "definition": "CLINICAL TRIAL INSPECTION Effective Date: 01-11-2010", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Content", "definition": "Page No. 1. ABBREVIATIONS.............................................................................................................................................. 3 2. OBJECTIVES..................................................................................................................................................... 4 3. SCOPE AND EXTENT OF THE PROGRAMME............................................................................................... 4 4. PLANNING FOR INSPECTION......................................................................................................................... 4 4.1 SELECTION OF STUDIES ......................................................................................................................... 5 4.2 INSPECTION ASSIGNEMENTS ................................................................................................................ 5 4.3 PREPARING FOR INSPECTION................................................................................................................. 5 4.4 SCHEDULING THE INSPECTION............................................................................................................... 6 5. CONDUCTING THE INSPECTION:................................................................................................................... 6 5.1 CLINICAL TRIAL SITE............................................................................................................................... 6 5.1.1 OPENING INTERVIEW: .................................................................................................................. 6 5.1.2 ORGANISATION AND DELEGATION OF RESPONSIBILITIES..................................................... 7 5.1.3 STUDY PROTOCOL ....................................................................................................................... 8 5.1.4 SUBJECT RECORD & INFORMED CONSENT .............................................................................. 9 5.1.5 SOURCE DOCUMENTS AND CASE RECORD FORM (CRF) ........................................................ 9 5.1.6 ETHICS COMMITTEE (EC)/INDEPENDENT ETHICS COMMITTEE (IEC)..................................... 10 5.1.7 SPONSOR .................................................................................................................................... 11 5.1.8 TEST DRUG ACCOUNTABILITY ......................................................................................... 11 5.1.9 RECORD RETENTION .................................................................................................................. 12 5.1.10 CONCLUDING THE INSPECTION ......................................................................................... 12 5.2 INSPECTION OF CRO/SPONSOR............................................................................................................. 12 5.2.1 DOCUMENTS SUBMITTED TO CDSCO AND REGULATORY APPROVALS OBTAINED ........... 12 5.2.2. ORGANISATION AND PERSONNEL ............................................................................................. 13 5.2.3 SELECTION AND MONITORING OF INVESTIGATORS ................................................................ 14 5.2.4 QUALITY ASSURANCE (QA).......................................................................................................... 15 5.2.5 ADVERSE EVENTS (AE) REPORTING ......................................................................................... 15 5.2.6 DATA COLLECTION AND HANDLING ............................................................................................ 15 5.2.7 ELECTRONIC RECROD AND CLINIAL DATABASE...................................................................... 16 5.2.8 DATA COLLECTION ....................................................................................................................... 16 5.2.9 COMPUTEIZED SYSTEM SECURITY............................................................................................. 16 5.2.10 INVESTIGATIONAL PRODUCT (IP).............................................................................................. 17 6. REPORTING OF INSPECTION ....................................................................................................................... 17 CLINICAL TRIAL INSPECTION Effective Date: 01-11-2010", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "SLA", "definition": "State Licencing Authority CLINICAL TRIAL INSPECTION Effective Date: 01-11-2010", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Page 4 of 17", "definition": "CLINICAL TRIAL INSPECTION PROGRAMME 2 Objectives : The aims of the programme are: a. To verify GCP compliance to protect the rights, safety and well being of the subjects involved in clinical trial b. To verify the credibility and integrity of clinical trial data generated c. To verify the compliance with various regulatory provisions as per Drugs & Cosmetics Rules The purpose of this programme is to provide direction to inspectors/CDSCO officers for conducting inspection of site of clinical trial, sponsor / CRO’s facilities involved in clinical trial and information to investigators, sponsor/ CRO’S about procedures for inspection and follow up of action. 3 Scope and extent of the programme: Clinical trial inspection programme covers all clinical trial sites and sponsor / CRO’s facilities involved in clinical trial of drugs including biological and medical device covered under Drugs & Cosmetics Act. CLINICAL TRIAL INSPECTION Effective Date: 01-11-2010", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Page 5 of 17", "definition": "4. Planning for Inspection: Inspection can be conducted before, during or after a clinical trial is completed. 4.1 Selection of studies: Inspection can be carried out as a routine surveillance or for any specific cause(s).Study may be selected for inspection based on, but not restricted to the following criteria: 4.1.1", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Nature of study", "definition": "4.1.2 For regulatory decision based on clinical trial data 4.1.3", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Complaints", "definition": "4.1.5 Vulnerability of subjects 4.1.6 Number of CT including number of subject enrolled at a", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "particular site", "definition": "4.2 Inspection assignments: CDSCO HQ will issue instruction to the CDSCO Officers /Inspectors to conduct the inspection identifying the Clinical trial, name, address, contact number of clinical trial site, sponsor / CRO’s facilities to be inspected. It may also identify the type and purpose of the inspection and provide background materials like study protocol, CRF etc. 4.3 Preparing for inspection: The inspector shall go through the information provided by CDSCO HQ and develop a plan for conducting the inspection. CLINICAL TRIAL INSPECTION Effective Date: 01-11-2010", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Page 6 of 17", "definition": "4.4 Scheduling the inspection: Inspection of clinical trial site would generally be pre-announced to ensure availability of the Investigator / Sub- Investigator and other personnel along with study records at the time of the inspection. The date of inspection and other arrangements would be finalised by the CDSCO Officers / Inspector(s) in coordination with the investigator /sponsor/ CRO. Under some specific circumstances unannounced inspection of clinical trial sites can be carried out as per the direction of CDSCO HQ. Inspection of CRO/Sponsor can be conducted without prior notice. 5. Conducting the inspection: 5.1 Clinical Trial Sites: The inspection includes verification of essential documents to determine whether the trial related activities were in accordance with the protocol, GCP guidelines published by DGHS, Govt. of India and Schedule Y as well as other applicable regulatory requirements. When inspection is carried out after completion of the clinical trial, it will include comparison of data generated by the sponsor with source documents at the clinical trial sites and Case Record Form (CRF) in the investigator’s files. If it is a routine surveillance or “for cause” inspection of an ongoing clinical trial, the comparison will generally include source documents and CRF. 5.1.1 Opening interview: Inspector should meet investigator / key person of Sponsor and present his / her identity card. The inspector should provide verbal summary of methods and procedures to be followed during the inspection. CLINICAL TRIAL INSPECTION Effective Date: 01-11-2010", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Page 7 of 17", "definition": "During opening interview following main activities should be found out: 5.1.1.1 Investigator prior education and GCP experience, GCP training provided by the sponsor. 5.1.1.2 Who did what, when, where and how with respect to following:  Obtaining Informed consent of subjects,  Screening and admission of subjects to the study,  Receipt, handling, administration, return of investigational product,  Collection and analysing of data,  Recording, transcribing and reporting of data to sponsor,  Archiving the data 5.1.1.3 How did the investigator identify the subjects for the study, 5.1.1.4 Date of enrolment first and last subject 5.1.1.5 About Ethics Committee the site is using 5.1.1.6 Whether the investigator has copies of protocol, permission from CDSCO, undertaking by the investigator etc. 5.1.1.7 Information about unexpected and serious adverse events (if any) occurred at the site, 5.1.1.8 Information about monitoring/auditing of the site by sponsor/CRO. During the interview other relevant facts may also be found out. 5.1.2 ORGANIZATION & DELEGATION OF RESPONSIBILITIES: Inspector shall verify / obtain following: 5.1.2.1 Brief about study site. 5.1.2.2 Status of the study. 5.1.2.3 Whether investigator has agreement with sponsor for the study. 5.1.2.4 Whether financial & Confidentiality agreement with Investigator and concerned laboratory (ies) in place. 5.1.2.5 In Investigator undertaking protocol title, Investigator’s name, address, telephone no of site, qualification, Name & address of laboratories, Name of Sub-Investigator etc are in-compliance with Schedule Y of Drugs & Cosmetics Rules 1945. CLINICAL TRIAL INSPECTION Effective Date: 01-11-2010", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Page 8 of 17", "definition": "5.1.2.6 Obtain list of all clinical trials performed by investigator. The list should have information such as  Protocol Number  Protocol Title  Name of Sponsor/CRO  Study date 5.1.2.7 Determine whether authority for conducting various Clinical trial related activities were delegated properly by the Investigator to the competent personnel so that investigator was able to supervise the study adequately. Obtain a list of personnel with delegated activity. 5.1.2.8 Documents following;  Date of EC / IEC approval including initial review of protocol, amendment, ICD etc.  Date of screening of first subject,  Date of signing ICF by the first subject  Date of first administration of IP,  Date of last follow up of any subject, 5.1.2.9 List the name and address of facilities involved in laboratory test required by protocol. Verify accreditation status and adequacy of these facilities to perform the specified test, 5.1.2.10 Obtain a copy of site enrolment log, 5.1.2.11 Determine whether SOP’s for various activity are established and documented, 5.1.3 Study Protocol 5.1.3.1 Determine if, there are any difference between protocol provided to CDSCO and the protocol in the Investigator’s file with respect to following  Version number and effective date  Eligibility of Subject (Inclusion/ Exclusion Criteria) ", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Dosage", "definition": " Route of administration ", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Frequency of dosage", "definition": " Randomisation & Blinding process CLINICAL TRIAL INSPECTION Effective Date: 01-11-2010", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Page 9 of 17", "definition": " Verify whether Investigator follow the protocol as", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "approved", "definition": " Version number and EC approval of amendments 5.1.4 Subject record & Informed consent: 5.1.4.1 Review the Informed Consent Form (ICF) signed by the subjects. If the number of subjects at site is relatively small (e.g.20or less) 100% of the ICF can be reviewed. Determine the following: 5.1.4.2 whether ICF have all the elements enlisted in Appendix V of Schedule Y, 5.1.4.3 whether IC has been obtained from each subjects prior to participation of the subject in the study, 5.1.4.4 whether signature/thumb impression of the subjects have been affixed with date, 5.1.4.5 whether in case of illiterate subjects or illiterate representative of a subject, there are signature and details of an impartial witness, 5.1.4.6 Have witness/ signature been personally dated, 5.1.4.7 Have patient signature been personally dated? 5.1.4.8 Has the dated signature of the designated person for administering informed consent (IC) been affixed? 5.1.4.9 Is the designated person for administering IC medically qualified? 5.1.4.10 If IC has been administered by a designated person who is not medically qualified, is there evidence that subject's queries of a medical nature were answered by a medically qualified person or the investigator? 5.1.4.11 Is the completed ICF signed and dated by the investigator? 5.1.5 Source Documents and Case Record Form 5.1.5.1 Verify condition, completeness, legibility, accessibility of the investigators source data file. 5.1.5.2 Determine whether subjects who were enrolled and /or completed the study meet inclusion and exclusion criteria; 5.1.5.3 Determine whether subject received the test drug with respect to dose and frequency specified according to the protocol; CLINICAL TRIAL INSPECTION Effective Date: 01-11-2010", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Page 10 of 17", "definition": "5.1.5.4 Determine whether safety/ efficacy end point data was collected and reported in accordance with the protocol; 5.1.5.5 Does medical record mentions subject ID/ name /hospital registration number / and indication that subjects are participating in a clinical trial 5.1.5.6 Whether all adverse events were reported in CRF; 5.1.5.7 Compare the source document with CRF and determine whether source data have been correctly transcribed in CRF; 5.1.5.8 Verify whether all SAE’s have been reported to the sponsor (within 24 hours) and EC (within 7 working days); 5.1.5.9 Verify whether adequate medical care have been given to the subject especially in the event of inter current illness, adverse events including abnormal lab parameters; 5.1.6 Ethics Committee (EC) / Independent Ethics Committee (IEC): 5.1.6.1 Identify the name , address of the EC/ IEC in the approval letter and compare it with that stated in investigators undertaking ; 5.1.6.2 Verify if IEC approval letter mention study code , Protocol title and version number of the protocol, list of other documents reviewed, list of members present at the meeting, quorum of five members as specified in Schedule Y satisfied, date, time , venue of the meeting, signature and date of member secretary / Chairman; 5.1.6.3 In case the site does not have an IEC, verify whether following are in place:  Statement of the investigator / institution that approval granted by another IEC would be abided by & statement from the approving IEC that they would take responsibility for ongoing supervision of the site;  Has the investigator submitted reports of all SAEs to the IEC and apprised the EC/IEC about the trial progress? CLINICAL TRIAL INSPECTION Effective Date: 01-11-2010", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Page 11 of 17", "definition": "5.1.7 Sponsor: Verify/ determine: 5.1.7.1 Whether a clinical trial Investigators agreement has been signed for this study with the sponsor; 5.1.7.2 Whether investigator maintains copies of all reports submitted to the sponsor; 5.1.7.3 Whether all SAE are reported to sponsor within 24 hours; 5.1.7.4 Whether all CRFs were submitted to sponsor after completion of study; 5.1.7.5 Whether all dropouts and reasons thereof were reported to sponsor; 5.1.7.6 The method and frequency of monitoring the progress of the study by the sponsor; 5.1.7.7 Whether a log of onsite monitoring visit is maintained at the site; 5.1.8 Test Drug Accountability: 5.1.8.1 Review individual subject record to verify the correct dose administration with respect to dose, frequency, route of administration; 5.1.8.2 Determine whether unqualified /unauthorised persons administered/dispensed the test drug 5.1.8.3 Determine whether adequate record of qty. of test drug received , dispensed/ destroyed/returned is maintained ; 5.1.8.4 Determine whether storage condition/monitoring method are as per protocol/recommendation; 5.1.8.5 Whether trial medication are maintained under controlled access; 5.1.8.6 Have un-used trial medications been returned to the sponsor or disposed of according to protocol? In case of destruction at site, is there a certificate of destruction on file? 5.1.8.7 Are the drugs dispensing records being maintained properly? 5.1.8.8 Are the records for reconciliation of all IPs received from the sponsor maintained? CLINICAL TRIAL INSPECTION Effective Date: 01-11-2010", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Page 12 of 17", "definition": "5.1.9 Record retention: 5.1.9.1 Is adequate space available at the site for retention of", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "documents", "definition": "5.1.9.2 Determine whether documents are maintained properly and for the period as specified and necessary measures", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "premature", "definition": "destruction; 5.1.9.3 Determine who maintained custody of the documents and means for assuring prompt action; 5.1.10 Concluding the Inspection: The inspector should conclude the inspection with final discussion with the Investigator. During discussion the inspector should explain inspection finding .The inspector may also issue a list of observation at the conclusion of inspection. 5.2 Inspection of CRO/Sponsor The inspection includes verification of essential documents to compare practice and procedure followed by the CRO/Sponsor to that committed in the clinical trial application and GCP guidelines published by DGHS, Govt. of India and Schedule-Y as well as other applicable regulatory requirements. Inspection of CRO/Sponsor can be conducted without prior notice. During inspection following aspects may be verified. 5.2.1 Documents submitted to CDSCO and regulatory approvals obtained. 5.2.1.1 Clinical Trial application and DCGI approval letter 5.2.1.2 Import license application(Form 12) and import licence obtained (Form 11)Copy of license in Form 29 from (State Licencing Authority) SLA (in case of manufacture of test drugs) 5.2.1.3 Export NOC for biological samples 5.2.1.4 List of investigators 5.2.1.5 Investigator Undertaking (as per Appendix VII of Schedule Y) 5.2.1.6 Investigator's brochure 5.2.1.7 Protocol and Protocol amendments CLINICAL TRIAL INSPECTION Effective Date: 01-11-2010", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Page 13 of 17", "definition": "5.2.1.8 Patient Information Sheet and Informed Consent Form 5.2.1.9", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Case Record Form", "definition": "5.2.1.10 Ethics Committee approval and notifications to CDSCO 5.2.1.11 Unexpected and Serious Adverse Event Reports 5.2.1.12", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Study report", "definition": "5.2.2 Organisation and personnel: 5.2.2.1 Company profile and overall structure, 5.2.2.2 Organization chart for management of the clinical trial, Structure and responsibilities for all activities involving investigational products. Departments, functions, and key", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Protocol", "definition": "development, Investigator's brochure, Case Record Form, Informed consent form (ICF), translations and amendments ,Selection of investigators, Regulatory approval, Ethics Committee (EC) approval, Monitoring, Quality assurance Adverse Event (AE) Reporting, Data Management , Statistical Analysis, Electronic Records/Clinical Database, Clinical Supplies-Investigational Products (IP) Archival. 5.2.2.3 Identify and determine the personnel responsible for", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "following", "definition": " Authority to review and approve study documents  For final evaluations and decisions in the review of", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "study", "definition": " For obtaining & reviewing adverse events and", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "qualifications", "definition": " Job description of key stake holders  Verify clinical personnel training record  To obtain a list of external service providers and contractors and documentation of the service they provide.  Verify", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "various", "definition": "responsibilities and clinical trial related activities. CLINICAL TRIAL INSPECTION Effective Date: 01-11-2010", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Page 14 of 17", "definition": "5.2.3 Selection and monitoring of investigators 5.2.3.1 Obtain list of all investigators along with Investigator Undertaking, Signed Investigator Agreements 5.2.3.2 Criteria for selection of sites 5.2.3.3 Information provided to sites viz. Informed consent form, Protocol, Reports/publications of previous trials, Investigator's Brochure, Product labelling, Training, All versions and updates etc. 5.2.3.4 Investigator’s non-compliance (If any)  Deviations from CDSCO regulations  Deviations form protocol  How sponsor handles serious deviations from approved protocol or Schedule Y /Indian GCP Guidelines. 5.2.3.5 Steps for correction:  Verify whether any investigators terminated? Review monitoring reports reported to CDSCO,  Any Non-compliant investigator /terminated? Reasons? 5.2.3.6 Selection of monitor:  List all monitors for study duration  Selection criteria for monitors  Job descriptions/responsibilities  Qualifications  Training Records and CVs  Reporting structure  Monitoring SOP Frequency, scope and process, Obtain a copy of SOP and check compliance, If no SOPs, interview monitors to check how monitoring was done , Monitoring Plan, Monitoring Reports 5.2.3.7 Review the Pre trial and periodic trial visit report in respect of following content:  Process of verifying compliance to protocol  Process of verifying investigator responsibilities  Ethics Committee Approvals Amendments/Re- approval Communication-progress reports/SAEs etc Validity/Completeness  Informed Consents, Confirmation of consent and process of consent.  Use of IEC approved forms.  Adequacy of consent documentation,", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "completeness", "definition": "CLINICAL TRIAL INSPECTION Effective Date: 01-11-2010", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Page 15 of 17", "definition": " Which CRFs were compared to source docs? When and who verified CRFs against source data (hospital records, office charts, laboratory reports, etc.) at the study site. Form for data verification  Check copy of any SOPs and guidelines for data", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "verification", "definition": " Data correction handling, Compliance to Monitoring Plan, Frequency, Follow up etc. 5.2.4 Quality Assurance (QA): 5.2.4.1 Verify SOP for QA audits and operation of quality", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "assurance unit", "definition": "5.2.4.2 Describe how the audit and monitoring are separated 5.2.4.3 Obtain list of audited trial 5.2.5 Adverse events reporting: 5.2.5.1 Verify sponsor’s method for following up of adverse events and for dissemination of AE information to others Investigators: 5.2.5.2 Obtain list of SAE reported, Including death 5.2.5.3 Verify the timeline for reporting the SAE to CDSCO and other Investigators /EC; 5.2.6 Data collection and handling 5.2.6.1 Study tabulations: List of all studies for marketing", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Authorization", "definition": "application(compare to CRFs submitted) 5.2.6.3 If any subjects not included in the marketing Authorization application? Why not included? 5.2.6.4 Review of SOPS to verify compliance to assure the integrity of safety and efficacy data collected from clinical", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "investigators", "definition": "5.2.6.5 Verify that the SOPs were followed and document any", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "deviations", "definition": "5.2.6.6 Deviations/Data queries resolutions 5.2.6.7", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Statistical processes", "definition": "5.2.6.8 Primary endpoints Compare the tabulations with CRFs", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Record retention", "definition": "CLINICAL TRIAL INSPECTION Effective Date: 01-11-2010", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Page 16 of 17", "definition": "5.2.7 Electronic Record and Clinical database: 5.2.7.1 Person responsible for designing and developing data", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "base", "definition": "5.2.7.2 Can it be modified, or has it been modified? If so, by whom? 5.2.7.3 If the clinical investigator can modify it, how would the sponsor be aware of any changes? 5.2.7.4 Validation :Person responsible, Process, Documentation", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "of process", "definition": "5.2.7.5 Error logs maintained for errors in software and systems? 5.2.7.6 Do error logs identify corrections made? 5.2.8 Data collection: Following aspects may be verified: 5.2.8.1 Responsibilities : Authorization to access the system, to enter data and to change data 5.2.8.2 Use of electronic data capture or data transcription from paper CRFs into an electronic record 5.2.8.3 Audit trail : to record Changes to electronic records, Person Responsible for the change and Time of the", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "change", "definition": "5.2.8.4 Process of data transmission from the clinical investigator", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "to sponsor or CRO", "definition": "5.2.9 Computerized System Security: Following aspects may be verified: 5.2.9.1 Management of system access e.g. access privileges, authorization/de-authorization procedures,", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "access controls", "definition": "5.2.9.2 Records of authorized personnel , Names, Titles. Description of their access privileges 5.2.9.3", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "identification", "definition": "code/password combinations, tokens, biometric signature, electronic signatures, digital signatures 5.2.9.4 Data security in case of disasters, e.g., power failure 5.2.9.5 Contingency plans and backup files 5.2.9.6 Controls in place to prevent data from being altered, browsed, queried, or reported via external software applications that do not enter through the protective", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "system software", "definition": "CLINICAL TRIAL INSPECTION Effective Date: 01-11-2010", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "Page 17 of 17", "definition": "5.2.10 Investigational Product(IP): Following aspects may be verified: 5.2.10.1 Transferred data from central lab to sponsor 5.2.10.2 Integrity Procedures to ensure integrity of IP from manufacturing to receipt by the clinical investigator. 5.2.10.3 If IP met required release specifications by review of the Certificate of Analysis? 5.2.10.4 Storage of IP and the conditions of storage 5.2.10.5 Process of verification of IP integrity during shipment to investigator. 5.2.10.6", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "IP label", "definition": "5.2.10.7 If the test article was recalled, withdrawn, or returned? 5.2.10.8 Accountability: Following aspects may be verified:  Names and addresses of clinical investigators receiving IP Shipment, date (s), quantity, batch number.  Final disposition of the test article.  Detailed audit if serious violations are suspected.  Sufficient records to reconcile IP usage (compare the amount shipped to the investigators to the amount used and returned or disposed of).  Check whether all unused or reusable supplies of IP returned to the sponsor when either the investigator(S)", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "completed", "definition": "participation in the clinical investigation, or the investigation was terminated. If the test article was not returned to the sponsor, describe the method of disposition and determine if adequate records were maintained. 6. Reporting of inspection The Inspection should be documented in writing in both during and after inspection. After the inspection a narrative report containing details of inspection finding should be prepared and submitted to CDSCO (HQ). ****************", "sources": [ "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf" ], "file": "Guidelines_On_Clinical_Trial_Inspection-DCGI_11-2-2011.pdf", "type": "pdf" }, { "term": "INTERNATIONAL CONFERENCE ON HARMONISATION OF TECHNICAL", "definition": "REQUIREMENTS FOR REGISTRATION OF PHARMACEUTICALS FOR HUMAN USE ICH HARMONISED TRIPARTITE GUIDELINE THE EXTENT OF POPULATION EXPOSURE TO ASSESS CLINICAL SAFETY FOR DRUGS INTENDED FOR LONG-TERM TREATMENT OF NON-LIFE-THREATENING CONDITIONS", "sources": [ "ICH-E1.pdf" ], "file": "ICH-E1.pdf", "type": "pdf" }, { "term": "dated 27 October 1994", "definition": "This Guideline has been developed by the appropriate ICH Expert Working Group and has been subject to consultation by the regulatory parties, in accordance with the ICH Process. At Step 4 of the Process the final draft is recommended for adoption to the regulatory bodies of the European Union, Japan and USA.", "sources": [ "ICH-E1.pdf" ], "file": "ICH-E1.pdf", "type": "pdf" }, { "term": "E1", "definition": "THE EXTENT OF POPULATION EXPOSURE TO ASSESS CLINICAL", "sources": [ "ICH-E1.pdf" ], "file": "ICH-E1.pdf", "type": "pdf" }, { "term": "SAFETY", "definition": "FOR DRUGS INTENDED FOR LONG-TERM TREATMENT OF NON-LIFE-THREATENING CONDITIONS ICH Harmonised Tripartite Guideline Having reached Step 4 of the ICH Process at the ICH Steering Committee meeting on 27 October 1994, this guideline is recommended for adoption to the three regulatory parties to ICH The objective of this guideline is to present an accepted set of principles for the safety evaluation of drugs intended for the long-term treatment (chronic or repeated intermittent use for longer than 6 months) of non-life-threatening diseases. The safety evaluation during clinical drug development is expected to characterise and quantify the safety profile of a drug over a reasonable duration of time consistent with the intended long-term use of the drug. Thus, duration of drug exposure and its relationship to both time and magnitude of occurrence of adverse events are important considerations in determining the size of the data base necessary to achieve such goals. For the purpose of this guideline, it is useful to distinguish between clinical data on adverse drug events (ADEs) derived from studies of shorter duration of exposure and data from studies of longer duration, which frequently are non-concurrently controlled studies. It is expected that short-term event rates (cumulative 3-month incidence of about 1%) will be well characterised. Events where the rate of occurrence changes over a longer period of time may need to be characterised depending on their severity and importance to the risk-benefit assessment of the drug. The safety evaluation during clinical drug development is not expected to characterise rare adverse events, for example, those occurring in less than 1 in 1000 patients. The design of the clinical studies can significantly influence the ability to make causality judgements about the relationships between the drug and adverse events. A placebo-controlled trial allows the adverse event rate in the drug-treated group to be compared directly with the background event rate in the patient population being studied. Although a study with a positive or active control will allow a comparison of adverse event rates to be made between the test drug and the control drug, no direct assessment of the background event rate in the population studied can be made. A study that has no concurrent control group makes it more difficult to assess the causality relationship between adverse events observed and the test drug. There was general agreement on the following: 1. A harmonised regulatory standard is of value for the extent and duration of treatment needed to provide the safety data base for drugs intended for long-term treatment of non-life-threatening conditions. Although this standard covers many indications and drug classes, there are exceptions. 2. Regulatory standards for the safety evaluation of drugs should be based on previous experience with the occurrence and detection of adverse drug events (ADEs), statistical considerations of the probability of detecting specified frequencies of ADEs, and practical considerations. 1", "sources": [ "ICH-E1.pdf" ], "file": "ICH-E1.pdf", "type": "pdf" }, { "term": "Population Exposure", "definition": "b. Situations in which there is a need to quantitate the occurrence rate of an expected specific low-frequency ADE will require a greater long-term data base. Examples would include situations where a specific serious ADE has been identified in similar drugs or where a serious event that could represent an alert event is observed in early clinical trials. c. Larger safety data bases may be needed to make risk/benefit decisions in situations where the benefit from the drug is either (1) small (e.g., symptomatic improvement in less serious medical conditions) or (2) will be experienced by only a fraction of the treated patients (e.g., certain preventive therapies administered to healthy populations) or (3) is of uncertain magnitude (e.g., efficacy determination on a surrogate endpoint). d. In situations where there is concern that a drug may add to an already significant background rate of morbidity or mortality, clinical trials may need to be designed with a sufficient number of patients to provide adequate statistical power to detect prespecified increases over the baseline morbidity or mortality. e. In some cases, a smaller number of patients may be acceptable, for example, where the intended treatment population is small. 8. Filing for approval will usually be possible based on the data from patients treated through 6 months. Data on patients treated through 12 months must be submitted as soon as available and prior to approval in the United States and Japan but may be submitted after approval in the E.C.. In the U.S. the initial submission for those drugs designated as priority drugs must include the 12- months patient data. 3", "sources": [ "ICH-E1.pdf" ], "file": "ICH-E1.pdf", "type": "pdf" }, { "term": "INTERNATIONAL CONFERENCE ON HARMONISATION OF TECHNICAL", "definition": "REQUIREMENTS FOR REGISTRATION OF PHARMACEUTICALS FOR HUMAN", "sources": [ "ICH_E2A.pdf" ], "file": "ICH_E2A.pdf", "type": "pdf" }, { "term": "USE", "definition": "ICH HARMONISED TRIPARTITE GUIDELINE CLINICAL SAFETY DATA MANAGEMENT: DEFINITIONS AND STANDARDS FOR", "sources": [ "ICH_E2A.pdf" ], "file": "ICH_E2A.pdf", "type": "pdf" }, { "term": "dated 27 October 1994", "definition": "This Guideline has been developed by the appropriate ICH Expert Working Group and has been subject to consultation by the regulatory parties, in accordance with the ICH Process. At Step 4 of the Process the final draft is recommended for adoption to the regulatory bodies of the European Union, Japan and USA.", "sources": [ "ICH_E2A.pdf" ], "file": "ICH_E2A.pdf", "type": "pdf" }, { "term": "E2A", "definition": "CLINICAL SAFETY DATA MANAGEMENT: DEFINITIONS AND STANDARDS FOR EXPEDITED REPORTING ICH Harmonised Tripartite Guideline Having reached Step 4 of the ICH Process at the ICH Steering Committee meeting on 27 October 1994, this guideline is recommended for adoption to the three regulatory parties to ICH I.", "sources": [ "ICH_E2A.pdf" ], "file": "ICH_E2A.pdf", "type": "pdf" }, { "term": "INTRODUCTION", "definition": "It is important to harmonise the way to gather and, if necessary, to take action on important clinical safety information arising during clinical development. Thus, agreed definitions and terminology, as well as procedures, will ensure uniform Good Clinical Practice standards in this area. The initiatives already undertaken for marketed medicines through the CIOMS-1 and CIOMS-2 Working Groups on expedited (alert) reports and periodic safety update reporting, respectively, are important precedents and models. However, there are special circumstances involving medicinal products under development, especially in the early stages and before any marketing experience is available. Conversely, it must be recognised that a medicinal product will be under various stages of development and/or marketing in different countries, and safety data from marketing experience will ordinarily be of interest to regulators in countries where the medicinal product is still under investigational-only (Phase 1, 2, or 3) status. For this reason, it is both practical and well-advised to regard pre-marketing and", "sources": [ "ICH_E2A.pdf" ], "file": "ICH_E2A.pdf", "type": "pdf" }, { "term": "as", "definition": "interdependent, while recognising that responsibility for clinical safety within regulatory bodies and companies may reside with different departments, depending on the status of the product (investigational vs. marketed). There are two issues within the broad subject of clinical safety data management that are appropriate for harmonisation at this time: (1) the development of standard definitions and terminology for key aspects of clinical safety reporting, and (2) the appropriate mechanism for handling expedited (rapid) reporting, in the investigational (i.e., pre-approval) phase. The provisions of this guideline should be used in conjunction with other ICH Good Clinical Practice guidelines. II. DEFINITIONS AND TERMINOLOGY ASSOCIATED WITH CLINICAL", "sources": [ "ICH_E2A.pdf" ], "file": "ICH_E2A.pdf", "type": "pdf" }, { "term": "SAFETY EXPERIENCE", "definition": "A. Basic Terms Definitions for the terms adverse event (or experience), adverse reaction, and unexpected adverse reaction have previously been agreed to by consensus of the more than 30 Collaborating Centres of the WHO International Drug Monitoring Centre (Uppsala, Sweden). [Edwards, I.R., et al, Harmonisation in Pharmacovigilance. Drug Safety 10(2): 93-102, 1994.] Although those definitions can pertain to situations involving clinical investigations, some minor modifications are necessary, especially to accommodate the pre-approval, development environment. 1 Clinical Safety Data Management The following definitions, with input from the WHO Collaborative Centre, have been agreed: 1. Adverse Event (or Adverse Experience) Any untoward medical occurrence in a patient or clinical investigation subject administered a pharmaceutical product and which does not necessarily have to have a causal relationship with this treatment. An adverse event (AE) can therefore be any unfavourable and unintended sign (including an abnormal laboratory finding, for example), symptom, or disease temporally associated with the use of a medicinal product, whether or not considered related to the medicinal product. 2. Adverse Drug Reaction (ADR) In the pre-approval clinical experience with a new medicinal product or its new usages, particularly as the therapeutic dose(s) may not be established: all noxious and unintended responses to a medicinal product related to any dose should be considered adverse drug reactions. The phrase \"responses to a medicinal products\" means that a causal relationship between a medicinal product and an adverse event is at least a reasonable possibility, i.e., the relationship cannot be ruled out. Regarding marketed medicinal products, a well-accepted definition of an adverse drug reaction in the post-marketing setting is found in WHO Technical Report 498 [1972] and reads as follows: A response to a drug which is noxious and unintended and which occurs at doses normally used in man for prophylaxis, diagnosis, or therapy of disease or for modification of physiological function. The old term \"side effect\" has been used in various ways in the past, usually to describe negative (unfavourable) effects, but also positive (favourable) effects. It is recommended that this term no longer be used and particularly should not be regarded as synonymous with adverse event or adverse reaction. 3. Unexpected Adverse Drug Reaction An adverse reaction, the nature or severity of which is not consistent with the applicable product information (e.g., Investigator's Brochure for an unapproved investigational medicinal product). (See section III.C.) B. Serious Adverse Event or Adverse Drug Reaction During clinical investigations, adverse events may occur which, if suspected to be medicinal product-related (adverse drug reactions), might be significant enough to lead to important changes in the way the medicinal product is developed (e.g., change in dose, population, needed monitoring, consent forms). This is particularly true for reactions which, in their most severe forms, threaten life or function. Such reactions should be reported promptly to regulators. 2 Clinical Safety Data Management Therefore, special medical or administrative criteria are needed to define reactions that, either due to their nature (\"serious\") or due to the significant, unexpected information they provide, justify expedited reporting. To ensure no confusion or misunderstanding of the difference between the terms \"serious\" and \"severe,\" which are not synonymous, the following note of clarification is provided: The term \"severe\" is often used to describe the intensity (severity) of a specific event (as in mild, moderate, or severe myocardial infarction); the event itself, however, may be of relatively minor medical significance (such as severe headache). This is not the same as \"serious,\" which is based on patient/event outcome or action criteria usually associated with events that pose a threat to a patient's life or functioning. Seriousness (not severity) serves as a guide for defining regulatory reporting obligations. After reviewing the various regulatory and other definitions in use or under discussion elsewhere, the following definition is believed to encompass the spirit and meaning of them all: A serious adverse event (experience) or reaction is any untoward medical occurrence that at any dose: * results in death, * is life-threatening, NOTE: The term \"life-threatening\" in the definition of \"serious\" refers to an event in which the patient was at risk of death at the time of the event; it does not refer to an event which hypothetically might have caused death if it were more severe. * requires inpatient hospitalisation or prolongation of existing hospitalisation, * results in persistent or significant disability/incapacity, or * is a congenital anomaly/birth defect. Medical and scientific judgement should be exercised in deciding whether expedited reporting is appropriate in other situations, such as important medical events that may not be immediately life-threatening or result in death or hospitalisation but may jeopardise the patient or may require intervention to prevent one of the other outcomes listed in the definition above. These should also usually be considered serious. Examples of such events are intensive treatment in an emergency room or at home for allergic bronchospasm; blood dyscrasias or convulsions that do not result in hospitalisation; or development of drug dependency or drug abuse. C. Expectedness of an Adverse Drug Reaction The purpose of expedited reporting is to make regulators, investigators, and other appropriate people aware of new, important information on serious reactions. Therefore, such reporting will generally involve events previously unobserved or undocumented, and a guideline is needed on how to define an event as \"unexpected\" or \"expected\" (expected/unexpected from the perspective of 3 Clinical Safety Data Management previously observed, not on the basis of what might be anticipated from the pharmacological properties of a medicinal product). As stated in the definition (II.A.3.), an \"unexpected\" adverse reaction is one, the nature or severity of which is not consistent with information in the relevant source document(s). Until source documents are amended, expedited reporting is required for additional occurrences of the reaction. The following documents or circumstances will be used to determine whether an adverse event/reaction is expected: 1. For a medicinal product not yet approved for marketing in a country, a company's Investigator's Brochure will serve as the source document in that country. (See section III.F. and ICH Guideline for the Investigator's Brochure.) 2. Reports which add significant information on specificity or severity of a known, already documented serious ADR constitute unexpected events. For example, an event more specific or more severe than described in the Investigator's Brochure would be considered \"unexpected\". Specific examples would be (a) acute renal failure as a labeled ADR with a subsequent new report of interstitial nephritis and (b) hepatitis with a first report of fulminant hepatitis. III. STANDARDS FOR EXPEDITED REPORTING A. What Should be Reported? 1. Single Cases of Serious, Unexpected ADRs All adverse drug reactions (ADRs) that are both serious and unexpected are subject to expedited reporting. This applies to reports from spontaneous sources and from any type of clinical or epidemiological investigation, independent of design or purpose. It also applies to cases not reported directly to a sponsor or manufacturer (for example, those found in regulatory authority-generated ADR registries or in publications). The source of a report (investigation, spontaneous, other) should always be specified. Expedited reporting of reactions which are serious but expected will ordinarily be inappropriate. Expedited reporting is also inappropriate for serious events from clinical investigations that are considered not related to study product, whether the event is expected or not. Similarly, non-serious adverse reactions, whether expected or not, will ordinarily not be subject to expedited reporting. Information obtained by a sponsor or manufacturer on serious, unexpected reports from any source should be submitted on an expedited basis to appropriate regulatory authorities if the minimum criteria for expedited reporting can be met. See section III.B. Causality assessment is required for clinical investigation cases. All cases judged by either the reporting health care professional or the sponsor as having a reasonable suspected causal relationship to the medicinal product qualify as ADRs. For purposes of reporting, adverse event reports associated with marketed drugs (spontaneous reports) usually imply causality. 4 Clinical Safety Data Management Many terms and scales are in use to describe the degree of causality (attributability) between a medicinal product and an event, such as certainly, definitely, probably, possibly or likely related or not related. Phrases such as \"plausible relationship,\" \"suspected causality,\" or \"causal relationship cannot be ruled out\" are also invoked to describe cause and effect. However, there is currently no standard international nomenclature. The expression \"reasonable causal relationship\" is meant to convey in general that there are facts (evidence) or arguments to suggest a causal relationship. 2.", "sources": [ "ICH_E2A.pdf" ], "file": "ICH_E2A.pdf", "type": "pdf" }, { "term": "Other Observations", "definition": "There are situations in addition to single case reports of \"serious\" adverse events or reactions that may necessitate rapid communication to regulatory authorities; appropriate medical and scientific judgement should be applied for each situation. In general, information that might materially influence the benefit- risk assessment of a medicinal product or that would be sufficient to consider changes in medicinal product administration or in the overall conduct of a clinical investigation represents such situations. Examples include: a. For an \"expected,\" serious ADR, an increase in the rate of occurrence which is judged to be clinically important. b. A significant hazard to the patient population, such as lack of efficacy with a medicinal product used in treating life-threatening disease. c. A major safety finding from a newly completed animal study (such as carcinogenicity). B. Reporting Time Frames 1. Fatal or Life-Threatening Unexpected ADRs Certain ADRs may be sufficiently alarming so as to require very rapid notification to regulators in countries where the medicinal product or indication, formulation, or population for the medicinal product are still not approved for marketing, because such reports may lead to consideration of suspension of, or other limitations to, a clinical investigations program. Fatal or life-threatening, unexpected ADRs occurring in clinical investigations qualify for very rapid reporting. Regulatory agencies should be notified (e.g., by telephone, facsimile transmission, or in writing) as soon as possible but no later than 7 calendar days after first knowledge by the sponsor that a case qualifies, followed by as complete a report as possible within 8 additional calendar days. This report must include an assessment of the importance and implication of the findings, including relevant previous experience with the same or similar medicinal products. 2. All Other Serious, Unexpected ADRs Serious, unexpected reactions (ADRs) that are not fatal or life-threatening must be filed as soon as possible but no later than 15 calendar days after first knowledge by the sponsor that the case meets the minimum criteria for expedited reporting. 5 Clinical Safety Data Management 3. Minimum criteria for reporting Information for final description and evaluation of a case report may not be available within the required time frames for reporting outlined above. Nevertheless, for regulatory purposes, initial reports should be submitted within the prescribed time as long as the following minimum criteria are met: an identifiable patient; a suspect medicinal product; an identifiable reporting source; and an event or outcome that can be identified as serious and unexpected, and for which, in clinical investigation cases, there is a reasonable suspected causal relationship. Follow-up information should be actively sought and submitted as it becomes available. C. How to Report The CIOMS-I form has been a widely accepted standard for expedited adverse event reporting. However, no matter what the form or format used, it is important that certain basic information/data elements, when available, be included with any expedited report, whether in a tabular or narrative presentation. The listing in Attachment 1 addresses those data elements regarded as desirable; if all are not available at the time of expedited reporting, efforts should be made to obtain them. (See section III.B.) All reports must be sent to those regulators or other official parties requiring them (as appropriate for the local situation) in countries where the drug is under development. D. Managing Blinded Therapy Cases When the sponsor and investigator are blinded to individual patient treatment (as in a double-blind study), the occurrence of a serious event requires a decision on whether to open (break) the code for the specific patient. If the investigator breaks the blind, then it is assumed the sponsor will also know the assigned treatment for that patient. Although it is advantageous to retain the blind for all patients prior to final study analysis, when a serious adverse reaction is judged reportable on an expedited basis, it is recommended that the blind be broken only for that specific patient by the sponsor even if the investigator has not broken the blind. It is also recommended that, when possible and appropriate, the blind be maintained for those persons, such as biometrics personnel, responsible for analysis and interpretation of results at the study's conclusion. There are several disadvantages to maintaining the blind under the circumstances described which outweigh the advantages. By retaining the blind, placebo and comparator (usually a marketed product) cases are filed unnecessarily. When the blind is eventually opened, which may be many weeks or months after reporting to regulators, it must be ensured that company and regulatory data bases are revised. If the event is serious, new, and possibly related to the medicinal product, then if the Investigator's Brochure is updated, notifying relevant parties of the new information in a blinded fashion is inappropriate and possibly misleading. Moreover, breaking the blind for a single patient usually has little or no significant implications for the conduct of the clinical investigation or on the analysis of the final clinical investigation data. However, when a fatal or other \"serious\" outcome is the primary efficacy endpoint in a clinical investigation, the integrity of the clinical investigation may be compromised if the blind is broken. Under these and similar circumstances, it 6 Clinical Safety Data Management may be appropriate to reach agreement with regulatory authorities in advance concerning serious events that would be treated as disease-related and not subject to routine expedited reporting. E. Miscellaneous Issues 1. Reactions Associated with Active Comparator or Placebo Treatment It is the sponsor's responsibility to decide whether active comparator drug reactions should be reported to the other manufacturer and/or directly to appropriate regulatory agencies. Sponsors must report such events to either the manufacturer of the active control or to appropriate regulatory agencies. Events associated with placebo will usually not satisfy the criteria for an ADR and, therefore, for expedited reporting. 2. Products with More than one Presentation or Use To avoid ambiguities and uncertainties, an ADR that qualifies for expedited reporting with one presentation of a product (e.g., a dosage form, formulation, delivery system) or product use (e.g., for an indication or population), should be reported or referenced to regulatory filings across other product presentations and uses. It is not uncommon that more than one dosage form, formulation, or delivery system (oral, IM, IV, topical, etc.) of the pharmacologically active compound(s) is under study or marketed; for these different presentations there may be some marked differences in the clinical safety profile. The same may apply for a given product used in different indications or populations (single dose vs. chronic administration, for example). Thus, \"expectedness\" may be product or product- use specific, and separate Investigator's Brochures may be used accordingly. However, such documents are expected to cover ADR information that applies to all affected product presentations and uses. When relevant, separate discussions of pertinent product-specific or use-specific safety information will also be included. It is recommended that any adverse drug reactions that qualify for expedited reporting observed with one product dosage form or use be cross referenced to regulatory records for all other dosage forms and uses for that product. This may result in a certain amount of overreporting or unnecessary reporting in obvious situations (for example, a report of phlebitis on IV injection sent to authorities in a country where only an oral dosage form is studied or marketed). However, underreporting is completely avoided. 3.", "sources": [ "ICH_E2A.pdf" ], "file": "ICH_E2A.pdf", "type": "pdf" }, { "term": "Post-study Events", "definition": "Although such information is not routinely sought or collected by the sponsor, serious adverse events that occurred after the patient had completed a clinical study (including any protocol-required post-treatment follow-up) will possibly be reported by an investigator to the sponsor. Such cases should be regarded for expedited reporting purposes as though they were study reports. Therefore, a causality assessment and determination of expectedness are needed for a decision on whether or not expedited reporting is required. 7 Clinical Safety Data Management F. INFORMING INVESTIGATORS AND ETHICS COMMITTEES/ INSTITUTIONAL REVIEW BOARDS OF NEW SAFETY INFORMATION International standards regarding such communication are discussed within the ICH GCP Guidelines, including the addendum on \"Guideline for the Investigator's Brochure.\" In general, the sponsor of a study should amend the Investigator's Brochure as needed, and in accord with any local regulatory requirements, so as to keep the description of safety information updated. 8 Clinical Safety Data Management", "sources": [ "ICH_E2A.pdf" ], "file": "ICH_E2A.pdf", "type": "pdf" }, { "term": "Attachment 1", "definition": "KEY DATA ELEMENTS FOR INCLUSION IN EXPEDITED REPORTS OF SERIOUS ADVERSE DRUG REACTIONS The following list of items has its foundation in several established precedents, including those of CIOMS-I, the WHO International Drug Monitoring Centre, and various regulatory authority forms and guidelines. Some items may not be relevant depending on the circumstances. The minimum information required for expedited reporting purposes is: an identifiable patient, the name of a suspect medicinal product, an identifiable reporting source, and an event or outcome that can be identified as serious and unexpected and for which, in clinical investigation cases, there is a reasonable suspected causal relationship. Attempts should be made to obtain follow-up information on as many other listed items pertinent to the case. 1. Patient Details", "sources": [ "ICH_E2A.pdf" ], "file": "ICH_E2A.pdf", "type": "pdf" }, { "term": "Initials", "definition": "Other relevant identifier (clinical investigation number, for example)", "sources": [ "ICH_E2A.pdf" ], "file": "ICH_E2A.pdf", "type": "pdf" }, { "term": "Gender", "definition": "Age and/or date of birth", "sources": [ "ICH_E2A.pdf" ], "file": "ICH_E2A.pdf", "type": "pdf" }, { "term": "Height", "definition": "2. Suspected Medicinal Product(s) Brand name as reported International Non-Proprietary Name (INN)", "sources": [ "ICH_E2A.pdf" ], "file": "ICH_E2A.pdf", "type": "pdf" }, { "term": "Batch number", "definition": "Indication(s) for which suspect medicinal product was prescribed or tested Dosage form and strength Daily dose and regimen (specify units - e.g., mg, ml, mg/kg) Route of administration Starting date and time of day Stopping date and time, or duration of treatment 3. Other Treatment(s) For concomitant medicinal products (including non-prescription/OTC medicinal products) and non-medicinal product therapies, provide the same information as for the suspected product. 9 Clinical Safety Data Management 4. Details of Suspected Adverse Drug Reaction(s) Full description of reaction(s) including body site and severity, as well as the criterion (or criteria) for regarding the report as serious should be given. In addition to a description of the reported signs and symptoms, whenever possible, attempts should be made to establish a specific diagnosis for the reaction. Start date (and time) of onset of reaction Stop date (and time) or duration of reaction Dechallenge and rechallenge information Setting (e.g., hospital, out-patient clinic, home, nursing home) Outcome: information on recovery and any sequelae; what specific tests and/or treatment may have been required and their results; for a fatal outcome, cause of death and a comment on its possible relationship to the suspected reaction should be provided. Any autopsy or other post-mortem findings (including a coroner's report) should also be provided when available. Other information: anything relevant to facilitate assessment of the case, such as medical history including allergy, drug or alcohol abuse; family history; findings from special investigations. 5. Details on Reporter of Event (Suspected ADR)", "sources": [ "ICH_E2A.pdf" ], "file": "ICH_E2A.pdf", "type": "pdf" }, { "term": "Telephone number", "definition": "Profession (speciality) 6. Administrative and Sponsor/Company Details Source of report: was it spontaneous, from a clinical investigation (provide details), from the literature (provide copy), other? Date event report was first received by sponsor/manufacturer Country in which event occurred Type of report filed to authorities: initial or follow-up (first, second, etc.) Name and address of sponsor/manufacturer/company Name, address, telephone number, and FAX number of contact person in reporting company or institution Identifying regulatory code or number for marketing authorisation dossier or clinical investigation process for the suspected product (for example IND or CTX number, NDA number) Sponsor/manufacturer's identification number for the case (this number must be the same for the initial and follow-up reports on the same case). 10", "sources": [ "ICH_E2A.pdf" ], "file": "ICH_E2A.pdf", "type": "pdf" }, { "term": "INTERNATIONAL CONFERENCE ON HARMONISATION OF TECHNICAL", "definition": "REQUIREMENTS FOR REGISTRATION OF PHARMACEUTICALS FOR HUMAN", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "USE", "definition": "ICH HARMONISED TRIPARTITE GUIDELINE STRUCTURE AND CONTENT OF CLINICAL STUDY REPORTS", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "E3", "definition": "STRUCTURE AND CONTENT OF CLINICAL STUDY REPORTS ICH Harmonised Tripartite Guideline Having reached Step 4 of the ICH Process at the ICH Steering Committee meeting on 30 November 1995, this guideline is recommended for adoption to the three regulatory parties to ICH", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "INTRODUCTION TO THE GUIDELINE.....................................................................1", "definition": "1. TITLE PAGE...........................................................................................................3 2. SYNOPSIS...............................................................................................................3 3. TABLE OF CONTENTS FOR THE INDIVIDUAL CLINICAL STUDY REPORT..................................................................................................................4 4. LIST OF ABBREVIATIONS AND DEFINITION OF TERMS .......................4 5. ETHICS....................................................................................................................4 5.1 INDEPENDENT ETHICS COMMITTEE (IEC) OR INSTITUTIONAL REVIEW BOARD (IRB)............................................................................................................4 5.2 ETHICAL CONDUCT OF THE STUDY.................................................................4 5.3 PATIENT INFORMATION AND CONSENT.........................................................4 6. INVESTIGATORS AND STUDY ADMINISTRATIVE STRUCTURE...........4 7. INTRODUCTION...................................................................................................5 8. STUDY OBJECTIVES...........................................................................................5 9. INVESTIGATIONAL PLAN .................................................................................5 9.1 OVERALL STUDY DESIGN AND PLAN - DESCRIPTION .................................5 9.2 DISCUSSION OF STUDY DESIGN, INCLUDING THE CHOICE OF CONTROL GROUPS................................................................................................6 9.3 SELECTION OF STUDY POPULATION...............................................................7 9.3.1 Inclusion Criteria........................................................................................7 9.3.2 Exclusion Criteria.......................................................................................7 9.3.3 Removal of Patients from Therapy or Assessment...................................7 9.4 TREATMENTS.........................................................................................................7 9.4.1 Treatments Administered ..........................................................................7 9.4.2 Identity of Investigational Product(s)........................................................7 9.4.3 Method of Assigning Patients to Treatment Groups ................................8", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "STRUCTURE AND CONTENT OF CLINICAL STUDY REPORTS", "definition": "INTRODUCTION TO THE GUIDELINE The objective of this guideline is to allow the compilation of a single core clinical study report acceptable to all regulatory authorities of the ICH regions. The regulatory authority specific additions will consist of modules to be considered as appendices, available upon request according to regional regulatory requirements. The clinical study report described in this guideline is an \"integrated\" full report of an individual study of any therapeutic, prophylactic or diagnostic agent (referred to herein as drug or treatment) conducted in patients, in which the clinical and statistical description, presentations, and analyses are integrated into a single report, incorporating tables and figures into the main text of the report, or at the end of the text, and with appendices containing the protocol, sample case report forms, investigator related information, information related to the test drugs/investigational products including active control/comparators, technical statistical documentation, related publications, patient data listings, and technical statistical details such as derivations, computations, analyses, and computer output etc. The integrated full report of a study should not be derived by simply joining a separate clinical and statistical report. Although this guideline is mainly aimed at efficacy and safety trials, the basic principles and structure described can be applied to other kinds of trials, such as clinical pharmacology studies. Depending on the nature and importance of such studies, a less detailed report might be appropriate. The guideline is intended to assist sponsors in the development of a report that is complete, free from ambiguity, well organised and easy to review. The report should provide a clear explanation of how the critical design features of the study were chosen and enough information on the plan, methods and conduct of the study so that there is no ambiguity in how the study was carried out. The report with its appendices should also provide enough individual patient data, including the demographic and baseline data, and details of analytical methods, to allow replication of the critical analyses when authorities wish to do so. It is also particularly important that all analyses, tables, and figures carry, in text or as part of the table, clear identification of the set of patients from which they were generated. Depending on the regulatory authority's review policy, abbreviated reports using summarised data or with some sections deleted, may be acceptable for uncontrolled studies or other studies not designed to establish efficacy (but a controlled safety study should be reported in full), for seriously flawed or aborted studies, or for controlled studies that examine conditions clearly unrelated to those for which a claim is made. However, a full description of safety aspects should be included in these cases. If an abbreviated report is submitted, there should be enough detail of design and results to allow the regulatory authority to determine whether a full report is needed. If there is any question regarding whether the reports are needed, it may be useful to consult the regulatory authority. In presenting the detailed description of how the study was carried out, it may be possible simply to restate the description in the initial protocol. Often, however, it is possible to present the methodology of the study more concisely in a separate document. In each section describing the design and conduct of the study, it is particularly important to clarify features of the study that are not well-described in 1 Structure and Content of Clinical Study Reports the protocol and identify ways in which the study as conducted differed from the protocol, and to discuss the statistical methods and analyses used to account for these deviations from the planned protocol. The full integrated report of the individual study should include the most detailed discussion of individual adverse events or laboratory abnormalities, but these should usually be reexamined as part of an overall safety analysis of all available data in any application.", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "ii", "definition": "Structure and Content of Clinical Study Reports 11.4.2.5 Multiple Comparison/Multiplicity..........................................17 11.4.2.6 Use of an \"Efficacy Subset\" of Patients..................................17 11.4.2.7 Active-Control Studies Intended to Show Equivalence ........17 11.4.2.8 Examination of Subgroups .....................................................18 11.4.3 Tabulation of Individual Response Data.................................................18 11.4.4 Drug Dose, Drug Concentration, and Relationships to Response..........19 11.4.5 Drug-Drug and Drug-Disease Interactions.............................................19 11.4.6 By-Patient Displays..................................................................................19 11.4.7 Efficacy Conclusions.................................................................................19 12. SAFETY EVALUATION......................................................................................19 12.1 EXTENT OF EXPOSURE......................................................................................20 12.2 ADVERSE EVENTS (AES)....................................................................................21 12.2.1 Brief Summary of Adverse Events...........................................................21 12.2.2 Display of Adverse Events .......................................................................21 12.2.3 Analysis of Adverse Events......................................................................22 12.2.4 Listing of Adverse Events by Patient ......................................................23 12.3 DEATHS, OTHER SERIOUS ADVERSE EVENTS, AND OTHER SIGNIFICANT ADVERSE EVENTS ....................................................................23 12.3.1 Listing of Deaths, other Serious Adverse Events and Other Significant Adverse Events .........................................................................................23 12.3.1.1 Deaths......................................................................................23 12.3.1.2 Other Serious Adverse Events ...............................................24 12.3.1.3 Other Significant Adverse Events..........................................24 12.3.2 Narratives of Deaths, Other Serious Adverse Events and Certain Other Significant Adverse Events..............................................24 12.3.3 Analysis and Discussion of Deaths, Other Serious Adverse Events and Other Significant Adverse Events....................................................24 12.4 CLINICAL LABORATORY EVALUATION .........................................................25 12.4.1 Listing of Individual Laboratory Measurements by Patient (16.2.8) and Each Abnormal Laboratory Value (14.3.4) ......................................25 12.4.2 Evaluation of Each Laboratory Parameter .............................................25 12.4.2.1 Laboratory Values Over Time................................................26 12.4.2.2 Individual Patient Changes....................................................26", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "iii", "definition": "Structure and Content of Clinical Study Reports 12.4.2.3 Individual Clinically Significant Abnormalities ................... 26 12.5 VITAL SIGNS, PHYSICAL FINDINGS AND OTHER OBSERVATIONS RELATED TO SAFETY......................................................................................... 27 12.6 SAFETY CONCLUSIONS..................................................................................... 27 13. DISCUSSION AND OVERALL CONCLUSIONS...........................................27 14. TABLES, FIGURES AND GRAPHS REFERRED TO BUT NOT INCLUDED IN THE TEXT ................................................................................27 14.1 DEMOGRAPHIC DATA ........................................................................................ 27 14.2 EFFICACY DATA.................................................................................................. 28 14.3 SAFETY DATA ...................................................................................................... 28 14.3.1 Displays of Adverse Events ..................................................................... 28 14.3.2 Listings of Deaths, Other Serious and Significant Adverse Events...... 28 14.3.3 Narratives of Deaths, Other Serious and Certain Other Significant Adverse Events......................................................................................... 28 14.3.4 Abnormal Laboratory Value Listing (Each Patient) .............................. 28 15. REFERENCE LIST .............................................................................................28 16. APPENDICES ......................................................................................................28 16.1 STUDY INFORMATION....................................................................................... 28 16.1.1 Protocol and protocol amendments ......................................................... 28 16.1.2 Sample case report form (unique pages only)......................................... 28 16.1.3 List of IECs or IRBs (plus the name of the committee Chair if required by the regulatory authority) - Representative written information for patient and sample consent forms........................................................... 28 16.1.4 List and description of investigators and other important participants in the study, including brief (1 page) CVs or equivalent summaries of training and experience relevant to the performance of the clinical study.......................................................................................................... 28 16.1.5 Signatures of principal or coordinating investigator(s) or sponsor’s responsible medical officer, depending on the regulatory authority's requirement .............................................................................................. 28 16.1.6 Listing of patients receiving test drug(s)/investigational product(s) from specific batches, where more than one batch was used ......................... 28 16.1.7 Randomisation scheme and codes (patient identification and treatment assigned) ................................................................................................... 29 16.1.8 Audit certificates (if available) (see Annex IVa and IVb of the guideline) ............................................... 29 16.1.9 Documentation of statistical methods..................................................... 29", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "iv", "definition": "Structure and Content of Clinical Study Reports 16.1.10 Documentation of inter-laboratory standardisation methods and quality assurance procedures if used ...................................................................29 16.1.11 Publications based on the study ...............................................................29 16.1.12 Important publications referenced in the report .....................................29 16.2. PATIENT DATA LISTINGS..................................................................................29 16.2.1 Discontinued patients...............................................................................29 16.2.2 Protocol deviations....................................................................................29 16.2.3 Patients excluded from the efficacy analysis ..........................................29 16.2.4 Demographic data.....................................................................................29 16.2.5 Compliance and/or drug concentration data (if available) .....................29 16.2.6 Individual efficacy response data ............................................................29 16.2.7 Adverse event listings (each patient) ......................................................29 16.2.8. Listing of individual laboratory measurements by patient, when required by regulatory authorities ..........................................................29 16.3 CASE REPORT FORMS ........................................................................................29 16.3.1 CRFs for deaths, other serious adverse events and withdrawals for AE...................................................................................29 16.3.2 Other CRFs submitted .............................................................................29 16.4. INDIVIDUAL PATIENT DATA LISTINGS (US ARCHIVAL LISTINGS) .........29", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "ANNEX II", "definition": "PRINCIPAL OR COORDINATING INVESTIGATOR(S) SIGNATURE(S) OR SPONSOR’S RESPONSIBLE MEDICAL OFFICER _______________ STUDY TITLE: ................................................................................. STUDY AUTHOR(S): ................................................................................. I have read this report and confirm that to the best of my knowledge it accurately describes the conduct and results of the study INVESTIGATOR: _______________________SIGNATURE(S) ____________________ OR SPONSOR’S RESPONSIBLE", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "ANNEX V", "definition": "STUDY # (Data Set Identification) LISTING OF PATIENTS WHO DISCONTINUED THERAPY Centre:", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "ANNEX VI", "definition": "STUDY # (Data Set Identification) LISTING OF PATIENTS AND OBSERVATIONS EXCLUDED FROM EFFICACY ANALYSIS Centre:", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "predictive", "definition": "characteristics of the study population and, where the study is large enough to permit this, present data for demographic (e.g., age, sex, race, weight) and other (e.g., renal or hepatic function) subgroups so that possible differences in efficacy or safety can be identified. Usually, however, subgroup responses should be examined in the larger database used in the overall analysis. The data listings requested as part of the report (usually in an appendix) are those needed to support critical analyses. Data listings that are part of the report should be readily usable by the reviewer. Thus, although it may be desirable to include many variables in a single listing to limit size, this should not be at the expense of clarity. An excess of data should not be allowed to lead to overuse of symbols instead of words or easily understood abbreviations or to too small displays etc. In this case, it is preferable to produce several listings. Data should be presented in the report at different levels of detail: overall summary figures and tables for important demographic, efficacy and safety variables may be placed in the text to illustrate important points; other summary figures, tables and listings for demographic, efficacy and safety variables should be provided in section 14; individual patient data for specified groups of patients should be provided as listings in Appendix 16.2; and all individual patient data (archival listings requested only in the US) should be provided in Appendix 16.4. In any table, figure or data listing, estimated or derived values, if used, should be identified in a conspicuous fashion. Detailed explanations should be provided as to how such values were estimated or derived and what underlying assumptions were made. The guidance provided below is detailed and is intended to notify the applicant of virtually all of the information that should routinely be provided so that post- submission requests for further data clarification and analyses can be reduced as much as possible. Nonetheless, specific requirements for data presentation and/ or analysis may depend on specific situations, may evolve over time, may vary from drug class to drug class, may differ among regions and cannot be described in general terms; it is therefore important to refer to specific clinical guidelines and to discuss data presentation and analyses with the reviewing authority, whenever possible. Detailed written guidance on statistical approaches is available from some authorities. Each report should consider all of the topics described (unless clearly not relevant) although the specific sequence and grouping of topics may be changed if alternatives are more logical for a particular study. Some data in the appendices are specific requirements of individual regulatory authorities and should be submitted as appropriate. The numbering should then be adapted accordingly. 2 Structure and Content of Clinical Study Reports In the case of very large trials, some of the provisions of this guideline may be impractical or inappropriate. When planning and when reporting such trials, contact with regulatory authorities to discuss an appropriate report format is encouraged. The provisions of this guideline should be used in conjunction with other ICH guidelines. STRUCTURE AND CONTENT OF CLINICAL STUDY REPORTS 1.", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "TITLE PAGE", "definition": "The title page should contain the following information: − study title − name of test drug/ investigational product − indication studied − if not apparent from the title, a brief (1 to 2 sentences) description giving design (parallel, cross-over, blinding, randomised) comparison (placebo, active, dose/response), duration, dose, and patient population − name of the sponsor − protocol identification (code or number) − development phase of study − study initiation date (first patient enrolled, or any other verifiable definition) − date of early study termination, if any − study completion date (last patient completed) − name and affiliation of principal or coordinating investigator(s) or sponsor’s responsible medical officer − name of company/sponsor signatory (the person responsible for the study report within the company/sponsor. The name, telephone number and fax number of the company/sponsor contact persons for questions arising during review of the study report should be indicated on this page or in the letter of application.) − statement indicating whether the study was performed in compliance with Good Clinical Practices (GCP), including the archiving of essential documents − date of the report (identify any earlier reports from the same study by title and date). 2.", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "SYNOPSIS", "definition": "A brief synopsis (usually limited to 3 pages) that summarises the study should be provided (see Annex I of the guideline for an example of a synopsis format used in Europe). The synopsis should include numerical data to illustrate results, not just text or p-values. 3 Structure and Content of Clinical Study Reports 3. TABLE OF CONTENTS FOR THE INDIVIDUAL CLINICAL STUDY", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "REPORT", "definition": "The table of contents should include: − the page number or other locating information of each section, including summary tables, figures and graphs; − a list and the locations of appendices, tabulations and any case report forms provided. 4. LIST OF ABBREVIATIONS AND DEFINITION OF TERMS A list of the abbreviations, and lists and definitions of specialised or unusual terms or measurements units used in the report should be provided. Abbreviated terms should be spelled out and the abbreviation indicated in parentheses at first appearance in the text. 5.", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "ETHICS", "definition": "5.1 INDEPENDENT ETHICS COMMITTEE (IEC) OR INSTITUTIONAL REVIEW BOARD (IRB) It should be confirmed that the study and any amendments were reviewed by an Independent Ethics Committee or Institutional Review Board. A list of all IECs or IRBs consulted should be given in appendix 16.1.3 and, if required by the regulatory authority, the name of the committee Chair should be provided. 5.2 ETHICAL CONDUCT OF THE STUDY It should be confirmed that the study was conducted in accordance with the ethical principles that have their origins in the Declaration of Helsinki. 5.3 PATIENT INFORMATION AND CONSENT How and when informed consent was obtained in relation to patient enrolment, (e.g., at allocation, pre-screening) should be described. Representative written information for the patient (if any) and a sample patient consent form should be provided in appendix 16.1.3. 6. INVESTIGATORS AND STUDY ADMINISTRATIVE STRUCTURE The administrative structure of the study (e.g., principal investigator, coordinating investigator, steering committee, administration, monitoring and evaluation committees, institutions, statistician, central laboratory facilities, contract research organisation (C.R.O.), clinical trial supply management) should be described briefly in the body of the report. There should be provided in appendix 16.1.4 a list of the investigators with their affiliations, their role in the study and their qualifications (curriculum vitae or equivalent). A similar list for other persons whose participation materially affected the conduct of the study should also be provided in appendix 16.1.4. In the case of large trials with many investigators the above requirements may be abbreviated to consist of general statements of qualifications for persons carrying out particular roles in the study with only the name, degree and institutional affiliation and roles of each investigator or other participant. The listing should include: 4 Structure and Content of Clinical Study Reports a) Investigators b) Any other person carrying out observations of primary or other major efficacy variables, such as a nurse, physician's assistant, clinical psychologist, clinical pharmacist, or house staff physician. It is not necessary to include in this list a person with only an occasional role, e.g., an on-call physician who dealt with a possible adverse effect or a temporary substitute for any of the above c) The author(s) of the report, including the responsible biostatistician(s). Where signatures of the principal or coordinating investigators are required by regulatory authorities, these should be included in appendix 16.1.5 (see Annex II for a sample form). Where these are not required, the signature of the sponsor’s responsible medical officer should be provided in appendix 16.1.5. 7.", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "INTRODUCTION", "definition": "The introduction should contain a brief statement (maximum: 1 page) placing the study in the context of the development of the test drug/investigational product, relating the critical features of the study (e.g., rationale and aims, target population, treatment, duration, primary endpoints) to that development. Any guidelines that were followed in the development of the protocol or any other agreements/meetings between the sponsor/company and regulatory authorities that are relevant to the particular study, should be identified or described. 8.", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "STUDY OBJECTIVES", "definition": "A statement describing the overall purpose(s) of the study should be provided. 9.", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "INVESTIGATIONAL PLAN", "definition": "9.1 OVERALL STUDY DESIGN AND PLAN - DESCRIPTION The overall study plan and design (configuration) of the study (e.g., parallel, cross- over) should be described briefly but clearly, using charts and diagrams as needed. If other studies used a very similar protocol, it may be useful to note this and describe any important differences. The actual protocol and any changes should be included as appendix 16.1.1 and a sample case report form (unique pages only; i.e., it is not necessary to include identical pages from forms for different evaluations or visits) as appendix 16.1.2. If any of the information in this section comes from sources other than the protocol, these should be identified. The information provided should include: − treatments studied (specific drugs, doses and procedures); − patient population studied and the number of patients to be included; − level and method of blinding/masking (e.g., open, double-blind, single-blind, blinded evaluators and unblinded patients and/or investigators); − kind of control(s) (e.g., placebo, no treatment, active drug, dose-response, historical) and study configuration (parallel, cross-over); − method of assignment to treatment (randomisation, stratification); − sequence and duration of all study periods, including pre-randomisation and post-treatment periods, therapy withdrawal periods and single- and double- blind treatment periods. When patients are randomised should be specified. 5 Structure and Content of Clinical Study Reports It is usually helpful to display the design graphically with a flow chart which includes timing of assessments (see Annexes IIIa and IIIb for an example); − any safety, data monitoring or special steering or evaluation committees; − any interim analyses. 9.2 DISCUSSION OF STUDY DESIGN, INCLUDING THE CHOICE OF", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "CONTROL GROUPS", "definition": "The specific control chosen and the study design used should be discussed, as necessary. Examples of design issues meriting discussion follow. Generally, the control (comparison) groups that are recognised are placebo concurrent control, no treatment concurrent control, active treatment concurrent control, dose comparison concurrent control, and historical control. In addition to the type of control, other critical design features that may need discussion are use of a cross-over design and selection of patients with particular prior history, such as response or non- response to a specific drug or member of a drug class. If randomisation was not used, it is important to explain how other techniques, if any, guarded against systematic selection bias. Known or potential problems associated with the study design or control group chosen, should be discussed in light of the specific disease and therapies being studied. For a crossover design, for example, there should be consideration, among other things, of the likelihood of spontaneous change in the disease and of carry-over effects of treatment during the study. If efficacy was to be demonstrated by showing equivalence, i.e., the absence of a specified degree of inferiority of the new treatment compared to an established treatment, problems associated with such study designs should be addressed. Specifically there should be provided a basis for considering the study capable of distinguishing active from inactive therapy. Support may be provided by an analysis of previous studies similar to the present study with respect to important design characteristics (patient selection, study endpoints, duration, dose of active control, concomitant therapy etc.) showing a consistent ability to demonstrate superiority of the active control to placebo. How to assess the ability of the present study to distinguish effective from ineffective therapy should also be discussed. For example, it may be possible to identify a treatment response (based on past studies) that would clearly distinguish between the treated population and an untreated group. Such a response could be the change of a measure from baseline or some other specified outcome like healing rate or survival rate. Attainment of such a response would support the expectation that the study could have distinguished the active drug from an inactive drug. There should also be a discussion of the degree of inferiority of the therapy (often referred to as the delta value) the study was intended to show was not exceeded. The limitations of historical controls are well known (difficulty of assuring comparability of treated groups, inability to blind investigators to treatment, change in therapy/disease, difference due to placebo effect etc.) and deserve particular attention. Other specific features of the design may also deserve discussion, including presence or absence of washout periods and the duration of the treatment period, especially for a chronic illness. The rationale for dose and dose-interval selection should be explained, if it is not obvious. For example, once daily dosing with a short half-life 6 Structure and Content of Clinical Study Reports drug whose effect is closely related in time to blood level is not usually effective; if the study design uses such dosing, this should be explained, e.g., by pointing to pharmacodynamic evidence that effect is prolonged compared to blood levels. The procedures used to seek evidence of \"escape\" from drug effect at the end of the dose- interval, such as measurements of effect just prior to dosing, should be described. Similarly, in a parallel design dose-response study, the choice of doses should be explained. 9.3 SELECTION OF STUDY POPULATION 9.3.1 Inclusion Criteria The patient population and the selection criteria used to enter the patients into the study should be described, and the suitability of the population for the purposes of the study discussed. Specific diagnostic criteria used, as well as specific disease requirements (e.g., disease of a particular severity or duration, results of a particular test or rating scale(s) or physical examination, particular features of clinical history, such as failure or success on prior therapy, or other potential prognostic factors and any age, sex or ethnic factors) should be presented. Screening criteria and any additional criteria for randomisation or entry into the test drug/investigational product treatment part of the trial should be described. If there is reason to believe that there were additional entry criteria, not defined in the protocol, the implications of these should be discussed. For example, some investigators may have excluded, or entered into other studies, patients who were particularly ill or who had particular baseline characteristics. 9.3.2 Exclusion Criteria The criteria for exclusion at entry into the study should be specified and the rationale (e.g., safety concerns, administrative reasons or lack of suitability for the trial) provided. The impact of exclusions on the generalisability of the study should be discussed in section 13 of the study report, or in an overview of safety and efficacy. 9.3.3 Removal of Patients from Therapy or Assessment The predetermined reasons for removing patients from therapy or assessment observation, if any, should be described, as should the nature and duration of any planned follow-up observations in those patients. 9.4", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "TREATMENTS", "definition": "9.4.1 Treatments Administered The precise treatments or diagnostic agents to be administered in each arm of the study, and for each period of the study, should be described including route and mode of administration, dose and dosage schedule. 9.4.2 Identity of Investigational Product(s) In the text of the report, a brief description of the test drug(s)/investigational product(s) (formulation, strength, batch number(s)) should be given. If more than one batch of test drug/investigational product was used, patients receiving each batch should be identified in appendix 16.1.6. The source of placebos and active control/comparator product(s) should be provided. Any modification of comparator product(s) from their usual commercial state should 7 Structure and Content of Clinical Study Reports be noted, and the steps taken to assure that their bioavailability was unaltered should be described. For long-duration trials of investigational products with limited shelf-lives or incomplete stability data, the logistics of resupply of the materials should be described. Any use of test materials past their expiry date should be noted, and patients receiving them identified. If there were specific storage requirements, these should also be described. 9.4.3 Method of Assigning Patients to Treatment Groups The specific methods used to assign patients to treatment groups, e.g., centralised allocation, allocation within sites, adaptive allocation (that is, assignment on the basis of earlier assignment or outcome) should be described in the text of the report, including any stratification or blocking procedures. Any unusual features should be explained. A detailed description of the randomisation method, including how it was executed, should be given in appendix 16.1.7 with references cited if necessary. A table exhibiting the randomisation codes, patient identifier, and treatment assigned should also be presented in the appendix. For a multicentre study, the information should be given by centre. The method of generating random numbers should be explained. For a historically controlled trial, it is important to explain how the particular control was selected and what other historical experiences were examined, if any, and how their results compared to the control used. 9.4.4 Selection of Doses in the Study The doses or dose ranges used in the study should be given for all treatments and the basis for choosing them described (e.g., prior experience in humans, animal data). 9.4.5 Selection and Timing of Dose for each Patient Procedures for selecting each patient's dose of test drug/investigational product and active control/comparator should be described. These procedures can vary from simple random assignment to a selected fixed drug/dose regimen, to some specified titration procedure, to more elaborate response-determined selection procedures, e.g., where dose is titrated upward at intervals until intolerance or some specified endpoint is achieved. Procedures for back-titration, if any, should also be described. The timing (time of day, interval) of dosing and the relation of dosing to meals should be described, and if it was not specified, this should be noted. Any specific instructions to patients about when or how to take the dose(s) should be described. 9.4.6 Blinding A description of the specific procedures used to carry out blinding should be provided (e.g., how bottles were labelled, labels that reveal blind-breakage, sealed code list/envelopes, double dummy techniques), including the circumstances in which the blind would be broken for an individual or for all patients, e.g., for serious adverse events, the procedures used and who had access to patient codes. If the study allowed for some investigators to remain unblinded (e.g., to allow them to adjust medication), the means of shielding other investigators should be explained. Measures taken to ensure that test drug/investigational product and placebo were indistinguishable and 8 Structure and Content of Clinical Study Reports evidence that they were indistinguishable, should be described, as should the appearance, shape, smell, and taste of the test material. Measures to prevent unblinding by laboratory measurements, if used, should be described. If there was a data monitoring committee with access to unblinded data, procedures to ensure maintenance of overall study blinding should be described. The procedure to maintain the blinding when interim analyses are performed should also be explained. If blinding was considered unnecessary to reduce bias for some or all of the observations, this should be explained; e.g., use of a random-zero sphygmomanometer eliminates possible observer bias in reading blood pressure and Holter tapes are often read by automated systems that are presumably immune to observer bias. If blinding was considered desirable but not feasible, the reasons and implications should be discussed. Sometimes blinding is attempted but is known to be imperfect because of obvious drug effects in at least some patients (dry mouth, bradycardia, fever, injection site reactions, changes in laboratory data). Such problems or potential problems should be identified and if there were any attempts to assess the magnitude of the problem or manage it (e.g., by having some endpoint measurements carried out by people shielded from information that might reveal treatment assignment), they should be described. 9.4.7 Prior and Concomitant Therapy Which drugs or procedures were allowed before and during the study, whether and how their use was recorded, and any other specific rules and procedures related to permitted or forbidden concomitant therapy should be described. How allowed concomitant therapy might affect the outcome due either to drug-drug interaction or to direct effects on the study endpoints should be discussed, and how the independent effects of concomitant and study therapies could be ascertained should be explained. 9.4.8 Treatment Compliance The measures taken to ensure and document treatment compliance should be described, e.g., drug accountability, diary cards, blood, urine or other body fluid drug level measurements, or medication event monitoring. 9.5 EFFICACY AND SAFETY VARIABLES 9.5.1 Efficacy and Safety Measurements Assessed and Flow Chart The specific efficacy and safety variables to be assessed and laboratory tests to be conducted, their schedule (days of study, time of day, relation to meals, and the timing of critical measures in relation to test drug administration, e.g., just prior to next dose, two hours after dose), the methods for measuring them, and the persons responsible for the measurements should be described. If there were changes in personnel carrying out critical measurements, these should be reported. It is usually helpful to display graphically in a flow chart (see Annex III of the guideline) the frequency and timing of efficacy and safety measurements; visit numbers and times should be shown, or, alternatively, times alone can be used (visit numbers alone are more difficult to interpret). Any specific instructions (e.g., guidance or use of a diary) to the patients should also be noted. Any definitions used to characterise outcome (e.g., criteria for determining occurrence of acute myocardial infarction, designation of the location of the infarction, characterisation of a stroke as thrombotic or haemorrhagic, distinction between TIA 9 Structure and Content of Clinical Study Reports and stroke, assignment of cause of death) should be explained in full. Any techniques used to standardise or compare results of laboratory tests or other clinical measurements (e.g., ECG, chest X-ray) should also be described. This is particularly important in multicentre studies. If anyone other than the investigator was responsible for evaluation of clinical outcomes (e.g., the sponsor or an external committee to review X-rays or ECG's or to determine whether the patient had a stroke, acute infarction, or sudden death) the person or group should be identified. The procedures, including means of maintaining blindness, and centralising readings and measurements, should be described fully. The means of obtaining adverse event data should be described (volunteered, checklist, or, questioning), as should any specific rating scale(s) used and any specifically planned follow-up procedures for adverse events or any planned rechallenge procedure. Any rating of adverse events by the investigator, sponsor or external group, (e.g., rating by severity or, likelihood of drug causation) should be described. The criteria for such ratings, if any, should be given and the parties responsible for the ratings should be clearly identified. If efficacy or safety was to be assessed in terms of categorical ratings, numerical scores etc., the criteria used for point assignment (e.g., definitions of point scores) should be provided. For multicentre studies, indicate how methods were standardised. 9.5.2 Appropriateness of Measurements If any of the efficacy or safety assessments was not standard, i.e., widely used and generally recognised as reliable, accurate, and relevant (able to discriminate between effective and ineffective agents), its reliability, accuracy and relevance should be documented. It may be helpful to describe alternatives considered but rejected. If a surrogate end point (a laboratory measurement or physical measurement or sign that is not a direct measure of clinical benefit) was used as a study end point, this should be justified e.g., by reference to clinical data, publications, guidelines or previous actions by regulatory authorities. 9.5.3 Primary Efficacy Variable(s) The primary measurements and endpoints used to determine efficacy should be clearly specified. Although the critical efficacy measurements may seem obvious, when there are multiple variables, or when variables are measured repeatedly, the protocol should identify the primary ones, with an explanation of why they were chosen, or designate the pattern of significant findings or other method of combining information that would be interpreted as supporting efficacy. If the protocol did not identify the primary variables, the study report should explain how these critical variables were selected (e.g., by reference to publications, guidelines or previous actions by regulatory authorities) and when they were identified (i.e., before or after the study was completed and unblinded). If an efficacy threshold was defined in the protocol, this should be described. 9.5.4 Drug Concentration Measurements Any drug concentrations to be measured, and the sample collection times and periods in relation to the timing of drug administration, should be described. Any relation of drug administration and sampling to ingestion of food, posture and the possible 10 Structure and Content of Clinical Study Reports effects of concomitant medication/alcohol/caffeine/nicotine should also be addressed. The biological sample measured the handling of samples and the method of measurement used should be described, referring to published and/or internal assay validation documentation for methodological details. Where other factors are believed important in assessing pharmacokinetics (e.g., soluble circulating receptors, renal or hepatic function), the timing and plans to measure these factors should also be specified. 9.6 DATA QUALITY ASSURANCE The quality assurance and quality control systems implemented to assure the quality of the data should be described in brief. If none were used, this should be stated. Documentation of inter-laboratory standardisation methods and quality assurance procedures, if used, should be provided under appendix 16.1.10. Any steps taken at the investigation site or centrally to ensure the use of standard terminology and the collection of accurate, consistent, complete, and reliable data, such as training sessions, monitoring of investigators by sponsor personnel, instruction manuals, data verification, cross-checking, use of a central laboratory for certain tests, centralised ECG reading, or data audits, should be described. It should be noted whether investigator meetings or other steps were taken to prepare investigators and standardise performance. If the sponsor used an independent internal or external auditing procedure, it should be mentioned here and described in appendix 16.1.8; and audit certificates, if available, should be provided in the same appendix. 9.7 STATISTICAL METHODS PLANNED IN THE PROTOCOL AND DETERMINATION OF SAMPLE SIZE 9.7.1 Statistical and Analytical Plans The statistical analyses planned in the protocol and any changes made before outcome results were available should be described. In this section emphasis should be on which analyses, comparisons and statistical tests were planned, not on which ones were actually used. If critical measurements were made more than once, the particular measurements (e.g., average of several measurements over the entire study, values at particular times, values only from study completers, or last on- therapy value) planned as the basis for comparison of test drug/investigational product and control should be specified. Similarly, if more than one analytical approach is plausible, e.g., changes from baseline response, slope analysis, life table analysis, the planned approach should be identified. Also, whether the primary analysis is to include adjustment for covariates should be specified. If there were any planned reasons for excluding from analysis patients for whom data are available, these should be described. If there were any subgroups whose results were to be examined separately, these should be identified. If categorical responses (global scales, severity scores, responses of a certain size) were to be used in analysing responses, they should be clearly defined. Planned monitoring of the results of the study should be described. If there was a data monitoring committee, either within or outside the sponsor's control, its composition and operating procedures should be described and procedures to maintain study blinding should be given. The frequency and nature of any planned interim analysis, any specified circumstances in which the study would be terminated and any 11 Structure and Content of Clinical Study Reports statistical adjustments to be employed because of interim analyses should be described. 9.7.2 Determination of Sample Size The planned sample size and the basis for it, such as statistical considerations or practical limitations, should be provided. Methods for sample size calculation should be given together with their derivations or source of reference. Estimates used in the calculations should be given and explanations provided as to how they were obtained. For a study intended to show a difference between treatments, the difference the study is designed to detect should be specified. For a positive control study intended to show that a new therapy is at least as effective as the standard therapy, the sample size determination should specify the difference between treatments that would be considered unacceptably large and therefore the difference the study is designed to be able to exclude. 9.8 CHANGES IN THE CONDUCT OF THE STUDY OR PLANNED", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "ANALYSES", "definition": "Any change in the conduct of the study or planned analyses (e.g., dropping a treatment group, changing the entry criteria or drug dosages, adjusting the sample size etc.) instituted after the start of the study should be described. The time(s) and reason(s) for the change(s), the procedure used to decide on the change(s), the person(s) or group(s) responsible for the change(s) and the nature and content of the data available (and to whom they were available) when the change was made should also be described, whether the change was documented as a formal protocol amendment or not (Personnel changes need not be included). Any possible implications of the change(s) for the interpretation of the study should be discussed briefly in this section and more fully in other appropriate sections of the report. In every section of the report, a clear distinction between conditions (procedures) planned in the protocol and amendments or additions should be made. In general, changes in planned analyses made prior to breaking the blind have limited implications for study interpretation. It is therefore particularly critical that the timing of changes relative to blind breaking and availability of outcome results be well characterised. 10.", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "STUDY PATIENTS", "definition": "10.1 DISPOSITION OF PATIENTS There should be a clear accounting of all patients who entered the study, using figures or tables in the text of the report. The numbers of patients who were randomised, and who entered and completed each phase of the study, (or each week/month of the study) should be provided, as well as the reasons for all post-randomisation discontinuations, grouped by treatment and by major reason (lost to follow-up, adverse event, poor compliance etc.). It may also be relevant to provide the number of patients screened for inclusion and a breakdown of the reasons for excluding patients during screening, if this could help clarify the appropriate patient population for eventual drug use. A flow chart is often helpful (see Annexes IVa and IVb of the guideline for example). Whether patients are followed for the duration of the study, even if drug is discontinued, should be made clear. In appendix 16.2.1, there should also be a listing of all patients discontinued from the study after enrolment, broken down by centre and treatment group, giving a patient identifier, the specific reason for discontinuation, the treatment (drug and dose), 12 Structure and Content of Clinical Study Reports cumulative dose, (where appropriate), and the duration of treatment before discontinuation. Whether or not the blind for the patient was broken at the time of discontinuation should be noted. It may also be useful to include other information, such as critical demographic data (e.g., age, sex, race), concomitant medication, and the major response variable(s) at termination. See Annex V for an example of such a listing. 10.2", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "PROTOCOL DEVIATIONS", "definition": "All important deviations related to study inclusion or exclusion criteria, conduct of the trial, patient management or patient assessment should be described. In the body of the text, protocol deviations should be appropriately summarised by centre and grouped into different categories, such as: − those who entered the study even though they did not satisfy the entry criteria; − those who developed withdrawal criteria during the study but were not withdrawn; − those who received the wrong treatment or incorrect dose; − those who received an excluded concomitant treatment. In appendix 16.2.2, individual patients with these protocol deviations should be listed, broken down by centre for multicentre studies. 11.", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "DATA SETS ANALYSED", "definition": "Exactly which patients were included in each efficacy analysis should be precisely defined, e.g., all patients receiving any test drugs/investigational products, all patients with any efficacy observation or with a certain minimum number of observations, only patients completing the trial, all patients with an observation during a particular time window, only patients with a specified degree of compliance etc. It should be clear, if not defined in the study protocol, when, (relative to study unblinding), and how inclusion/exclusion criteria for the data sets analysed were developed. Generally, even if the applicant's proposed primary analysis is based on a reduced subset of the patients with data, there should also be for any trial intended to establish efficacy an additional analysis using all randomised (or otherwise entered) patients with any on-treatment data. There should be a tabular listing of all patients, visits and observations excluded from the efficacy analysis provided in appendix 16.2.3 (see Annex VI of the guideline for an example). The reasons for exclusions should also be analysed for the whole treatment group over time (see Annex VII of the guideline for an example). 11.2 DEMOGRAPHIC AND OTHER BASELINE CHARACTERISTICS Group data for the critical demographic and baseline characteristics of the patients, as well as other factors arising during the study that could affect response, should be presented in this section and comparability of the treatment groups for all relevant characteristics should be displayed by use of tables or graphs in section 14.1. The data for the patient sample included in the \"all patients with data\" analysis should be given first. This can then be followed by data on other groups used in principal analyses, such as the \"per-protocol\" analysis or other analyses, e.g., groups defined by compliance, concomitant disease/therapy, or demographic/baseline characteristics. 13 Structure and Content of Clinical Study Reports When such groups are used, data for the complementary excluded group should also be shown. In a multicentre study where appropriate, comparability should be assessed by centre, and centres should be compared. A diagram showing the relationship between the entire sample and any other analysis groups should be provided. The critical variables will depend on the specific nature of the disease and on the protocol but will usually include: ¾ demographic variables − age − sex − race ¾ disease factors − specific entry criteria (if not uniform), duration, stage and severity of disease and other clinical classifications and sub-groupings in common usage or of known prognostic significance − baseline values for critical clinical measurements carried out during the study or identified as important indicators of prognosis or response to", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "therapy", "definition": "− concomitant illness at trial initiation, such as renal disease, diabetes,", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "heart failure", "definition": "− relevant previous illness − relevant previous treatment for illness treated in the study − concomitant treatment maintained, even if the dose was changed during the study, including oral contraceptive and hormone replacement therapy; treatments stopped at entry into the study period (or changed at study initiation) ¾ other factors that might affect response to therapy (e.g., weight, renin status, antibody levels, metabolic status) ¾ other possibly relevant variables (e.g., smoking, alcohol intake, special diets) and, for women, menstrual status and date of last menstrual period, if pertinent for the study. In addition to tables and graphs giving group data for these baseline variables, relevant individual patient demographic and baseline data, including laboratory values, and all concomitant medication for all individual patients randomised (broken down by treatment and by centre for multicentre studies) should be presented in by- patient tabular listings in appendix 16.2.4. Although some regulatory authorities will require all baseline data to be presented elsewhere in tabular listings, the appendix to the study report should be limited to only the most relevant data, generally the variables listed above. 14 Structure and Content of Clinical Study Reports 11.3 MEASUREMENTS OF TREATMENT COMPLIANCE Any measurements of compliance of individual patients with the treatment regimen under study and drug concentrations in body fluids should be summarised, analysed by treatment group and time interval, and tabulated in Appendix 16.2.5. 11.4 EFFICACY RESULTS AND TABULATIONS OF INDIVIDUAL PATIENT", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "DATA", "definition": "11.4.1 Analysis of Efficacy Treatment groups should be compared for all critical measures of efficacy (primary and secondary end-points; any pharmacodynamic end points studied), as well as benefit/risk assessment(s) in each patient where these are utilised. In general, the results of all analyses contemplated in the protocol and an analysis including all patients with on-study data should be performed in studies intended to establish efficacy. The analysis should show the size (point estimate) of the difference between the treatments, the associated confidence interval, and where utilised, the results of hypothesis testing. Analyses based on continuous variables (e.g., mean blood pressure or depression scale score) and categorical responses (e.g., cure of an infection) can be equally valid; ordinarily both should be presented if both were planned and are available. If categories are newly created, (i.e., not in the statistical plan) the basis for them should be explained. Even if one variable receives primary attention (e.g., in a blood pressure study, supine blood pressure at week x), other reasonable measures (e.g., standing blood pressure and blood pressures at other particular times) should be assessed, at least briefly. In addition, the time course of response should be described, if possible. For a multicentre study, where appropriate, data display and analysis of individual centres should be included for critical variables to give a clear picture of the results at each site, especially the larger sites. If any critical measurements or assessments of efficacy or safety outcomes were made by more than one party (e.g., both the investigator and an expert committee may offer an opinion on whether a patient had an acute infarction), overall differences between the ratings should be shown, and each patient having disparate assessments should be identified. The assessments used should be clear in all analyses. In many cases, efficacy and safety endpoints are difficult to distinguish, (e.g., deaths in a fatal disease study). Many of the principles addressed below should be adopted for critical safety measures as well. 11.4.2 Statistical/Analytical Issues The statistical analysis used should be described for clinical and statistical reviewers in the text of the report, with detailed documentation of statistical methods (see section Annex IX) presented in appendix 16.1.9. Important features of the analysis including the particular methods used, adjustments made for demographic or baseline measurements or concomitant therapy, handling of drop-outs and missing data, adjustments for multiple comparisons, special analyses of multicentre studies, and adjustments for interim analyses, should be discussed. Any changes in the analysis made after blind-breaking should be identified. 15 Structure and Content of Clinical Study Reports In addition to the general discussion the following specific issues should be addressed (unless not applicable): 11.4.2.1 Adjustments for Covariates Selection of, and adjustments for, demographic or baseline measurements, concomitant therapy, or any other covariate or prognostic factor should be explained in the report, and methods of adjustment, results of analyses, and supportive information (e.g., ANCOVA or Cox regression output) should be included in the detailed documentation of statistical methods. If the covariates or methods used in these analyses differed from those planned in the protocol, the differences should be explained and where possible and relevant, the results of planned analyses should also be presented. Although not part of the individual study report, comparisons of covariate adjustments and prognostic factors across individual studies may be an informative analysis in a summary of clinical efficacy data. 11.4.2.2 Handling of Dropouts or Missing Data There are several factors that may affect dropout rates. These include the duration of the study, the nature of the disease, the efficacy and toxicity of the drug under study, and other factors that are not therapy related. Ignoring the patients who dropped out of the study and drawing conclusions based only on patients who completed the study can be misleading. A large number of dropouts, however, even if included in an analysis, may introduce bias, particularly if there are more early dropouts in one treatment group or the reasons for dropping out are treatment or outcome related. Although the effects of early dropouts, and sometimes even the direction of bias, can be difficult to determine, possible effects should be explored as fully as possible. It may be helpful to examine the observed cases at various time points or, if dropouts were very frequent, to concentrate on analyses at time points when most of the patients were still under observation and when the full effect of the drug was realised. It may also be helpful to examine modelling approaches to the evaluation of such incomplete data sets. The results of a clinical trial should be assessed not only for the subset of patients who completed the study, but also for the entire patient population as randomised or at least for all those with any on-study measurements. Several factors need to be considered and compared for the treatment groups in analysing the effects of dropouts: the reasons for the dropouts, the time to dropout, and the proportion of dropouts among treatment groups at various time points. Procedures for dealing with missing data, e.g., use of estimated or derived data, should be described. Detailed explanation should be provided as to how such estimations or derivations were done and what underlying assumptions were made. 11.4.2.3 Interim Analyses and Data Monitoring The process of examining and analysing data accumulating in a clinical trial, either formally or informally, can introduce bias and/or increase type I error. Therefore, all interim analyses, formal or informal, pre-planned or ad hoc, by any study participant, sponsor staff member, or data monitoring group should be described in full, even if the treatment groups were not identified. The need for statistical adjustment because of such analyses should be addressed. Any operating instructions or procedures used for such analyses should be described. The minutes of meetings of any data monitoring group and any data reports reviewed at those meetings, particularly a meeting that led to a change in the protocol or early termination of the study, may be 16 Structure and Content of Clinical Study Reports helpful and should be provided in appendix 16.1.9. Data monitoring without code- breaking should also be described, even if this kind of monitoring is considered to cause no increase in type I error. 11.4.2.4", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "Multicentre Studies", "definition": "A multicentre study is a single study under a common protocol, involving several centres (e.g., clinics, practices, hospitals) where the data collected are intended to be analysed as a whole (as opposed to a post-hoc decision to combine data or results from separate studies). Individual centre results should be presented, however, where appropriate, e.g., when the centres have sufficient numbers of patients to make such analysis potentially valuable, the possibility of qualitative or quantitative treatment- by-centre interaction should be explored. Any extreme or opposite results among centres should be noted and discussed, considering such possibilities as differences in study conduct, patient characteristics, or clinical settings. Treatment comparison should include analyses that allow for centre differences with respect to response. If appropriate, demographic, baseline, and post-baseline data, as well as efficacy data, should be presented by centre, even though the combined analysis is the primary one. 11.4.2.5 Multiple Comparison/Multiplicity False positive findings increase in number as the number of significance tests (number of comparisons) performed increases. If there was more than one primary endpoint (outcome variable), more than one analysis of particular endpoint, or if there were multiple treatment groups, or subsets of the patient population being examined, the statistical analysis should reflect awareness of this and either explain the statistical adjustment used for type I error criteria or give reasons why it was considered unnecessary. 11.4.2.6 Use of an \"Efficacy Subset\" of Patients Particular attention should be devoted to the effects of dropping patients with available data from analyses because of poor compliance, missed visits, ineligibility, or any other reason. As noted above, an analysis using all available data should be carried out for all studies intended to establish efficacy, even if it is not the analysis proposed as the primary analysis by the applicant. In general, it is advantageous to demonstrate robustness of the principal trial conclusions with respect to alternative choices of patient populations for analysis. Any substantial differences resulting from the choice of patient population for analysis should be the subject of explicit discussion. 11.4.2.7 Active-Control Studies Intended to Show Equivalence If an active control study is intended to show equivalence (i.e., lack of a difference greater than a specified size) between the test drug/investigational product and the active control/comparator, the analysis should show the confidence interval for the comparison between the two agents for critical end points and the relation of that interval to the prespecified degree of inferiority that would be considered unacceptable. (See 9.2, for important considerations when using the active control equivalence design.) 17 Structure and Content of Clinical Study Reports 11.4.2.8 Examination of Subgroups If the size of the study permits, important demographic or baseline value-defined subgroups should be examined for unusually large or small responses and the results presented, e.g., comparison of effects by age, sex, or race, by severity or prognostic groups, by history of prior treatment with a drug of the same class etc. If these analyses were not carried out because the study was too small it should be noted. These analyses are not intended to \"salvage\" an otherwise non-supportive study but may suggest hypotheses worth examining in other studies or be helpful in refining labelling information, patient selection, dose selection etc. Where there is a prior hypothesis of a differential effect in a particular subgroup, this hypothesis and its assessment should be part of the planned statistical analysis. 11.4.3 Tabulation of Individual Response Data In addition to tables and graphs representing group data, individual response data and other relevant study information should be presented in tables. Some regulatory authorities may require all individual data in archival case report tabulations. What needs to be included in the report will vary from study to study and from one drug class to another and the applicant must decide, if possible after consultation with the regulatory authority, what to include in appendix to the study report. The study report should indicate what material is included as an appendix, what is in the more extensive archival case report tabulations, if required by the regulatory authority, and what is available on request. For a controlled study in which critical efficacy measurements or assessments (e.g., blood or urine cultures, pulmonary function tests, angina frequency, or global evaluations) are repeated at intervals, the data listings accompanying the report should include, for each patient, a patient identifier, all measured or observed values of critical measurements, including baseline measurements, with notation of the time during the study (e.g., days on therapy and time of day, if relevant) when the measurements were made, the drug/dose at the time (if useful, given as mg/kg), any measurements of compliance, and any concomitant medications at the time of, or close to the time of, measurement or assessment. If, aside from repeated assessments, the study included some overall responder vs non-responder evaluation(s), (bacteriologic cure or failure), it should also be included. In addition to critical measurements, the tabulation should note whether the patient was included in the efficacy evaluation (and which evaluation, if more than one), provide patient compliance information, if collected, and a reference to the location of the case report form, if included. Critical baseline information such as age, sex, weight, disease being treated (if more than one in study), and disease stage or severity, is also helpful. The baseline values for critical measurements would ordinarily be included as zero time values for each efficacy measurement. The tabulation described should usually be included in appendix 16.2.6 of the study report, rather than in the more extensive case report tabulations required by some regulatory authorities, because it represents the basic efficacy data supporting summary tables. Such a thorough tabulation can be unwieldy for review purposes, however, and it is expected that more targeted displays will be developed as well. For example, if there are many measurements reported, tabulations of the most critical measurements for each patient (e.g., the blood pressure value at certain visits might be more important than others) will be useful in providing an overview of each individual's results in a study, with each patient's response summarised on a single line or small number of lines. 18 Structure and Content of Clinical Study Reports 11.4.4 Drug Dose, Drug Concentration, and Relationships to Response When the dose in each patient can vary, the actual doses received by patients should be shown and individual patient's doses should be tabulated. Although studies not designed as dose-response studies may have limited ability to contribute dose- response information, the available data should be examined for whatever information they can yield. In examining the dose response, it may be helpful to calculate dose as mg/kg body weight or mg/m² body surface. Drug concentration information, if available, should also be tabulated (Appendix 16.2.5), analysed in pharmacokinetic terms and, if possible, related to response. Further guidance on the design and analysis of studies exploring dose-response or concentration response can be found in the ICH Guideline \"Dose-Response Information to Support Drug Registration\". 11.4.5 Drug-Drug and Drug-Disease Interactions Any apparent relationship between response and concomitant therapy and between response and past and/or concurrent illness should be described. 11.4.6 By-Patient Displays While individual patient data ordinarily can be displayed in tabular listings, it has on occasion been helpful to construct individual patient profiles in other formats, such as graphic displays. These might, for example, show the value of (a) particular parameter(s) over time, the drug dose over the same period, and the times of particular events (e.g., an adverse event or change in concomitant therapy). Where group mean data represent the principal analyses, this kind of \"case report extract\" may offer little advantage; it may be helpful, however, if overall evaluation of individual responses is a critical part of the analysis. 11.4.7 Efficacy Conclusions The important conclusions concerning efficacy should be concisely described, considering primary and secondary end points, pre-specified and alternative statistical approaches and results of exploratory analyses. 12.", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "SAFETY EVALUATION", "definition": "Analysis of safety-related data can be considered at three levels. First, the extent of exposure (dose, duration, number of patients) should be examined to determine the degree to which safety can be assessed from the study. Second, the more common adverse events, laboratory test changes etc. should be identified, classified in some reasonable way, compared for treatment groups, and analysed, as appropriate, for factors that may affect the frequency of adverse reactions/events, such as time dependence, relation to demographic characteristics, relation to dose or drug concentration etc. Finally, serious adverse events and other significant adverse events should be identified, usually by close examination of patients who left the study prematurely because of an adverse event, whether or not identified as drug related, or who died. The ICH Guideline on Clinical Safety Data Management, Definitions and Standards for Expedited Reporting defines serious adverse events as follows: a \"serious adverse event\" (experience) or reaction is any untoward medical occurrence that at any dose: results in death, is life-threatening, requires inpatient hospitalisation or prolongation of existing hospitalisation, results in persistent or significant disability/incapacity, or is a congenital anomaly/birth defect. 19 Structure and Content of Clinical Study Reports For the purpose of this guideline, \"other significant adverse events\" are marked haematological and other laboratory abnormalities and any adverse events that led to an intervention, including withdrawal of drug treatment, dose reduction or significant additional concomitant therapy. In the following sections, three kinds of analysis and display are called for: 1) summarised data, often using tables and graphical presentations presented in the main body of the report 2) listings of individual patient data, and 3) narrative statements of events of particular interest. In all tabulations and analyses, events associated with both test drug and control treatment should be displayed. 12.1", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "EXTENT OF EXPOSURE", "definition": "The extent of exposure to test drugs/investigational products (and to active control and placebo) should be characterised according to the number of patients exposed, the duration of exposure, and the dose to which they were exposed. • Duration: Duration of exposure to any dose can be expressed as a median or mean, but it is also helpful to describe the number of patients exposed for specified periods of time, such as for one day or less, 2 days to one week, more than one week to one month, more than one month to 6 months etc. The numbers exposed to test drug(s)/investigational product(s) for the various durations should also be broken down into age, sex, and racial subgroups, and any other pertinent subgroups, such as disease (if more than one is represented), disease severity, concurrent illness. • Dose: The mean or median dose used and the number of patients exposed to specified daily dose levels should be given; the daily dose levels used could be the maximum dose for each patient, the dose with longest exposure for each patient, or the mean daily dose. It is often useful to provide combined dose- duration information, such as the numbers exposed for a given duration (e.g., at least one month) to the most common dose, the highest dose, the maximum recommended dose etc. In some cases, cumulative dose might be pertinent. Dosage may be given as the actual daily dose or on a mg/kg or mg/m² basis as appropriate. The numbers of patients exposed to various doses should be broken down into age, sex, and racial subgroups, and any other pertinent subgroups. • Drug concentration: If available, drug concentration data (e.g., concentration at the time of an event, maximum plasma concentration, area under curve) may be helpful in individual patients for correlation with adverse events or changes in laboratory variables. (Appendix 16.2.5.) It is assumed that all patients entered into treatment who received at least one dose of the treatment are included in the safety analysis; if that is not so, an explanation should be provided. 20 Structure and Content of Clinical Study Reports 12.2 ADVERSE EVENTS (AEs) 12.2.1 Brief Summary of Adverse Events The overall adverse event experience in the study should be described in a brief narrative, supported by the following more detailed tabulations and analyses. In these tabulations and analyses, events associated with both the test drug and control treatment should be displayed. 12.2.2 Display of Adverse Events All adverse events occurring after initiation of study treatments (including events likely to be related to the underlying disease or likely to represent concomitant illness, unless there is a prior agreement with the regulatory authority to consider specified events as disease related) should be displayed in summary tables (section 14.3.1). The tables should include changes in vital signs and any laboratory changes that were considered serious adverse events or other significant adverse events. In most cases, it will also be useful to identify in such tables \"treatment emergent signs and symptoms\" (TESS; those not seen at baseline and those that worsened even if present at baseline). The tables should list each adverse event, the number of patients in each treatment group in whom the event occurred, and the rate of occurrence. When treatments are cyclical, e.g., cancer chemotherapy, it may also be helpful to list results separately for each cycle. Adverse events should be grouped by body system. Each event may then be divided into defined severity categories (e.g., mild, moderate, severe) if these were used. The tables may also divide the adverse events into those considered at least possibly related to drug use and those considered not related, or use some other causality scheme (e.g., unrelated or possibly, probably, or definitely related). Even when such a causality assessment is used, the tables should include all adverse events, whether or not considered drug related, including events thought to represent intercurrent illnesses. Subsequent analyses of the study or of the overall safety data base may help to distinguish between adverse events that are, or are not, considered drug related. So that it is possible to analyse and evaluate the data in these tables, it is important to identify each patient having each adverse event. An example of such a tabular presentation is shown below. 21 Structure and Content of Clinical Study Reports ADVERSE EVENTS: NUMBER OBSERVED AND RATE, WITH PATIENT IDENTIFICATIONS", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "N61", "definition": "12 (24%) 4 (8%) *NR = not related; related could be expanded, e.g., as definite, probable, possible **Patient identification number In addition to these complete tables provided in 14.3.1, an additional summary table comparing treatment and control groups, without the patient identifying numbers limited to relatively common adverse events (e.g., those in at least 1% of the treated group), should be provided in the body of the report. In presenting adverse events, it is important both to display the original terms used by the investigator and to attempt to group related events (i.e., events that probably represent the same phenomena) so that the true occurrence rate is not obscured. One way to do this is with a standard adverse reaction/events dictionary. 12.2.3 Analysis of Adverse Events The basic display of adverse event rates described in section 12.2.2 (and located in section 14.3.1) of the report, should be used to compare rates in treatment and control groups. For this analysis it may be helpful to combine the event severity categories and the causality categories, leading to a simpler side-by-side comparison of treatment groups. In addition, although this is usually best done in an integrated analysis of safety, if study size and design permit, it may be useful to examine the more common adverse events that seem to be drug related for relationship to dosage and to mg/kg or mg/m² dose, to dose regimen, to duration of treatment, to total dose, to demographic characteristics such as age, sex, race, to other baseline features such as renal status, to efficacy outcomes, and to drug concentration. It may also be useful to examine time of onset and duration of adverse events. A variety of additional analyses may be suggested by the study results or by the pharmacology of the test drug/investigational product. It is not intended that every adverse event be subjected to rigorous statistical evaluation. It may be apparent from initial display and inspection of the data that a significant relation to demographic or other baseline features is not present. If the studies are small and if the number of events is relatively small, it may be sufficient to limit analyses to a comparison of treatment and control. 22 Structure and Content of Clinical Study Reports Under certain circumstances, life table or similar analyses may be more informative than reporting of crude adverse event rates. When treatments are cyclical, e.g., cancer chemotherapy, it may also be helpful to analyse results separately for each cycle. 12.2.4 Listing of Adverse Events by Patient All adverse events for each patient, including the same event on several occasions should be listed in appendix 16.2.7, giving both preferred term and the original term used by the investigator. The listing should be by investigator and by treatment group and should include: − Patient identifier − Age, race, sex, weight (height, if relevant) − Location of CRFs, if provided − The adverse event (preferred term, reported term) − Duration of the adverse event − Severity (e.g., mild, moderate, severe) − Seriousness (serious/non-serious) − Action taken (none, dose reduced, treatment stopped, specific treatment instituted etc.) − Outcome (e.g., CIOMS format) − Causality assessment (e.g., related/not related). How this was determined should be described in the table or elsewhere − Date of onset or date of clinic visit at which the event was discovered − Timing of onset of the adverse event in relation to last dose of test drug/investigational product (when applicable) − Study treatment at time of event or most recent study treatment taken − Test drug/investigational product dose in absolute amount, mg/kg or mg/m² at", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "time of event", "definition": "− Drug concentration (if known) − Duration of test drug/investigational product treatment − Concomitant treatment during study. Any abbreviations and codes should be clearly explained at the beginning of the listing or, preferably, on each page. 12.3 DEATHS, OTHER SERIOUS ADVERSE EVENTS, AND OTHER SIGNIFICANT ADVERSE EVENTS Deaths, other serious adverse events, and other significant adverse events deserve special attention. 12.3.1 Listing of Deaths, other Serious Adverse Events and Other Significant", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "Adverse Events", "definition": "Listings, containing the same information as called for in section 12.2.4 above, should be provided for the following events. 12.3.1.1", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "Deaths", "definition": "23 Structure and Content of Clinical Study Reports All deaths during the study, including the post treatment follow-up period, and deaths that resulted from a process that began during the study, should be listed by patient in section 14.3.2. 12.3.1.2 Other Serious Adverse Events All serious adverse events (other than death but including the serious adverse events temporally associated with or preceding the deaths) should be listed in section 14.3.2. The listing should include laboratory abnormalities, abnormal vital signs and abnormal physical observations that were considered serious adverse events. 12.3.1.3 Other Significant Adverse Events Marked haematological and other laboratory abnormalities (other than those meeting the definition of serious) and any events that led to an intervention, including withdrawal of test drug/investigational product treatment, dose reduction, or significant additional concomitant therapy, other than those reported as serious adverse events, should be listed in section 14.3.2. 12.3.2 Narratives of Deaths, Other Serious Adverse Events and Certain Other Significant Adverse Events There should be brief narratives describing each death, each other serious adverse event, and those of the other significant adverse events that are judged to be of special interest because of clinical importance. These narratives can be placed either in the text of the report or in section 14.3.3, depending on their number. Events that were clearly unrelated to the test drug/investigational product may be omitted or described very briefly. In general, the narrative should describe the following: the nature and intensity of event, the clinical course leading up to event, with an indication of timing relevant to test drug/investigational product administration; relevant laboratory measurements, whether the drug was stopped, and when; countermeasures; post mortem findings; investigator's opinion on causality, and sponsor's opinion on causality, if appropriate. In addition, the following information should be included: − Patient identifier − Age and sex of patient; general clinical condition of patient, if appropriate − Disease being treated (if the same for all patients this is not required) with duration (of current episode) of illness − Relevant concomitant/previous illnesses with details of occurrence/duration − Relevant concomitant/previous medication with details of dosage − Test drug/investigational product administered, drug dose, if this varied among patients, and length of time administered. 12.3.3 Analysis and Discussion of Deaths, Other Serious Adverse Events and Other Significant Adverse Events The significance of the deaths, other serious adverse events and other significant adverse events leading to withdrawal, dose reduction or institution of concomitant therapy should be assessed with respect to the safety of the test drug/investigational product. Particular attention should be paid to whether any of these events may represent a previously unsuspected important adverse effect of the test drug/investigational product. For serious adverse events that appear of particular 24 Structure and Content of Clinical Study Reports importance, it may be useful to use life table or similar analyses to show their relation to time on test drug/investigational product and to assess their risk over time. 12.4 CLINICAL LABORATORY EVALUATION 12.4.1 Listing of Individual Laboratory Measurements by Patient (16.2.8) and Each Abnormal Laboratory Value (14.3.4) When required by regulatory authorities, the results of all safety-related laboratory tests should be available in tabular listings, using a display similar to the following, where each row represents a patient visit at which a laboratory study was done, with patients grouped by investigator (if more than one) and treatment group, and columns include critical demographic data, drug dose data, and the results of the laboratory tests. As not all tests can be displayed in a single table, they should be grouped logically (haematological tests, liver chemistries, electrolytes, urinalysis etc.). Abnormal values should be identified, e.g., by underlining, bracketing etc. These listings should be submitted as part of the registration/marketing application, when this is required, or may be available on request. LIST OF LABORATORY MEASUREMENTS", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "SGPT", "definition": "AP..........X # 1 # 2", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "70 kg", "definition": "50 kg 400mg 300mg V1*", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "V21", "definition": "* Vn = value of a particular test For all regulatory authorities, there should be a by-patient listing of all abnormal laboratory values in section 14.3.4, using the format described above. For laboratory abnormalities of special interest (abnormal laboratory values of potential clinical importance), it may also be useful to provide additional data, such as normal values before and after the abnormal value, and values of related laboratory tests. In some cases, it may be desirable to exclude certain abnormal values from further analysis. For example, single, non-replicated, small abnormalities of some tests (e.g., uric acid or electrolytes) or occasional low values of some tests (e.g., transaminase, alkaline phosphatase, BUN etc.) can probably be defined as clinically insignificant and excluded. Any such decisions should be clearly explained, however, and the complete list of values provided (or available to authorities on request) should identify every abnormal value. 12.4.2 Evaluation of Each Laboratory Parameter The necessary evaluation of laboratory values must in part be determined by the results seen, but, in general, the following analyses should be provided. For each analysis, comparison of the treatment and control groups should be carried out, as appropriate, and as compatible with study size. In addition, normal laboratory ranges should be given for each analysis. 25 Structure and Content of Clinical Study Reports 12.4.2.1 Laboratory Values Over Time For each parameter at each time over the course of the study (e.g., at each visit) the following should be described: the group mean or median values, the range of values, and the number of patients with abnormal values, or with abnormal values that are of a certain size (e.g., twice the upper limit of normal, 5 times the upper limit; choices should be explained). Graphs may be used. 12.4.2.2 Individual Patient Changes An analysis of individual patient changes by treatment group should be given. A variety of approaches may be used, including: I. \"Shift tables\" - These tables show the number of patients who are low, normal, or high at baseline and then at selected time intervals. II. Tables showing the number or fraction of patients who had a change in parameter of a predetermined size at selected time intervals. For example, for BUN, it might be decided that a change of more than 10 mg/dL BUN should be noted. For this parameter, the number of patients having a change less than this or greater than this would be shown for one or more visits, usually grouping patients separately depending on baseline BUN (normal or elevated). The possible advantage of this display, compared to the usual shift table, is that changes of a certain size are noted, even if the final value is not abnormal. III. A graph comparing the initial value and the on-treatment values of a laboratory measurement for each patient by locating the point defined by the initial value on the abscissa and a subsequent value on the ordinate. If no changes occur, the point representing each patient will be located on the 45° line. A general shift to higher values will show a clustering of points above the 45° line. As this display usually shows only a single time point for a single treatment, interpretation requires a time series of these plots for treatment and control groups. Alternatively the display could show baseline and most extreme on-treatment value. These displays identify outliers readily (it is useful to include patient identifiers for the outliers). 12.4.2.3 Individual Clinically Significant Abnormalities Clinically significant changes (defined by the applicant) should be discussed. A narrative of each patient whose laboratory abnormality was considered a serious adverse event and, in certain cases, considered an other significant adverse event, should be provided under sections 12.3.2 or 14.3.3. When toxicity grading scales are used (e.g., WHO, NCI), changes graded as severe should be discussed regardless of seriousness. An analysis of the clinically significant changes, together with a recapitulation of discontinuations due to laboratory measurements, should be provided for each parameter. The significance of the changes and likely relation to the treatment should be assessed, e.g., by analysis of such features as relationship to dose, relationship to drug concentration, disappearance on continued therapy, positive dechallenge, positive rechallenge, and the nature of concomitant therapy. 26 Structure and Content of Clinical Study Reports 12.5 VITAL SIGNS, PHYSICAL FINDINGS AND OTHER OBSERVATIONS", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "RELATED TO SAFETY", "definition": "Vital signs, other physical findings, and other observations related to safety should be analysed and presented in a way similar to laboratory variables. If there is evidence of a drug effect, any dose-response or drug concentration-response relationship or relationship to patient variables (e.g., disease, demographics, concomitant therapy) should be identified and the clinical relevance of the observation described. Particular attention should be given to changes not evaluated as efficacy variables and to those considered to be adverse events. 12.6", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "SAFETY CONCLUSIONS", "definition": "The overall safety evaluation of the test drug(s)/investigational product(s) should be reviewed, with particular attention to events resulting in changes of dose or need for concomitant medication, serious adverse events, events resulting in withdrawal, and deaths. Any patients or patient groups at increased risk should be identified and particular attention paid to potentially vulnerable patients who may be present in small numbers, e.g., children, pregnant women, frail elderly, people with marked abnormalities of drug metabolism or excretion etc. The implication of the safety evaluation for the possible uses of the drug should be described. 13. DISCUSSION AND OVERALL CONCLUSIONS The efficacy and safety results of the study and the relationship of risks and benefit should be briefly summarised and discussed, referring to the tables, figures, and sections above as needed. The presentation should not simply repeat the description of results nor introduce new results. The discussion and conclusions should clearly identify any new or unexpected findings, comment on their significance and discuss any potential problems such as inconsistencies between related measures. The clinical relevance and importance of the results should also be discussed in the light of other existing data. Any specific benefits or special precautions required for individual subjects or at-risk groups and any implications for the conduct of future studies should be identified. Alternatively, such discussions may be reserved for summaries of safety and efficacy referring to the entire dossier (integrated summaries). 14. TABLES, FIGURES AND GRAPHS REFERRED TO BUT NOT", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "INCLUDED IN THE TEXT", "definition": "Figures should be used to visually summarise the important results, or to clarify results that are not easily understood from tables. Important demographic, efficacy and safety data should be presented in summary figures or tables in the text of the report. However, if these become obtrusive because of size or number they should be presented here, cross-referenced to the text, along with supportive, or additional, figures, tables or listings. The following information may be presented in this section of the core clinical study report: 14.1", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "DEMOGRAPHIC DATA", "definition": "Summary figures and tables 27 Structure and Content of Clinical Study Reports 14.2", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "EFFICACY DATA", "definition": "Summary figures and tables 14.3", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "SAFETY DATA", "definition": "Summary figures and tables 14.3.1 Displays of Adverse Events 14.3.2 Listings of Deaths, Other Serious and Significant Adverse Events 14.3.3 Narratives of Deaths, Other Serious and Certain Other Significant Adverse", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "Events", "definition": "14.3.4 Abnormal Laboratory Value Listing (Each Patient) 15.", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "REFERENCE LIST", "definition": "A list of articles from the literature pertinent to the evaluation of the study should be provided. Copies of important publications should be attached in an appendix (16.1.11 and 16.1.12). References should be given in accordance with the internationally accepted standards of the 1979 Vancouver Declaration on \"Uniform Requirements for Manuscripts Submitted to Biomedical Journals\" or the system used in \"Chemical Abstracts\". 16.", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "APPENDICES", "definition": "This section should be prefaced by a full list of all appendices available for the study report. Where permitted by the regulatory authority, some of the following appendices need not be submitted with the report but need to be provided only on request. The applicant should therefore clearly indicate those appendices that are submitted with the report. N.B. In order to have appendices available on request, they should be finalised by the time of filing of the submission. 16.1", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "STUDY INFORMATION", "definition": "16.1.1 Protocol and protocol amendments 16.1.2 Sample case report form (unique pages only) 16.1.3 List of IECs or IRBs (plus the name of the committee Chair if required by the regulatory authority) - Representative written information for patient and", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "sample consent forms", "definition": "16.1.4 List and description of investigators and other important participants in the study, including brief (1 page) CVs or equivalent summaries of training and experience relevant to the performance of the clinical study 16.1.5 Signatures of principal or coordinating investigator(s) or sponsor’s responsible medical officer, depending on the regulatory authority's requirement 16.1.6 Listing of patients receiving test drug(s)/investigational product(s) from specific batches, where more than one batch was used 28 Structure and Content of Clinical Study Reports 16.1.7 Randomisation scheme and codes (patient identification and treatment assigned) 16.1.8 Audit certificates (if available) (see Annex IVa and IVb of the guideline) 16.1.9 Documentation of statistical methods 16.1.10 Documentation of inter-laboratory standardisation methods and quality assurance procedures if used 16.1.11 Publications based on the study 16.1.12 Important publications referenced in the report 16.2. PATIENT DATA LISTINGS 16.2.1 Discontinued patients 16.2.2 Protocol deviations 16.2.3 Patients excluded from the efficacy analysis 16.2.4 Demographic data 16.2.5 Compliance and/or drug concentration data (if available) 16.2.6 Individual efficacy response data 16.2.7 Adverse event listings (each patient) 16.2.8. Listing of individual laboratory measurements by patient, when required by regulatory authorities 16.3", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "CASE REPORT FORMS", "definition": "16.3.1 CRFs for deaths, other serious adverse events and withdrawals for AE 16.3.2 Other CRFs submitted 16.4. INDIVIDUAL PATIENT DATA LISTINGS (US ARCHIVAL LISTINGS) 29 Structure and Content of Clinical Study Reports", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "ANNEX I", "definition": "Name of Sponsor/Company: Individual Study Table", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "of the Dossier", "definition": "(For National Authority Use Only) Name of Finished Product: Volume: Name of Active Ingredient: Page: Criteria for evaluation: Efficacy: Safety: Statistical methods:", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "SUMMARY - CONCLUSIONS", "definition": "EFFICACY RESULTS: SAFETY RESULTS: CONCLUSION: Date of the report: 31 Structure and Content of Clinical Study Reports", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "MEDICAL OFFICER", "definition": "AFFILIATION: _______________________ ______________________________ ______________________________ DATE: _______________________ 32 Structure and Content of Clinical Study Reports", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "ANNEX III a", "definition": "STUDY DESIGN AND SCHEDULE OF ASSESSMENTS", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "Run-in", "definition": "5 mg 10 mg 5 mg 10 mg Test Drug/", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "Product B", "definition": "5 mg 10 mg 5 mg 10 mg", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "Visit", "definition": "-2 (-3) 1 0 2 3 3 6 4 9 5 12 6", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "1 = 14-20 days after visit 1", "definition": "2 = 1-7 days after the first exercise test 33 Structure and Content of Clinical Study Reports 34", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "ANNEX III b", "definition": "STUDY DESIGN AND SCHEDULE OF ASSESSMENTS", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "Dose 1", "definition": "1 2 3 4 R 11", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "Dose 3", "definition": "(7d) (7d) (7d) (7d) 5 6 7 8 9", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "Study Week", "definition": "-2 -1 0 1 2 3 4 5 6 8", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "PATIENTS RECEIVING", "definition": "DOUBLE-BLIND MEDICATION N = 340", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "Withdrawn", "definition": "ADVERSE EVENT (20) UNSAT. RESPONSE (32) etc. ...... etc. ...... 36 Structure and Content of Clinical Study Reports", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "Screening Failures", "definition": "Reasons: ___________ (300) ___________ (271) ___________ N= 8", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "ANY MEDICATION", "definition": "Reasons: _________ (2) _________ (4) _________ (2) N= 1724", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "Placebo", "definition": "*The specific reaction leading to discontinuation (Repeat for other centres) 37 Structure and Content of Clinical Study Reports", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "Reference Tables", "definition": "Summary: 38 Structure and Content of Clinical Study Reports", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "ANNEX VII", "definition": "STUDY # (Data Set Identification) NUMBER OF PATIENTS EXCLUDED FROM EFFICACY ANALYSIS Test Drug/Investigational Product N =", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "Total", "definition": "Similar tables should be prepared for the other treatment groups. 39 Structure and Content of Clinical Study Reports", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "ANNEX VIII", "definition": "GUIDANCE FOR SECTION 11.4.2 - STATISTICAL/ANALYTICAL ISSUES AND APPENDIX 16.1.9 A. Statistical Considerations Details of the statistical analysis performed on each primary efficacy variable should be presented in an appendix. Details reported should include at least the following information: a) The statistical model underlying the analysis. This should be presented precisely and completely, using references if necessary. b) A statement of the clinical claim tested in precise statistical terms, e.g., in terms of null and alternative hypotheses. c) The statistical methods applied to estimate effects, construct confidence intervals etc. Literature references should be included where appropriate. d) The assumptions underlying the statistical methods. It should be shown, insofar as statistically reasonable, that the data satisfy crucial assumptions, especially when necessary to confirm the validity of an inference. When extensive statistical analyses have been performed by the applicant, it is essential to consider the extent to which the analyses were planned prior to the availability of data and, if they were not, how bias was avoided in choosing the particular analysis used as a basis for conclusions. This is particularly important in the case of any subgroup analyses, because if such analyses are not preplanned they will ordinarily not provide an adequate basis for definitive conclusions. (i) In the event data transformation was performed, a rationale for the choice of data transformation along with interpretation of the estimates of treatment effects based on transformed data should be provided. (ii) A discussion of the appropriateness of the choice of statistical procedure and the validity of statistical conclusions will guide the regulatory authority's statistical reviewer in determining whether reanalysis of data is needed. e) The test statistic, the sampling distribution of the test statistic under the null hypothesis, the value of the test statistic, significance level (i.e., p- value), and intermediate summary data, in a format that enables the regulatory authority's statistical reviewer to verify the results of the analysis quickly and easily. The p-values should be designated as one- or two-tailed. The rationale for using a one-tailed test should be provided. For example, the documentation of a two-sample t-test should consist of the value of the t-statistic, the associated degrees of freedom, the p-value, the two sample sizes, mean and variance for each of the samples, and the pooled estimate of variance. The documentation of multi-centre studies analysed by analysis of variance techniques should include, at a minimum, an analysis of variance table with terms for centres, treatments, their interaction, error, and total. For crossover designs, the documentation should include information regarding sequences, patients within sequences, baselines at the 40 Structure and Content of Clinical Study Reports start of each period, washouts and length of washouts, dropouts during each period, treatments, periods, treatment by period interaction, error, and total. For each source of variation, aside from the total, the table should contain the degrees of freedom, the sum of squares, the mean square, the appropriate F-test, the p-value, and the expected mean square. Intermediate summary data should display the demographic data and response data, averaged or otherwise summarised, for each centre-by- treatment combination (or other design characteristic such as sequence) at each observation time. B. Format and Specifications for Submission of Data Requested by Regulatory Authority's Statistical Reviewers In the report of each controlled clinical study, there should be data listings (tabulations) of patient data utilised by the sponsor for statistical analyses and tables supporting conclusions and major findings. These data listings are necessary for the regulatory authority's statistical review, and the sponsor may be asked to supply these patient data listings in a computer-readable form. 41", "sources": [ "ICH_E3.pdf" ], "file": "ICH_E3.pdf", "type": "pdf" }, { "term": "INTERNATIONAL CONFERENCE ON HARMONISATION OF TECHNICAL", "definition": "REQUIREMENTS FOR REGISTRATION OF PHARMACEUTICALS FOR HUMAN", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "USE", "definition": "ICH HARMONISED TRIPARTITE GUIDELINE GUIDELINE FOR GOOD CLINICAL PRACTICE E6(R1) Current Step 4 version", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "dated 10 June 1996", "definition": "(including the Post Step 4 corrections) This Guideline has been developed by the appropriate ICH Expert Working Group and has been subject to consultation by the regulatory parties, in accordance with the ICH Process. At Step 4 of the Process the final draft is recommended for adoption to the regulatory bodies of the European Union, Japan and USA. E6(R1)", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "E6", "definition": "Approval by the Steering Committee of Post-Step 4 editorial corrections. 10", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "June", "definition": "1996 E6(R1) GUIDELINE FOR GOOD CLINICAL PRACTICE ICH Harmonised Tripartite Guideline Having reached Step 4 of the ICH Process at the ICH Steering Committee meeting on 1 May 1996, this guideline is recommended for adoption to the three regulatory parties to ICH (This document includes the Post Step 4 corrections agreed by the Steering Committee on 10 June 1996)", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "INTRODUCTION.............................................................................................................1", "definition": "1. GLOSSARY .............................................................................................................2 2. THE PRINCIPLES OF ICH GCP........................................................................8 3. INSTITUTIONAL REVIEW BOARD/INDEPENDENT ETHICS COMMITTEE (IRB/IEC).......................................................................................9 3.1 Responsibilities.........................................................................................................9 3.2 Composition, Functions and Operations...............................................................11 3.3 Procedures ..............................................................................................................11 3.4 Records....................................................................................................................12 4. INVESTIGATOR ..................................................................................................12 4.1 Investigator's Qualifications and Agreements......................................................12 4.2 Adequate Resources ...............................................................................................12 4.3 Medical Care of Trial Subjects...............................................................................13 4.4 Communication with IRB/IEC...............................................................................13 4.5 Compliance with Protocol ......................................................................................13 4.6 Investigational Product(s)......................................................................................14 4.7 Randomization Procedures and Unblinding.........................................................15 4.8 Informed Consent of Trial Subjects.......................................................................15 4.9 Records and Reports...............................................................................................18 4.10 Progress Reports.....................................................................................................19 4.11 Safety Reporting.....................................................................................................19 4.12 Premature Termination or Suspension of a Trial ................................................19 4.13 Final Report(s) by Investigator..............................................................................20 5. SPONSOR..............................................................................................................20 5.1 Quality Assurance and Quality Control................................................................20 5.2 Contract Research Organization (CRO)................................................................20 5.3 Medical Expertise...................................................................................................21 5.4 Trial Design ............................................................................................................21 5.5 Trial Management, Data Handling, and Record Keeping....................................21", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Guideline for Good Clinical Practice", "definition": "8.4 After Completion or Termination of the Trial After completion or termination of the trial, all of the documents identified in sections 8.2 and 8.3 should be in the file together with the", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "ii", "definition": "Guideline for Good Clinical Practice 6.9 Statistics .................................................................................................................32 6.10 Direct Access to Source Data/Documents .............................................................33 6.11 Quality Control and Quality Assurance................................................................33 6.12 Ethics ......................................................................................................................33 6.13 Data Handling and Record Keeping......................................................................33 6.14 Financing and Insurance .......................................................................................33 6.15 Publication Policy...................................................................................................33 6.16 Supplements ...........................................................................................................33 7. INVESTIGATOR’S BROCHURE.......................................................................34 7.1 Introduction ............................................................................................................34 7.2 General Considerations..........................................................................................35 7.2.1 Title Page ..............................................................................................35 7.2.2 Confidentiality Statement....................................................................35 7.3 Contents of the Investigator’s Brochure ...............................................................35 7.3.1 Table of Contents..................................................................................35 7.3.2 Summary...............................................................................................35 7.3.3 Introduction ..........................................................................................35 7.3.4 Physical, Chemical, and Pharmaceutical Properties and Formulation ..........................................................................................35 7.3.5 Nonclinical Studies...............................................................................36 7.3.6 Effects in Humans ................................................................................37 7.3.7 Summary of Data and Guidance for the Investigator ........................38 7.4 APPENDIX 1: .........................................................................................................39 7.5 APPENDIX 2: .........................................................................................................40 8. ESSENTIAL DOCUMENTS FOR THE CONDUCT OF A CLINICAL TRIAL................................................................................................41 8.1 Introduction ............................................................................................................41 8.2 Before the Clinical Phase of the Trial Commences ..............................................42 8.3 During the Clinical Conduct of the Trial ..............................................................46 8.4 After Completion or Termination of the Trial ......................................................52", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "iii", "definition": "GUIDELINE FOR GOOD CLINICAL PRACTICE", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Introduction", "definition": "Essential Documents are those documents which individually and collectively permit evaluation of the conduct of a trial and the quality of the data produced. These documents serve to demonstrate the compliance of the investigator, sponsor and monitor with the standards of Good Clinical Practice and with all applicable regulatory requirements. Essential Documents also serve a number of other important purposes. Filing essential documents at the investigator/institution and sponsor sites in a timely manner can greatly assist in the successful management of a trial by the investigator, sponsor and monitor. These documents are also the ones which are usually audited by the sponsor's independent audit function and inspected by the regulatory authority(ies) as part of the process to confirm the validity of the trial conduct and the integrity of data collected. The minimum list of essential documents which has been developed follows. The various documents are grouped in three sections according to the stage of the trial during which they will normally be generated: 1) before the clinical phase of the trial commences, 2) during the clinical conduct of the trial, and 3) after completion or termination of the trial. A description is given of the purpose of each document, and whether it should be filed in either the investigator/institution or sponsor files, or both. It is acceptable to combine some of the documents, provided the individual elements are readily identifiable. Trial master files should be established at the beginning of the trial, both at the investigator/institution’s site and at the sponsor's office. A final close-out of a trial can only be done when the monitor has reviewed both investigator/institution and sponsor files and confirmed that all necessary documents are in the appropriate files. Any or all of the documents addressed in this guideline may be subject to, and should be available for, audit by the sponsor’s auditor and inspection by the regulatory authority(ies). 41 Guideline for Good Clinical Practice 8.2 Before the Clinical Phase of the Trial Commences During this planning stage the following documents should be generated and should be on file before the trial formally starts", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "GLOSSARY", "definition": "1.1 Adverse Drug Reaction (ADR) In the pre-approval clinical experience with a new medicinal product or its new usages, particularly as the therapeutic dose(s) may not be established: all noxious and unintended responses to a medicinal product related to any dose should be considered adverse drug reactions. The phrase responses to a medicinal product means that a causal relationship between a medicinal product and an adverse event is at least a reasonable possibility, i.e. the relationship cannot be ruled out. Regarding marketed medicinal products: a response to a drug which is noxious and unintended and which occurs at doses normally used in man for prophylaxis, diagnosis, or therapy of diseases or for modification of physiological function (see the ICH Guideline for Clinical Safety Data Management: Definitions and Standards for Expedited Reporting). 1.2 Adverse Event (AE) Any untoward medical occurrence in a patient or clinical investigation subject administered a pharmaceutical product and which does not necessarily have a causal relationship with this treatment. An adverse event (AE) can therefore be any unfavourable and unintended sign (including an abnormal laboratory finding), symptom, or disease temporally associated with the use of a medicinal (investigational) product, whether or not related to the medicinal (investigational) product (see the ICH Guideline for Clinical Safety Data Management: Definitions and Standards for Expedited Reporting). 1.3 Amendment (to the protocol) See Protocol Amendment. 1.4 Applicable Regulatory Requirement(s) Any law(s) and regulation(s) addressing the conduct of clinical trials of investigational products. 1.5 Approval (in relation to Institutional Review Boards) The affirmative decision of the IRB that the clinical trial has been reviewed and may be conducted at the institution site within the constraints set forth by the IRB, the institution, Good Clinical Practice (GCP), and the applicable regulatory requirements. 1.6", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Audit", "definition": "28 Guideline for Good Clinical Practice If or when sponsors perform audits, as part of implementing quality assurance, they should consider: 5.19.1 Purpose The purpose of a sponsor's audit, which is independent of and separate from routine monitoring or quality control functions, should be to evaluate trial conduct and compliance with the protocol, SOPs, GCP, and the applicable regulatory requirements. 5.19.2 Selection and Qualification of Auditors (a) The sponsor should appoint individuals, who are independent of the clinical trials/systems, to conduct audits. (b) The sponsor should ensure that the auditors are qualified by training and experience to conduct audits properly. An auditor’s qualifications should be documented. 5.19.3 Auditing Procedures (a) The sponsor should ensure that the auditing of clinical trials/systems is conducted in accordance with the sponsor's written procedures on what to audit, how to audit, the frequency of audits, and the form and content of audit reports. (b) The sponsor's audit plan and procedures for a trial audit should be guided by the importance of the trial to submissions to regulatory authorities, the number of subjects in the trial, the type and complexity of the trial, the level of risks to the trial subjects, and any identified problem(s). (c) The observations and findings of the auditor(s) should be documented. (d) To preserve the independence and value of the audit function, the regulatory authority(ies) should not routinely request the audit reports. Regulatory authority(ies) may seek access to an audit report on a case by case basis when evidence of serious GCP non-compliance exists, or in the course of legal proceedings. (e) When required by applicable law or regulation, the sponsor should provide an audit certificate. 5.20", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Audit Certificate", "definition": "A declaration of confirmation by the auditor that an audit has taken place. 1.8", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Audit Report", "definition": "A written evaluation by the sponsor's auditor of the results of the audit. 2 Guideline for Good Clinical Practice 1.9", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Audit Trail", "definition": "Documentation that allows reconstruction of the course of events. 1.10 Blinding/Masking A procedure in which one or more parties to the trial are kept unaware of the treatment assignment(s). Single-blinding usually refers to the subject(s) being unaware, and double-blinding usually refers to the subject(s), investigator(s), monitor, and, in some cases, data analyst(s) being unaware of the treatment assignment(s). 1.11 Case Report Form (CRF) A printed, optical, or electronic document designed to record all of the protocol required information to be reported to the sponsor on each trial subject. 1.12 Clinical Trial/Study Any investigation in human subjects intended to discover or verify the clinical, pharmacological and/or other pharmacodynamic effects of an investigational product(s), and/or to identify any adverse reactions to an investigational product(s), and/or to study absorption, distribution, metabolism, and excretion of an investigational product(s) with the object of ascertaining its safety and/or efficacy. The terms clinical trial and clinical study are synonymous. 1.13 Clinical Trial/Study Report A written description of a trial/study of any therapeutic, prophylactic, or diagnostic agent conducted in human subjects, in which the clinical and statistical description, presentations, and analyses are fully integrated into a single report (see the ICH Guideline for Structure and Content of Clinical Study Reports). 1.14 Comparator (Product) An investigational or marketed product (i.e., active control), or placebo, used as a reference in a clinical trial. 1.15 Compliance (in relation to trials) Adherence to all the trial-related requirements, Good Clinical Practice (GCP) requirements, and the applicable regulatory requirements. 1.16", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Confidentiality", "definition": "Prevention of disclosure, to other than authorized individuals, of a sponsor's proprietary information or of a subject's identity. 1.17", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Contract", "definition": "A written, dated, and signed agreement between two or more involved parties that sets out any arrangements on delegation and distribution of tasks and obligations and, if appropriate, on financial matters. The protocol may serve as the basis of a contract. 1.18 Coordinating Committee A committee that a sponsor may organize to coordinate the conduct of a multicentre trial. 3 Guideline for Good Clinical Practice 1.19 Coordinating Investigator An investigator assigned the responsibility for the coordination of investigators at different centres participating in a multicentre trial. 1.20 Contract Research Organization (CRO) A person or an organization (commercial, academic, or other) contracted by the sponsor to perform one or more of a sponsor's trial-related duties and functions. 1.21", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Direct Access", "definition": "Permission to examine, analyze, verify, and reproduce any records and reports that are important to evaluation of a clinical trial. Any party (e.g., domestic and foreign regulatory authorities, sponsor's monitors and auditors) with direct access should take all reasonable precautions within the constraints of the applicable regulatory requirement(s) to maintain the confidentiality of subjects' identities and sponsor’s proprietary information. 1.22", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Documentation", "definition": "All records, in any form (including, but not limited to, written, electronic, magnetic, and optical records, and scans, x-rays, and electrocardiograms) that describe or record the methods, conduct, and/or results of a trial, the factors affecting a trial, and the actions taken. 1.23", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Essential Documents", "definition": "Documents which individually and collectively permit evaluation of the conduct of a study and the quality of the data produced (see 8. Essential Documents for the Conduct of a Clinical Trial). 1.24 Good Clinical Practice (GCP) A standard for the design, conduct, performance, monitoring, auditing, recording, analyses, and reporting of clinical trials that provides assurance that the data and reported results are credible and accurate, and that the rights, integrity, and confidentiality of trial subjects are protected. 1.25 Independent Data-Monitoring Committee (IDMC) (Data and Safety Monitoring Board, Monitoring Committee, Data Monitoring Committee) An independent data-monitoring committee that may be established by the sponsor to assess at intervals the progress of a clinical trial, the safety data, and the critical efficacy endpoints, and to recommend to the sponsor whether to continue, modify, or stop a trial. 1.26", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Impartial Witness", "definition": "A person, who is independent of the trial, who cannot be unfairly influenced by people involved with the trial, who attends the informed consent process if the subject or the subject’s legally acceptable representative cannot read, and who reads the informed consent form and any other written information supplied to the subject. 1.27 Independent Ethics Committee (IEC) An independent body (a review board or a committee, institutional, regional, national, or supranational), constituted of medical professionals and non-medical members, whose responsibility it is to ensure the protection of the rights, safety and well-being of human subjects involved in a trial and to provide public assurance of that protection, by, among other things, reviewing and approving / providing favourable 4 Guideline for Good Clinical Practice opinion on, the trial protocol, the suitability of the investigator(s), facilities, and the methods and material to be used in obtaining and documenting informed consent of the trial subjects. The legal status, composition, function, operations and regulatory requirements pertaining to Independent Ethics Committees may differ among countries, but should allow the Independent Ethics Committee to act in agreement with GCP as described in this guideline. 1.28", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Informed Consent", "definition": "A process by which a subject voluntarily confirms his or her willingness to participate in a particular trial, after having been informed of all aspects of the trial that are relevant to the subject's decision to participate. Informed consent is documented by means of a written, signed and dated informed consent form. 1.29", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Inspection", "definition": "The act by a regulatory authority(ies) of conducting an official review of documents, facilities, records, and any other resources that are deemed by the authority(ies) to be related to the clinical trial and that may be located at the site of the trial, at the sponsor's and/or contract research organization’s (CRO’s) facilities, or at other establishments deemed appropriate by the regulatory authority(ies). 1.30 Institution (medical) Any public or private entity or agency or medical or dental facility where clinical trials are conducted. 1.31 Institutional Review Board (IRB) An independent body constituted of medical, scientific, and non-scientific members, whose responsibility is to ensure the protection of the rights, safety and well-being of human subjects involved in a trial by, among other things, reviewing, approving, and providing continuing review of trial protocol and amendments and of the methods and material to be used in obtaining and documenting informed consent of the trial subjects. 1.32 Interim Clinical Trial/Study Report A report of intermediate results and their evaluation based on analyses performed during the course of a trial. 1.33 Investigational Product A pharmaceutical form of an active ingredient or placebo being tested or used as a reference in a clinical trial, including a product with a marketing authorization when used or assembled (formulated or packaged) in a way different from the approved form, or when used for an unapproved indication, or when used to gain further information about an approved use. 1.34", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "INVESTIGATOR", "definition": "4.1 Investigator's Qualifications and Agreements 4.1.1 The investigator(s) should be qualified by education, training, and experience to assume responsibility for the proper conduct of the trial, should meet all the qualifications specified by the applicable regulatory requirement(s), and should provide evidence of such qualifications through up-to-date curriculum vitae and/or other relevant documentation requested by the sponsor, the IRB/IEC, and/or the regulatory authority(ies). 4.1.2 The investigator should be thoroughly familiar with the appropriate use of the investigational product(s), as described in the protocol, in the current Investigator's Brochure, in the product information and in other information sources provided by the sponsor. 4.1.3 The investigator should be aware of, and should comply with, GCP and the applicable regulatory requirements. 4.1.4 The investigator/institution should permit monitoring and auditing by the sponsor, and inspection by the appropriate regulatory authority(ies). 4.1.5 The investigator should maintain a list of appropriately qualified persons to whom the investigator has delegated significant trial-related duties. 4.2", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Monitoring", "definition": "5.18.1 Purpose The purposes of trial monitoring are to verify that: (a) The rights and well-being of human subjects are protected. (b) The reported trial data are accurate, complete, and verifiable from source documents. (c) The conduct of the trial is in compliance with the currently approved protocol/amendment(s), with GCP, and with the applicable regulatory requirement(s). 5.18.2 Selection and Qualifications of Monitors (a) Monitors should be appointed by the sponsor. (b) Monitors should be appropriately trained, and should have the scientific and/or clinical knowledge needed to monitor the trial adequately. A monitor’s qualifications should be documented. (c) Monitors should be thoroughly familiar with the investigational product(s), the protocol, written informed consent form and any other written information to be provided to subjects, the sponsor’s SOPs, GCP, and the applicable regulatory requirement(s). 5.18.3 Extent and Nature of Monitoring The sponsor should ensure that the trials are adequately monitored. The sponsor should determine the appropriate extent and nature of monitoring. The determination of the extent and nature of monitoring should be based on considerations such as the objective, purpose, design, complexity, blinding, size, and endpoints of the trial. In general there is a need for on-site monitoring, before, during, and after the trial; however in exceptional circumstances the sponsor may determine that central monitoring in conjunction with procedures such as investigators’ training and meetings, and extensive written guidance can assure appropriate conduct of the trial in accordance with GCP. Statistically controlled sampling may be an acceptable method for selecting the data to be verified. 5.18.4 Monitor's Responsibilities 26 Guideline for Good Clinical Practice The monitor(s) in accordance with the sponsor’s requirements should ensure that the trial is conducted and documented properly by carrying out the following activities when relevant and necessary to the trial and the trial site: (a) Acting as the main line of communication between the sponsor and the investigator. (b) Verifying that the investigator has adequate qualifications and resources (see 4.1, 4.2, 5.6) and remain adequate throughout the trial period, that facilities, including laboratories, equipment, and staff, are adequate to safely and properly conduct the trial and remain adequate throughout the trial period. (c) Verifying, for the investigational product(s): (i) That storage times and conditions are acceptable, and that supplies are sufficient throughout the trial. (ii) That the investigational product(s) are supplied only to subjects who are eligible to receive it and at the protocol specified dose(s). (iii) That subjects are provided with necessary instruction on properly using, handling, storing,", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Monitoring Report", "definition": "A written report from the monitor to the sponsor after each site visit and/or other trial-related communication according to the sponsor’s SOPs. 1.40", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Multicentre Trial", "definition": "A clinical trial conducted according to a single protocol but at more than one site, and therefore, carried out by more than one investigator. 1.41", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Nonclinical Study", "definition": "Biomedical studies not performed on human subjects. 1.42 Opinion (in relation to Independent Ethics Committee) The judgement and/or the advice provided by an Independent Ethics Committee (IEC). 1.43 Original Medical Record See Source Documents. 1.44", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Protocol", "definition": "A document that describes the objective(s), design, methodology, statistical considerations, and organization of a trial. The protocol usually also gives the background and rationale for the trial, but these could be provided in other protocol referenced documents. Throughout the ICH GCP Guideline the term protocol refers to protocol and protocol amendments. 1.45", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Protocol Amendment", "definition": "A written description of a change(s) to or formal clarification of a protocol. 1.46 Quality Assurance (QA) All those planned and systematic actions that are established to ensure that the trial is performed and the data are generated, documented (recorded), and reported in compliance with Good Clinical Practice (GCP) and the applicable regulatory requirement(s). 6 Guideline for Good Clinical Practice 1.47 Quality Control (QC) The operational techniques and activities undertaken within the quality assurance system to verify that the requirements for quality of the trial-related activities have been fulfilled. 1.48", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Randomization", "definition": "The process of assigning trial subjects to treatment or control groups using an element of chance to determine the assignments in order to reduce bias. 1.49 Regulatory Authorities Bodies having the power to regulate. In the ICH GCP guideline the expression Regulatory Authorities includes the authorities that review submitted clinical data and those that conduct inspections (see 1.29). These bodies are sometimes referred to as competent authorities. 1.50 Serious Adverse Event (SAE) or Serious Adverse Drug Reaction (Serious ADR) Any untoward medical occurrence that at any dose: - results in death, - is life-threatening, - requires inpatient hospitalization or prolongation of existing hospitalization, - results in persistent or significant disability/incapacity,", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "or", "definition": "corrections made to CRF after initial data were", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Source Data", "definition": "All information in original records and certified copies of original records of clinical findings, observations, or other activities in a clinical trial necessary for the reconstruction and evaluation of the trial. Source data are contained in source documents (original records or certified copies). 1.52", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Source Documents", "definition": "Original documents, data, and records (e.g., hospital records, clinical and office charts, laboratory notes, memoranda, subjects' diaries or evaluation checklists, pharmacy dispensing records, recorded data from automated instruments, copies or transcriptions certified after verification as being accurate copies, microfiches, photographic negatives, microfilm or magnetic media, x-rays, subject files, and records kept at the pharmacy, at the laboratories and at medico-technical departments involved in the clinical trial). 1.53", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Sponsor", "definition": "8.4.7 FINAL REPORT BY INVESTIGATOR TO IRB/IEC WHERE REQUIRED, AND WHERE APPLICABLE, TO THE REGULATORY AUTHORITY(IES) To document completion of the trial", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Sponsor-Investigator", "definition": "An individual who both initiates and conducts, alone or with others, a clinical trial, and under whose immediate direction the investigational product is administered to, dispensed to, or used by a subject. The term does not include any person other than an individual (e.g., it does not include a corporation or an agency). The obligations of a sponsor-investigator include both those of a sponsor and those of an investigator. 1.55 Standard Operating Procedures (SOPs) Detailed, written instructions to achieve uniformity of the performance of a specific function. 1.56", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Subinvestigator", "definition": "Any individual member of the clinical trial team designated and supervised by the investigator at a trial site to perform critical trial-related procedures and/or to make important trial-related decisions (e.g., associates, residents, research fellows). See also Investigator. 1.57 Subject/Trial Subject An individual who participates in a clinical trial, either as a recipient of the investigational product(s) or as a control. 1.58 Subject Identification Code A unique identifier assigned by the investigator to each trial subject to protect the subject's identity and used in lieu of the subject's name when the investigator reports adverse events and/or other trial related data. 1.59", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Trial Site", "definition": "The location(s) where trial-related activities are actually conducted. 1.60 Unexpected Adverse Drug Reaction An adverse reaction, the nature or severity of which is not consistent with the applicable product information (e.g., Investigator's Brochure for an unapproved investigational product or package insert/summary of product characteristics for an approved product) (see the ICH Guideline for Clinical Safety Data Management: Definitions and Standards for Expedited Reporting). 1.61", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Vulnerable Subjects", "definition": "Individuals whose willingness to volunteer in a clinical trial may be unduly influenced by the expectation, whether justified or not, of benefits associated with participation, or of a retaliatory response from senior members of a hierarchy in case of refusal to participate. Examples are members of a group with a hierarchical structure, such as medical, pharmacy, dental, and nursing students, subordinate hospital and laboratory personnel, employees of the pharmaceutical industry, members of the armed forces, and persons kept in detention. Other vulnerable subjects include patients with incurable diseases, persons in nursing homes, unemployed or impoverished persons, patients in emergency situations, ethnic minority groups, homeless persons, nomads, refugees, minors, and those incapable of giving consent. 1.62 Well-being (of the trial subjects) The physical and mental integrity of the subjects participating in a clinical trial. 2. THE PRINCIPLES OF ICH GCP 8 Guideline for Good Clinical Practice 2.1 Clinical trials should be conducted in accordance with the ethical principles that have their origin in the Declaration of Helsinki, and that are consistent with GCP and the applicable regulatory requirement(s). 2.2 Before a trial is initiated, foreseeable risks and inconveniences should be weighed against the anticipated benefit for the individual trial subject and society. A trial should be initiated and continued only if the anticipated benefits justify the risks. 2.3 The rights, safety, and well-being of the trial subjects are the most important considerations and should prevail over interests of science and society. 2.4 The available nonclinical and clinical information on an investigational product should be adequate to support the proposed clinical trial. 2.5 Clinical trials should be scientifically sound, and described in a clear, detailed protocol. 2.6 A trial should be conducted in compliance with the protocol that has received prior institutional review board (IRB)/independent ethics committee (IEC) approval/favourable opinion. 2.7 The medical care given to, and medical decisions made on behalf of, subjects should always be the responsibility of a qualified physician or, when appropriate, of a qualified dentist. 2.8 Each individual involved in conducting a trial should be qualified by education, training, and experience to perform his or her respective task(s). 2.9 Freely given informed consent should be obtained from every subject prior to clinical trial participation. 2.10 All clinical trial information should be recorded, handled, and stored in a way that allows its accurate reporting, interpretation and verification. 2.11 The confidentiality of records that could identify subjects should be protected, respecting the privacy and confidentiality rules in accordance with the applicable regulatory requirement(s). 2.12 Investigational products should be manufactured, handled, and stored in accordance with applicable good manufacturing practice (GMP). They should be used in accordance with the approved protocol. 2.13 Systems with procedures that assure the quality of every aspect of the trial should be implemented. 3.", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Responsibilities", "definition": "3.1.1 An IRB/IEC should safeguard the rights, safety, and well-being of all trial subjects. Special attention should be paid to trials that may include vulnerable subjects. 9 Guideline for Good Clinical Practice 3.1.2 The IRB/IEC should obtain the following documents: trial protocol(s)/amendment(s), written informed consent form(s) and consent form updates that the investigator proposes for use in the trial, subject recruitment procedures (e.g. advertisements), written information to be provided to subjects, Investigator's Brochure (IB), available safety information, information about payments and compensation available to subjects, the investigator’s current curriculum vitae and/or other documentation evidencing qualifications, and any other documents that the IRB/IEC may need to fulfil its responsibilities. The IRB/IEC should review a proposed clinical trial within a reasonable time and document its views in writing, clearly identifying the trial, the documents reviewed and the dates for the following: - approval/favourable opinion; - modifications required prior to its approval/favourable opinion; - disapproval / negative opinion; and - termination/suspension of any prior approval/favourable opinion. 3.1.3 The IRB/IEC should consider the qualifications of the investigator for the proposed trial, as documented by a current curriculum vitae and/or by any other relevant documentation the IRB/IEC requests. 3.1.4 The IRB/IEC should conduct continuing review of each ongoing trial at intervals appropriate to the degree of risk to human subjects, but at least once per year. 3.1.5 The IRB/IEC may request more information than is outlined in paragraph 4.8.10 be given to subjects when, in the judgement of the IRB/IEC, the additional information would add meaningfully to the protection of the rights, safety and/or well-being of the subjects. 3.1.6 When a non-therapeutic trial is to be carried out with the consent of the subject’s legally acceptable representative (see 4.8.12, 4.8.14), the IRB/IEC should determine that the proposed protocol and/or other document(s) adequately addresses relevant ethical concerns and meets applicable regulatory requirements for such trials. 3.1.7 Where the protocol indicates that prior consent of the trial subject or the subject’s legally acceptable representative is not possible (see 4.8.15), the IRB/IEC should determine that the proposed protocol and/or other document(s) adequately addresses relevant ethical concerns and meets applicable regulatory requirements for such trials (i.e. in emergency situations). 3.1.8 The IRB/IEC should review both the amount and method of payment to subjects to assure that neither presents problems of coercion or undue influence on the trial subjects. Payments to a subject should be prorated and not wholly contingent on completion of the trial by the subject. 3.1.9 The IRB/IEC should ensure that information regarding payment to subjects, including the methods, amounts, and schedule of payment to trial subjects, is set forth in the written informed consent form and any other written information to be provided to subjects. The way payment will be prorated should be specified. 10 Guideline for Good Clinical Practice 3.2 Composition, Functions and Operations 3.2.1 The IRB/IEC should consist of a reasonable number of members, who collectively have the qualifications and experience to review and evaluate the science, medical aspects, and ethics of the proposed trial. It is recommended that the IRB/IEC should include: (a) At least five members. (b) At least one member whose primary area of interest is in a nonscientific area. (c) At least one member who is independent of the institution/trial site. Only those IRB/IEC members who are independent of the investigator and the sponsor of the trial should vote/provide opinion on a trial-related matter. A list of IRB/IEC members and their qualifications should be maintained. 3.2.2 The IRB/IEC should perform its functions according to written operating procedures, should maintain written records of its activities and minutes of its meetings, and should comply with GCP and with the applicable regulatory requirement(s). 3.2.3 An IRB/IEC should make its decisions at announced meetings at which at least a quorum, as stipulated in its written operating procedures, is present. 3.2.4 Only members who participate in the IRB/IEC review and discussion should vote/provide their opinion and/or advise. 3.2.5 The investigator may provide information on any aspect of the trial, but should not participate in the deliberations of the IRB/IEC or in the vote/opinion of the IRB/IEC. 3.2.6 An IRB/IEC may invite nonmembers with expertise in special areas for assistance. 3.3", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Procedures", "definition": "The IRB/IEC should establish, document in writing, and follow its procedures, which should include: 3.3.1 Determining its composition (names and qualifications of the members) and the authority under which it is established. 3.3.2 Scheduling, notifying its members of, and conducting its meetings. 3.3.3 Conducting initial and continuing review of trials. 3.3.4 Determining the frequency of continuing review, as appropriate. 3.3.5 Providing, according to the applicable regulatory requirements, expedited review and approval/favourable opinion of minor change(s) in ongoing trials that have the approval/favourable opinion of the IRB/IEC. 3.3.6 Specifying that no subject should be admitted to a trial before the IRB/IEC issues its written approval/favourable opinion of the trial. 3.3.7 Specifying that no deviations from, or changes of, the protocol should be initiated without prior written IRB/IEC approval/favourable opinion of an appropriate amendment, except when necessary to eliminate immediate hazards to the subjects or when the change(s) involves only logistical or 11 Guideline for Good Clinical Practice administrative aspects of the trial (e.g., change of monitor(s), telephone number(s)) (see 4.5.2). 3.3.8 Specifying that the investigator should promptly report to the IRB/IEC: (a) Deviations from, or changes of, the protocol to eliminate immediate hazards to the trial subjects (see 3.3.7, 4.5.2, 4.5.4). (b) Changes increasing the risk to subjects and/or affecting significantly the conduct of the trial (see 4.10.2). (c) All adverse drug reactions (ADRs) that are both serious and unexpected. (d) New information that may affect adversely the safety of the subjects or the conduct of the trial. 3.3.9", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "the", "definition": "investigator’s trial staff ( may be combined with 8.2.19)", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Records", "definition": "The IRB/IEC should retain all relevant records (e.g., written procedures, membership lists, lists of occupations/affiliations of members, submitted documents, minutes of meetings, and correspondence) for a period of at least 3 years after completion of the trial and make them available upon request from the regulatory authority(ies). The IRB/IEC may be asked by investigators, sponsors or regulatory authorities to provide its written procedures and membership lists. 4.", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Adequate Resources", "definition": "12 Guideline for Good Clinical Practice 4.2.1 The investigator should be able to demonstrate (e.g., based on retrospective data) a potential for recruiting the required number of suitable subjects within the agreed recruitment period. 4.2.2 The investigator should have sufficient time to properly conduct and complete the trial within the agreed trial period. 4.2.3 The investigator should have available an adequate number of qualified staff and adequate facilities for the foreseen duration of the trial to conduct the trial properly and safely. 4.2.4 The investigator should ensure that all persons assisting with the trial are adequately informed about the protocol, the investigational product(s), and their trial-related duties and functions. 4.3 Medical Care of Trial Subjects 4.3.1 A qualified physician (or dentist, when appropriate), who is an investigator or a sub-investigator for the trial, should be responsible for all trial-related medical (or dental) decisions. 4.3.2", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "regulatory", "definition": "authority(ies). 29 Guideline for Good Clinical Practice 5.21 Premature Termination or Suspension of a Trial If a trial is prematurely terminated or suspended, the sponsor should promptly inform the investigators/institutions, and the regulatory authority(ies) of the termination or suspension and the reason(s) for the termination or suspension. The IRB/IEC should also be informed promptly and provided the reason(s) for the termination or suspension by the sponsor or by the investigator/institution, as specified by the applicable regulatory requirement(s). 5.22 Clinical Trial/Study Reports Whether the trial is completed or prematurely terminated, the sponsor should ensure that the clinical trial reports are prepared and provided to the regulatory agency(ies) as required by the applicable regulatory requirement(s). The sponsor should also ensure that the clinical trial reports in marketing applications meet the standards of the ICH Guideline for Structure and Content of Clinical Study Reports. (NOTE: The ICH Guideline for Structure and Content of Clinical Study Reports specifies that abbreviated study reports may be acceptable in certain cases.) 5.23", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Records and Reports", "definition": "4.9.1 The investigator should ensure the accuracy, completeness, legibility, and timeliness of the data reported to the sponsor in the CRFs and in all required reports. 4.9.2 Data reported on the CRF, that are derived from source documents, should be consistent with the source documents or the discrepancies should be explained. 4.9.3 Any change or correction to a CRF should be dated, initialed, and explained (if necessary) and should not obscure the original entry (i.e. an audit trail should be maintained); this applies to both written and electronic changes or corrections (see 5.18.4 (n)). Sponsors should provide guidance to investigators and/or the investigators' designated representatives on making such corrections. Sponsors should have written procedures to assure that changes or corrections in CRFs made by sponsor's designated representatives are documented, are necessary, and are endorsed by the investigator. The investigator should retain records of the changes and corrections. 4.9.4 The investigator/institution should maintain the trial documents as specified in Essential Documents for the Conduct of a Clinical Trial (see 8.) and as", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Progress Reports", "definition": "4.10.1 The investigator should submit written summaries of the trial status to the IRB/IEC annually, or more frequently, if requested by the IRB/IEC. 4.10.2 The investigator should promptly provide written reports to the sponsor, the IRB/IEC (see 3.3.8) and, where applicable, the institution on any changes significantly affecting the conduct of the trial, and/or increasing the risk to subjects. 4.11", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Safety Reporting", "definition": "4.11.1 All serious adverse events (SAEs) should be reported immediately to the sponsor except for those SAEs that the protocol or other document (e.g., Investigator's Brochure) identifies as not needing immediate reporting. The immediate reports should be followed promptly by detailed, written reports. The immediate and follow-up reports should identify subjects by unique code numbers assigned to the trial subjects rather than by the subjects' names, personal identification numbers, and/or addresses. The investigator should also comply with the applicable regulatory requirement(s) related to the reporting of unexpected serious adverse drug reactions to the regulatory authority(ies) and the IRB/IEC. 4.11.2 Adverse events and/or laboratory abnormalities identified in the protocol as critical to safety evaluations should be reported to the sponsor according to the reporting requirements and within the time periods specified by the sponsor in the protocol. 4.11.3 For reported deaths, the investigator should supply the sponsor and the IRB/IEC with any additional requested information (e.g., autopsy reports and terminal medical reports). 4.12 Premature Termination or Suspension of a Trial If the trial is prematurely terminated or suspended for any reason, the investigator/institution should promptly inform the trial subjects, should assure appropriate therapy and follow-up for the subjects, and, where required by the applicable regulatory requirement(s), should inform the regulatory authority(ies). In addition: 4.12.1 If the investigator terminates or suspends a trial without prior agreement of the sponsor, the investigator should inform the institution where applicable, and the investigator/institution should promptly inform the sponsor and the IRB/IEC, and should provide the sponsor and the IRB/IEC a detailed written explanation of the termination or suspension. 19 Guideline for Good Clinical Practice 4.12.2 If the sponsor terminates or suspends a trial (see 5.21), the investigator should", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Medical Expertise", "definition": "The sponsor should designate appropriately qualified medical personnel who will be readily available to advise on trial related medical questions or problems. If necessary, outside consultant(s) may be appointed for this purpose. 5.4", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Trial Design", "definition": "The scientific integrity of the trial and the credibility of the data from the trial depend substantially on the trial design. A description of the trial design, should include: 6.4.1 A specific statement of the primary endpoints and the secondary endpoints, if any, to be measured during the trial. 6.4.2 A description of the type/design of trial to be conducted (e.g. double-blind, placebo-controlled, parallel design) and a schematic diagram of trial design, procedures and stages. 6.4.3 A description of the measures taken to minimize/avoid bias, including: (a) Randomization. (b) Blinding. 6.4.4 A description of the trial treatment(s) and the dosage and dosage regimen of the investigational product(s). Also include a description of the dosage form, packaging, and labelling of the investigational product(s). 6.4.5 The expected duration of subject participation, and a description of the sequence and duration of all trial periods, including follow-up, if any. 6.4.6 A description of the \"stopping rules\" or \"discontinuation criteria\" for individual subjects, parts of trial and entire trial. 31 Guideline for Good Clinical Practice 6.4.7 Accountability procedures for the investigational product(s), including the placebo(s) and comparator(s), if any. 6.4.8 Maintenance of trial treatment randomization codes and procedures for breaking codes. 6.4.9 The identification of any data to be recorded directly on the CRFs (i.e. no prior written or electronic record of data), and to be considered to be source data. 6.5 Selection and Withdrawal of Subjects 6.5.1 Subject inclusion criteria. 6.5.2 Subject exclusion criteria. 6.5.3", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Financing", "definition": "The financial aspects of the trial should be documented in an agreement between the sponsor and the investigator/institution. 5.10 Notification/Submission to Regulatory Authority(ies) Before initiating the clinical trial(s), the sponsor (or the sponsor and the investigator, if required by the applicable regulatory requirement(s)) should submit any required application(s) to the appropriate authority(ies) for review, acceptance, and/or permission (as required by the applicable regulatory requirement(s)) to begin the trial(s). Any notification/submission should be dated and contain sufficient information to identify the protocol. 5.11 Confirmation of Review by IRB/IEC 5.11.1 The sponsor should obtain from the investigator/institution: (a) The name and address of the investigator's/institution’s IRB/IEC. (b) A statement obtained from the IRB/IEC that it is organized and operates according to GCP and the applicable laws and regulations. (c) Documented IRB/IEC approval/favourable opinion and, if requested by the sponsor, a current copy of protocol, written informed consent form(s) and any other written information to be provided to subjects, subject recruiting procedures, and documents related to payments and compensation available to the subjects, and any other documents that the IRB/IEC may have requested. 5.11.2 If the IRB/IEC conditions its approval/favourable opinion upon change(s) in any aspect of the trial, such as modification(s) of the protocol, written informed consent form and any other written information to be provided to subjects, and/or", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "and", "definition": "absorption, plasma protein binding, distribution, and elimination). − Bioavailability of the investigational product (absolute, where possible, and/or relative) using a reference dosage form. − Population subgroups (e.g., gender, age, and impaired organ function). − Interactions (e.g., product-product interactions and effects of food). − Other pharmacokinetic data (e.g., results of population studies performed within clinical trial(s). (b) Safety and Efficacy A summary of information should be provided about the investigational product's/products' (including metabolites, where appropriate) safety, pharmacodynamics, efficacy, and dose response that were obtained from preceding trials in humans (healthy volunteers and/or patients). The implications of this information should be discussed. In cases where a number of clinical trials have been completed, the use of summaries of safety and efficacy across multiple trials by indications in subgroups may provide a clear presentation of the data. Tabular summaries of adverse drug reactions for all the clinical trials (including those for all the studied indications) would be useful. Important differences in adverse drug 37 Guideline for Good Clinical Practice reaction patterns/incidences across indications or subgroups should be discussed. The IB should provide a description of the possible risks and adverse drug reactions to be anticipated on the basis of prior experiences with the product under investigation and with related products. A description should also be provided of the precautions or special monitoring to be done as part of the investigational use of the product(s). (c) Marketing Experience The IB should identify countries where the investigational product has been marketed or approved. Any significant information arising from the marketed use should be summarised (e.g., formulations, dosages, routes of administration, and adverse product reactions). The IB should also identify all the countries where the investigational product did not receive approval/registration", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Record Access", "definition": "5.15.1 The sponsor should ensure that it is specified in the protocol or other written agreement that the investigator(s)/institution(s) provide direct access to source data/documents for trial-related monitoring, audits, IRB/IEC review, and regulatory inspection. 5.15.2 The sponsor should verify that each subject has consented, in writing, to direct access to his/her original medical records for trial-related monitoring, audit, IRB/IEC review, and regulatory inspection. 5.16", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Safety Information", "definition": "5.16.1 The sponsor is responsible for the ongoing safety evaluation of the investigational product(s). 5.16.2 The sponsor should promptly notify all concerned investigator(s)/institution(s) and the regulatory authority(ies) of findings that could affect adversely the 25 Guideline for Good Clinical Practice safety of subjects, impact the conduct of the trial, or alter the IRB/IEC's approval/favourable opinion to continue the trial. 5.17 Adverse Drug Reaction Reporting 5.17.1 The", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "concerned", "definition": "investigator(s)/institutions(s), to the IRB(s)/IEC(s), where required, and to the regulatory authority(ies) of all adverse drug reactions (ADRs) that are both serious and unexpected. 5.17.2 Such expedited reports should comply with the applicable regulatory requirement(s) and with the ICH Guideline for Clinical Safety Data Management: Definitions and Standards for Expedited Reporting. 5.17.3 The sponsor should submit to the regulatory authority(ies) all safety updates and periodic reports, as required by applicable regulatory requirement(s). 5.18", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "investigational", "definition": "product(s). (iv) That the receipt, use, and return of the investigational product(s) at the trial sites are controlled and documented adequately. (v) That the disposition of unused investigational product(s) at the trial sites complies with applicable regulatory requirement(s) and is in accordance with the sponsor. (d) Verifying that the investigator follows the approved protocol and all approved amendment(s), if any. (e) Verifying that written informed consent was obtained before each subject's participation in the trial. (f) Ensuring that the investigator receives the current Investigator's Brochure, all documents, and all trial supplies needed to conduct the trial properly and to comply with the applicable regulatory requirement(s). (g) Ensuring that the investigator and the investigator's trial staff are adequately informed about the trial. (h) Verifying that the investigator and the investigator's trial staff are performing the specified trial functions, in accordance with the protocol and any other written agreement between the sponsor and the investigator/institution, and have not delegated these functions to unauthorized individuals. (i) Verifying that the investigator is enroling only eligible subjects. (j) Reporting the subject recruitment rate. (k) Verifying that source documents and other trial records are accurate, complete, kept up-to-date and maintained. (l) Verifying that the investigator provides all the required reports, notifications, applications, and submissions, and that these documents are accurate, complete, timely, legible, dated, and identify the trial. 27 Guideline for Good Clinical Practice (m) Checking the accuracy and completeness of the CRF entries, source documents and other trial-related records against each other. The monitor specifically should verify that: (i) The data required by the protocol are reported accurately on the CRFs and are consistent with the source documents. (ii) Any dose and/or therapy modifications are well documented for each of the trial subjects. (iii) Adverse events, concomitant medications and intercurrent illnesses are reported in accordance with the protocol on the CRFs. (iv) Visits that the subjects fail to make, tests that are not conducted, and examinations that are not performed are clearly reported as such on the CRFs. (v) All withdrawals and dropouts of enrolled subjects from the trial are reported and explained on the CRFs. (n) Informing the investigator of any CRF entry error, omission, or illegibility. The monitor should ensure that appropriate corrections, additions, or deletions are made, dated, explained (if necessary), and initialled by the investigator or by a member of the investigator's trial staff who is authorized to initial CRF changes for the investigator. This authorization should be documented. (o) Determining whether all adverse events (AEs) are appropriately reported within the time periods required by GCP, the protocol, the IRB/IEC, the sponsor, and the applicable regulatory requirement(s). (p) Determining whether the investigator is maintaining the essential documents (see 8. Essential Documents for the Conduct of a Clinical Trial). (q) Communicating deviations from the protocol, SOPs, GCP, and the applicable regulatory requirements to the investigator and taking appropriate action designed to prevent recurrence of the detected deviations. 5.18.5 Monitoring Procedures The monitor(s) should follow the sponsor’s established written SOPs as well as those procedures that are specified by the sponsor for monitoring a specific trial. 5.18.6 Monitoring Report (a) The monitor should submit a written report to the sponsor after each trial- site visit or trial-related communication. (b) Reports should include the date, site, name of the monitor, and name of the investigator or other individual(s) contacted. (c) Reports should include a summary of what the monitor reviewed and the monitor's statements concerning the significant findings/facts, deviations and deficiencies, conclusions, actions taken or to be taken and/or actions recommended to secure compliance. (d) The review and follow-up of the monitoring report with the sponsor should be documented by the sponsor’s designated representative. 5.19", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Noncompliance", "definition": "5.20.1 Noncompliance with the protocol, SOPs, GCP, and/or applicable regulatory requirement(s) by an investigator/institution, or by member(s) of the sponsor's staff should lead to prompt action by the sponsor to secure compliance. 5.20.2 If the monitoring and/or auditing identifies serious and/or persistent noncompliance on the part of an investigator/institution, the sponsor should terminate the investigator's/institution’s participation in the trial. When an investigator's/institution’s", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Multicentre Trials", "definition": "For multicentre trials, the sponsor should ensure that: 5.23.1 All investigators conduct the trial in strict compliance with the protocol agreed to by the sponsor and, if required, by the regulatory authority(ies), and given approval/favourable opinion by the IRB/IEC. 5.23.2 The CRFs are designed to capture the required data at all multicentre trial sites. For those investigators who are collecting additional data, supplemental CRFs should also be provided that are designed to capture the additional data. 5.23.3 The responsibilities of coordinating investigator(s) and the other participating investigators are documented prior to the start of the trial. 5.23.4 All investigators are given instructions on following the protocol, on complying with a uniform set of standards for the assessment of clinical and laboratory findings, and on completing the CRFs. 5.23.5 Communication between investigators is facilitated. 6. CLINICAL TRIAL PROTOCOL AND PROTOCOL AMENDMENT(S) The contents of a trial protocol should generally include the following topics. However, site specific information may be provided on separate protocol page(s), or addressed in a separate agreement, and some of the information listed below may be contained in other protocol referenced documents, such as an Investigator’s Brochure. 6.1", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "General Information", "definition": "6.1.1 Protocol title, protocol identifying number, and date. Any amendment(s) should also bear the amendment number(s) and date(s). 6.1.2 Name and address of the sponsor and monitor (if other than the sponsor). 6.1.3 Name and title of the person(s) authorized to sign the protocol and the protocol amendment(s) for the sponsor. 6.1.4 Name, title, address, and telephone number(s) of the sponsor's medical expert (or dentist when appropriate) for the trial. 30 Guideline for Good Clinical Practice 6.1.5 Name and title of the investigator(s) who is (are) responsible for conducting the trial, and the address and telephone number(s) of the trial site(s). 6.1.6 Name, title, address, and telephone number(s) of the qualified physician (or dentist, if applicable), who is responsible for all trial-site related medical (or dental) decisions (if other than investigator). 6.1.7 Name(s) and address(es) of the clinical laboratory(ies) and other medical and/or technical department(s) and/or institutions involved in the trial. 6.2 Background Information 6.2.1 Name and description of the investigational product(s). 6.2.2 A summary of findings from nonclinical studies that potentially have clinical significance and from clinical trials that are relevant to the trial. 6.2.3 Summary of the known and potential risks and benefits, if any, to human subjects. 6.2.4 Description of and justification for the route of administration, dosage, dosage regimen, and treatment period(s). 6.2.5 A statement that the trial will be conducted in compliance with the protocol, GCP and the applicable regulatory requirement(s). 6.2.6 Description of the population to be studied. 6.2.7 References to literature and data that are relevant to the trial, and that provide background for the trial. 6.3 Trial Objectives and Purpose A detailed description of the objectives and the purpose of the trial. 6.4", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "product", "definition": "(s ) pharmacological class and its expected position within this class (e.g. advantages), the rationale for performing research with the investigational product(s), and the anticipated prophylactic, therapeutic, or diagnostic indication(s). Finally, the introductory statement should provide the general approach to be followed in evaluating the investigational product. 7.3.4 Physical, Chemical, and Pharmaceutical Properties and Formulation A description should be provided of the investigational product substance(s) (including the chemical and/or structural formula(e)), and a brief summary should be given of the relevant physical, chemical, and pharmaceutical properties. To permit appropriate safety measures to be taken in the course of the trial, a description of the formulation(s) to be used, including excipients, should be provided and justified if clinically relevant. Instructions for the storage and handling of the dosage form(s) should also be given. Any structural similarities to other known compounds should be mentioned. 35 Guideline for Good Clinical Practice 7.3.5 Nonclinical Studies Introduction:", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Treatment of Subjects", "definition": "6.6.1 The treatment(s) to be administered, including the name(s) of all the product(s), the dose(s), the dosing schedule(s), the route/mode(s) of administration, and the treatment period(s), including the follow-up period(s) for subjects for each investigational product treatment/trial treatment group/arm of the trial. 6.6.2 Medication(s)/treatment(s) permitted (including rescue medication) and not permitted before and/or during the trial. 6.6.3 Procedures for monitoring subject compliance. 6.7 Assessment of Efficacy 6.7.1 Specification of the efficacy parameters. 6.7.2 Methods and timing for assessing, recording, and analysing of efficacy parameters. 6.8", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Assessment of Safety", "definition": "6.8.1 Specification of safety parameters. 6.8.2 The methods and timing for assessing, recording, and analysing safety parameters. 6.8.3 Procedures for eliciting reports of and for recording and reporting adverse event and intercurrent illnesses. 6.8.4 The type and duration of the follow-up of subjects after adverse events. 6.9", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Statistics", "definition": "32 Guideline for Good Clinical Practice 6.9.1 A description of the statistical methods to be employed, including timing of any planned interim analysis(ses). 6.9.2 The number of subjects planned to be enrolled. In multicentre trials, the numbers of enrolled subjects projected for each trial site should be specified. Reason for choice of sample size, including reflections on (or calculations of) the power of the trial and clinical justification. 6.9.3 The level of significance to be used. 6.9.4 Criteria for the termination of the trial. 6.9.5 Procedure for accounting for missing, unused, and spurious data. 6.9.6 Procedures for reporting any deviation(s) from the original statistical plan (any deviation(s) from the original statistical plan should be described and justified in protocol and/or in the final report, as appropriate). 6.9.7 The selection of subjects to be included in the analyses (e.g. all randomized subjects, all dosed subjects, all eligible subjects, evaluable subjects). 6.10 Direct Access to Source Data/Documents The sponsor should ensure that it is specified in the protocol or other written agreement that the investigator(s)/institution(s) will permit trial-related monitoring, audits, IRB/IEC review, and regulatory inspection(s), providing direct access to source data/documents. 6.11 Quality Control and Quality Assurance 6.12", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Ethics", "definition": "Description of ethical considerations relating to the trial. 6.13 Data Handling and Record Keeping 6.14 Financing and Insurance Financing and insurance if not addressed in a separate agreement. 6.15", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Publication Policy", "definition": "Publication policy, if not addressed in a separate agreement. 6.16", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Supplements", "definition": "(NOTE: Since the protocol and the clinical trial/study report are closely related, further relevant information can be found in the ICH Guideline for Structure and Content of Clinical Study Reports.) 33 Guideline for Good Clinical Practice 7. INVESTIGATOR’S BROCHURE 7.1", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Title Page", "definition": "This should provide the sponsor's name, the identity of each investigational product (i.e., research number, chemical or approved generic name, and trade name(s) where legally permissible and desired by the sponsor), and the release date. It is also suggested that an edition number, and a reference to the number and date of the edition it supersedes, be provided. An example is given in Appendix 1. 7.2.2 Confidentiality Statement", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "Summary", "definition": "A brief summary (preferably not exceeding two pages) should be given,", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "significant", "definition": "physical, chemical, pharmaceutical, pharmacological, toxicological, pharmacokinetic, metabolic, and clinical information available that is relevant to the stage of clinical development of the investigational product. 7.3.3", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "nonclinical", "definition": "pharmacology, toxicology, pharmacokinetic, and investigational product metabolism studies should be provided in summary form. This summary should address the methodology used, the results, and a discussion of the relevance of the findings to the investigated therapeutic and the possible unfavourable and unintended effects in humans. The information provided may include the following, as appropriate, if known/available: • Species tested • Number and sex of animals in each group • Unit dose (e.g., milligram/kilogram (mg/kg)) • Dose interval • Route of administration • Duration of dosing • Information on systemic distribution • Duration of post-exposure follow-up • Results, including the following aspects: − Nature and frequency of pharmacological or toxic effects − Severity or intensity of pharmacological or toxic effects − Time to onset of effects − Reversibility of effects − Duration of effects − Dose response Tabular format/listings should be used whenever possible to enhance the clarity of the presentation. The following sections should discuss the most important findings from the studies, including the dose response of observed effects, the relevance to humans, and any aspects to be studied in humans. If applicable, the effective and nontoxic dose findings in the same animal species should be compared (i.e., the therapeutic index should be discussed). The relevance of this information to the proposed human dosing should be addressed. Whenever possible, comparisons should be made in terms of blood/tissue levels rather than on a mg/kg basis. (a) Nonclinical Pharmacology A summary of the pharmacological aspects of the investigational product and, where appropriate, its significant metabolites studied in animals, should be included. Such a summary should incorporate studies that assess potential therapeutic activity (e.g. efficacy models, receptor binding, and specificity) as well as those that assess safety (e.g., special studies to assess pharmacological actions other than the intended therapeutic effect(s)). 36 Guideline for Good Clinical Practice (b) Pharmacokinetics and Product Metabolism in Animals A summary of the pharmacokinetics and biological transformation and disposition of the investigational product in all species studied should be given. The discussion of the findings should address the absorption and the local and systemic bioavailability of the investigational product and its metabolites,", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "from", "definition": "marketing/registration. 7.3.7 Summary of Data and Guidance for the Investigator This section should provide an overall discussion of the nonclinical and clinical data, and should summarise the information from various sources on different aspects of the investigational product(s), wherever possible. In this way, the investigator can be provided with the most informative interpretation of the available data and with an assessment of the implications of the information for future clinical trials. Where appropriate, the published reports on related products should be discussed. This could help the investigator to anticipate adverse drug reactions or other problems in clinical trials. The overall aim of this section is to provide the investigator with a clear understanding of the possible risks and adverse reactions, and of the specific tests, observations, and precautions that may be needed for a clinical trial. This understanding should be based on the available physical, chemical, pharmaceutical, pharmacological, toxicological, and clinical information on the investigational product(s). Guidance should also be provided to the clinical investigator on the recognition and treatment of possible overdose and adverse drug reactions that is based on previous human experience and on the pharmacology of the investigational product. 38 Guideline for Good Clinical Practice 7.4 APPENDIX 1: TITLE PAGE (Example) SPONSOR'S NAME Product: Research Number: Name(s): Chemical, Generic (if approved) Trade Name(s) (if legally permissible and desired by the sponsor) INVESTIGATOR'S BROCHURE Edition Number: Release Date: Replaces Previous Edition Number: Date: 39 Guideline for Good Clinical Practice 40 7.5 APPENDIX 2: TABLE OF CONTENTS OF INVESTIGATOR'S BROCHURE (Example) - Confidentiality Statement (optional) ........................................................................... - Signature Page (optional) ............................................................................................. 1 Table of Contents ......................................................................................................... 2 Summary ...................................................................................................................... 3 Introduction .................................................................................................................. 4 Physical, Chemical, and Pharmaceutical Properties and Formulation .................... 5 Nonclinical Studies ...................................................................................................... 5.1 Nonclinical Pharmacology ........................................................................................... 5.2 Pharmacokinetics and Product Metabolism in Animals ............................................ 5.3 Toxicology ..................................................................................................................... 6 Effects in Humans ........................................................................................................ 6.1 Pharmacokinetics and Product Metabolism in Humans ............................................ 6.2 Safety and Efficacy ....................................................................................................... 6.3 Marketing Experience .................................................................................................. 7 Summary of Data and Guidance for the Investigator ................................................ NB: References on 1. Publications 2. Reports These references should be found at the end of each chapter Appendices (if any) Guideline for Good Clinical Practice 8. ESSENTIAL DOCUMENTS FOR THE CONDUCT OF A CLINICAL TRIAL 8.1", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.2.2", "definition": "SIGNED PROTOCOL AND AMENDMENTS, IF ANY, AND SAMPLE CASE REPORT FORM (CRF)", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "SUBJECT", "definition": "- INFORMED CONSENT FORM (including all applicable translations) To document the informed consent", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "- ANY OTHER WRITTEN INFORMATION", "definition": "To document that subjects will be given appropriate written information (content and wording) to support their ability to give fully", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "- ADVERTISEMENT FOR SUBJECT", "definition": "RECRUITMENT (if used) To document that recruitment measures are appropriate and not coercive", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.2.4", "definition": "FINANCIAL ASPECTS OF THE TRIAL To document the financial agreement between the investigator/institution and the sponsor for", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "INSURANCE STATEMENT", "definition": "(where required) To document that compensation to subject(s) for trial-related injury will be available", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.2.6", "definition": "SIGNED AGREEMENT BETWEEN INVOLVED PARTIES, e.g.: - investigator/institution and sponsor - investigator/institution and CRO -", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "sponsor and CRO", "definition": "- investigator/institution and authority(ies) (where required) To document agreements", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.2.7", "definition": "DATED, DOCUMENTED APPROVAL/FAVOURABLE OPINION OF INSTITUTIONAL REVIEW BOARD (IRB) /INDEPENDENT ETHICS COMMITTEE (IEC) OF THE FOLLOWING: - protocol and any amendments - CRF (if applicable) - informed consent form(s) - any other written information to be provided to the subject(s) - advertisement for subject recruitment (if used) - subject compensation (if any) - any other documents given approval/", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "favourable opinion", "definition": "To document that the trial has been subject to IRB/IEC review and given approval/favourable opinion. To identify the version number and date of the document(s)", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "COMMITTEE COMPOSITION", "definition": "To document that the IRB/IEC is constituted in", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "(where", "definition": "required) 8.3.21 SUBJECT IDENTIFICATION CODE LIST To document that investigator/institution keeps a confidential list of names of all subjects allocated to trial numbers on enrolling in the trial. Allows investigator/institution to reveal identity of any subject", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.2.11 NORMAL VALUE(S)/RANGE(S) FOR", "definition": "MEDICAL/ LABORATORY/TECHNICAL PROCEDURE(S) AND/OR TEST(S) INCLUDED IN THE PROTOCOL To document normal values and/or ranges of the", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "accreditation or", "definition": "- established quality control and/or external", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "quality assessment or", "definition": "- other validation (where required) To document that tests remain adequate throughout the trial period (see 8.2.12)", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "applicable", "definition": "labelling regulations and appropriateness of instructions provided to the subjects", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.2.14 INSTRUCTIONS FOR HANDLING OF", "definition": "INVESTIGATIONAL PRODUCT(S) AND TRIAL-RELATED MATERIALS (if not included in protocol or Investigator’s Brochure) To document instructions needed to ensure proper storage, packaging, dispensing and disposition of investigational products and trial-", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.2.15 SHIPPING RECORDS FOR", "definition": "INVESTIGATIONAL PRODUCT(S) AND TRIAL-RELATED MATERIALS To document shipment dates, batch numbers and method of shipment of investigational product(s) and trial-related materials. Allows tracking of product batch, review of shipping conditions, and accountability", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "SHIPPED", "definition": "To document identity, purity, and strength of investigational product(s) to be used in the trial", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "TRIALS", "definition": "To document how, in case of an emergency, identity of blinded investigational product can be revealed without breaking the blind for the remaining subjects' treatment", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "(third party if", "definition": "applicable) 8.2.19 PRE-TRIAL MONITORING REPORT To document that the site is suitable for the trial (may be combined with 8.2.20)", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.3", "definition": "During the Clinical Conduct of the Trial In addition to having on file the above documents, the following should be added to the files during the trial as evidence that all new relevant information is documented as it becomes available 8.3.1 INVESTIGATOR’S BROCHURE UPDATES To document that investigator is informed in a timely manner of relevant information as it", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "informed consent form", "definition": "- any other written information provided to", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "subjects", "definition": "- advertisement for subject recruitment (if used) To document revisions of these trial related documents that take effect during trial", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.3.3", "definition": "DATED, DOCUMENTED APPROVAL/FAVOURABLE OPINION OF INSTITUTIONAL REVIEW BOARD (IRB) /INDEPENDENT ETHICS COMMITTEE (IEC) OF THE FOLLOWING: - protocol amendment(s) - revision(s) of: - informed consent form - any other written information to be provided to the subject - advertisement for subject recruitment (if used) - any other documents given approval/favourable opinion - continuing review of trial (where required) To document that the amendment(s) and/or revision(s) have been subject to IRB/IEC review and were given approval/favourable opinion. To identify the version number and date of the document(s).", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "documents", "definition": "To document compliance with applicable regulatory requirements", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.3.5", "definition": "CURRICULUM VITAE FOR NEW INVESTIGATOR(S) AND/OR SUB- INVESTIGATOR(S) (see 8.2.10)", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "UPDATES TO NORMAL", "definition": "VALUE(S)/RANGE(S) FOR MEDICAL/ LABORATORY/ TECHNICAL PROCEDURE(S)/TEST(S) INCLUDED IN", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "THE PROTOCOL", "definition": "To document normal values and ranges that are revised during the trial (see 8.2.11)", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.3.7", "definition": "UPDATES OF MEDICAL/LABORATORY/ TECHNICAL PROCEDURES/TESTS -", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "DOCUMENTATION OF", "definition": "INVESTIGATIONAL PRODUCT(S) AND TRIAL-RELATED MATERIALS SHIPMENT (see 8.2.15.)", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.3.10 MONITORING VISIT REPORTS", "definition": "To document site visits by, and findings of, the", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "meeting notes", "definition": "- notes of telephone calls To document any agreements or significant", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "trial", "definition": "administration, protocol violations, trial conduct, adverse event (AE) reporting", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.3.12 SIGNED INFORMED CONSENT FORMS", "definition": "To document that consent is obtained in accordance with GCP and protocol and dated prior to participation of each subject in trial. Also to document direct access permission (see 8.2.3)", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.3.13 SOURCE DOCUMENTS", "definition": "To document the existence of the subject and substantiate integrity of trial data collected. To include original documents related to the trial, to medical treatment, and history of subject", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "(original)", "definition": "8.3.16 NOTIFICATION BY ORIGINATING INVESTIGATOR TO SPONSOR OF SERIOUS ADVERSE EVENTS AND", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "to", "definition": "sponsor of serious adverse events and related reports in accordance with 4.11", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.3.17 NOTIFICATION BY SPONSOR AND/OR", "definition": "INVESTIGATOR, WHERE APPLICABLE, TO REGULATORY AUTHORITY(IES) AND IRB(S)/IEC(S) OF UNEXPECTED SERIOUS ADVERSE DRUG REACTIONS AND OF OTHER SAFETY INFORMATION Notification by sponsor and/or investigator, where applicable, to regulatory authorities and IRB(s)/IEC(s) of unexpected serious adverse drug reactions in accordance with 5.17 and 4.11.1 and of other safety information in accordance with 5.16.2 and 4.11.2", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "INFORMATION", "definition": "Notification by sponsor to investigators of safety information in accordance with 5.16.2", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.3.19 INTERIM OR ANNUAL REPORTS TO", "definition": "IRB/IEC AND AUTHORITY(IES) Interim or annual reports provided to IRB/IEC in accordance with 4.10 and to authority(ies) in accordance with 5.17.3", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.3.23 INVESTIGATIONAL PRODUCTS", "definition": "ACCOUNTABILITY AT THE SITE To document that investigational product(s) have been used according to the protocol", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.3.24 SIGNATURE SHEET", "definition": "To document signatures and initials of all persons authorised to make entries and/or", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.3.25 RECORD OF RETAINED BODY FLUIDS/", "definition": "TISSUE SAMPLES (IF ANY) To document location and identification of retained samples if assays need to be repeated", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "of", "definition": "investigational product(s) received at the site, dispensed to subjects, returned by the subjects, and returned to sponsor", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "unused", "definition": "investigational products by sponsor or at site", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "CODE LIST", "definition": "To permit identification of all subjects enrolled in the trial in case follow-up is required. List should be kept in a confidential manner and for", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.4.4", "definition": "AUDIT CERTIFICATE (if available) To document that audit was performed", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "REPORT", "definition": "To document that all activities required for trial close-out are completed, and copies of essential documents are held in the appropriate files", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "8.4.6", "definition": "TREATMENT ALLOCATION AND DECODING DOCUMENTATION Returned to sponsor to document any decoding that may have occurred", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "CLINICAL STUDY REPORT", "definition": "To document results and interpretation of trial", "sources": [ "ICH_E6.pdf" ], "file": "ICH_E6.pdf", "type": "pdf" }, { "term": "INTERNATIONAL CONFERENCE ON HARMONISATION OF TECHNICAL", "definition": "REQUIREMENTS FOR REGISTRATION OF PHARMACEUTICALS FOR HUMAN", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "USE", "definition": "ICH HARMONISED TRIPARTITE GUIDELINE STATISTICAL PRINCIPLES FOR CLINICAL TRIALS", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "dated 5 February 1998", "definition": "This Guideline has been developed by the appropriate ICH Expert Working Group and has been subject to consultation by the regulatory parties, in accordance with the ICH Process. At Step 4 of the Process the final draft is recommended for adoption to the regulatory bodies of the European Union, Japan and USA.", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "E9", "definition": "STATISTICAL PRINCIPLES FOR CLINICAL TRIALS ICH Harmonised Tripartite Guideline Having reached Step 4 of the ICH Process at the ICH Steering Committee meeting on 5 February 1998, this guideline is recommended for adoption to the three regulatory parties to ICH", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "I.", "definition": "INTRODUCTION...............................................................................................1 1.1 Background and Purpose .....................................................................................1 1.2 Scope and Direction..............................................................................................2 II. CONSIDERATIONS FOR OVERALL CLINICAL DEVELOPMENT........3 2.1 Trial Context.........................................................................................................3 2.1.1 Development Plan...................................................................................3 2.1.2 Confirmatory Trial..................................................................................4 2.1.3 Exploratory Trial ....................................................................................4 2.2 Scope of Trials.......................................................................................................4 2.2.1 Population ...............................................................................................4 2.2.2 Primary and Secondary Variables .........................................................5 2.2.3 Composite Variables...............................................................................6 2.2.4 Global Assessment Variables.................................................................6 2.2.5 Multiple Primary Variables ...................................................................7 2.2.6 Surrogate Variables................................................................................7 2.2.7 Categorised Variables............................................................................7 2.3 Design Techniques to Avoid Bias ........................................................................8 2.3.1 Blinding...................................................................................................8 2.3.2 Randomisation ........................................................................................9 III. TRIAL DESIGN CONSIDERATIONS ..........................................................11 3.1 Design Configuration .........................................................................................11 3.1.1 Parallel Group Design ..........................................................................11 3.1.2 Crossover Design ..................................................................................11 3.1.3 Factorial Designs..................................................................................12 3.2 Multicentre Trials ..............................................................................................12 3.3 Type of Comparison............................................................................................14 3.3.1 Trials to Show Superiority ...................................................................14 3.3.2 Trials to Show Equivalence or Non-inferiority ...................................14 3.3.3 Trials to Show Dose-response Relationship ........................................16", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Statistical Principles for Clinical Trials", "definition": "3.4 Group Sequential Designs ................................................................................. 16 3.5 Sample Size ........................................................................................................ 16 3.6 Data Capture and Processing............................................................................ 18 IV. TRIAL CONDUCT CONSIDERATIONS ..................................................... 18 4.1 Trial Monitoring and Interim Analysis ............................................................ 18 4.2 Changes in Inclusion and Exclusion Criteria................................................... 19 4.3 Accrual Rates ..................................................................................................... 19 4.4 Sample Size Adjustment.................................................................................... 19 4.5 Interim Analysis and Early Stopping ............................................................... 19 4.6 Role of Independent Data Monitoring Committee (IDMC) ............................. 21 V. DATA ANALYSIS CONSIDERATIONS....................................................... 21 5.1 Prespecification of the Analysis ........................................................................ 21 5.2 Analysis Sets ...................................................................................................... 22 5.2.1 Full Analysis Set .................................................................................. 22 5.2.2 Per Protocol Set .................................................................................... 23 5.2.3 Roles of the Different Analysis Sets.................................................... 24 5.3 Missing Values and Outliers ............................................................................. 24 5.4 Data Transformation ......................................................................................... 25 5.5 Estimation, Confidence Intervals and Hypothesis Testing ............................. 25 5.6 Adjustment of Significance and Confidence Levels.......................................... 26 5.7 Subgroups, Interactions and Covariates........................................................... 26 5.8 Integrity of Data and Computer Software Validity.......................................... 27 VI. EVALUATION OF SAFETY AND TOLERABILITY.................................. 27 6.1 Scope of Evaluation............................................................................................ 27 6.2 Choice of Variables and Data Collection........................................................... 27 6.3 Set of Subjects to be Evaluated and Presentation of Data .............................. 28 6.4 Statistical Evaluation ........................................................................................ 29 6.5 Integrated Summary.......................................................................................... 29 VII. REPORTING .................................................................................................... 29 7.1 Evaluation and Reporting ................................................................................. 29 7.2 Summarising the Clinical Database ................................................................. 31 7.2.1 Efficacy Data ........................................................................................ 31 7.2.2 Safety Data .......................................................................................... 32 GLOSSARY ................................................................................................................. 32", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "ii", "definition": "STATISTICAL PRINCIPLES FOR CLINICAL TRIALS I.", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "INTRODUCTION", "definition": "1.1 Background and Purpose The efficacy and safety of medicinal products should be demonstrated by clinical trials which follow the guidance in 'Good Clinical Practice: Consolidated Guideline' (ICH E6) adopted by the ICH, 1 May 1996. The role of statistics in clinical trial design and analysis is acknowledged as essential in that ICH guideline. The proliferation of statistical research in the area of clinical trials coupled with the critical role of clinical research in the drug approval process and health care in general necessitate a succinct document on statistical issues related to clinical trials. This guidance is written primarily to attempt to harmonise the principles of statistical methodology applied to clinical trials for marketing applications submitted in Europe, Japan and the United States. As a starting point, this guideline utilised the CPMP (Committee for Proprietary Medicinal Products) Note for Guidance entitled 'Biostatistical Methodology in Clinical Trials in Applications for Marketing Authorisations for Medicinal Products' (December, 1994). It was also influenced by 'Guidelines on the Statistical Analysis of Clinical Studies' (March, 1992) from the Japanese Ministry of Health and Welfare and the U.S. Food and Drug Administration document entitled 'Guideline for the Format and Content of the Clinical and Statistical Sections of a New Drug Application' (July, 1988). Some topics related to statistical principles and methodology are also embedded within other ICH guidelines, particularly those listed below. The specific guidance that contains related text will be identified in various sections of this document. E1A: The Extent of Population Exposure to Assess Clinical Safety E2A: Clinical Safety Data Management: Definitions and Standards for", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Expedited Reporting", "definition": "E2B: Clinical Safety Data Management: Data Elements for Transmission of Individual Case Safety Reports E2C: Clinical Safety Data Management: Periodic Safety Update Reports for", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Marketed Drugs", "definition": "E3: Structure and Content of Clinical Study Reports E4: Dose-Response Information to Support Drug Registration E5: Ethnic Factors in the Acceptability of Foreign Clinical Data E6: Good Clinical Practice: Consolidated Guideline E7: Studies in Support of Special Populations: Geriatrics E8: General Considerations for Clinical Trials E10: Choice of Control Group in Clinical Trials M1: Standardisation of Medical Terminology for Regulatory Purposes M3: Non-Clinical Safety Studies for the Conduct of Human Clinical Trials for Pharmaceuticals. 1 Statistical Principles for Clinical Trials This guidance is intended to give direction to sponsors in the design, conduct, analysis, and evaluation of clinical trials of an investigational product in the context of its overall clinical development. The document will also assist scientific experts charged with preparing application summaries or assessing evidence of efficacy and safety, principally from clinical trials in later phases of development. 1.2 Scope and Direction The focus of this guidance is on statistical principles. It does not address the use of specific statistical procedures or methods. Specific procedural steps to ensure that principles are implemented properly are the responsibility of the sponsor. Integration of data across clinical trials is discussed, but is not a primary focus of this guidance. Selected principles and procedures related to data management or clinical trial monitoring activities are covered in other ICH guidelines and are not addressed here. This guidance should be of interest to individuals from a broad range of scientific disciplines. However, it is assumed that the actual responsibility for all statistical work associated with clinical trials will lie with an appropriately qualified and experienced statistician, as indicated in ICH E6. The role and responsibility of the trial statistician (see Glossary), in collaboration with other clinical trial professionals, is to ensure that statistical principles are applied appropriately in clinical trials supporting drug development. Thus, the trial statistician should have a combination of education/training and experience sufficient to implement the principles articulated in this guidance. For each clinical trial contributing to a marketing application, all important details of its design and conduct and the principal features of its proposed statistical analysis should be clearly specified in a protocol written before the trial begins. The extent to which the procedures in the protocol are followed and the primary analysis is planned a priori will contribute to the degree of confidence in the final results and conclusions of the trial. The protocol and subsequent amendments should be approved by the responsible personnel, including the trial statistician. The trial statistician should ensure that the protocol and any amendments cover all relevant statistical issues clearly and accurately, using technical terminology as appropriate. The principles outlined in this guidance are primarily relevant to clinical trials conducted in the later phases of development, many of which are confirmatory trials of efficacy. In addition to efficacy, confirmatory trials may have as their primary variable a safety variable (e.g. an adverse event, a clinical laboratory variable or an electrocardiographic measure), a pharmacodynamic or a pharmacokinetic variable (as in a confirmatory bioequivalence trial). Furthermore, some confirmatory findings may be derived from data integrated across trials, and selected principles in this guidance are applicable in this situation. Finally, although the early phases of drug development consist mainly of clinical trials that are exploratory in nature, statistical principles are also relevant to these clinical trials. Hence, the substance of this document should be applied as far as possible to all phases of clinical development. Many of the principles delineated in this guidance deal with minimising bias (see Glossary) and maximising precision. As used in this guidance, the term 'bias' describes the systematic tendency of any factors associated with the design, conduct, analysis and interpretation of the results of clinical trials to make the estimate of a treatment effect (see Glossary) deviate from its true value. It is important to identify potential sources of bias as completely as possible so that attempts to limit such bias may be made. The presence of bias may seriously compromise the ability to draw valid conclusions from clinical trials. 2 Statistical Principles for Clinical Trials Some sources of bias arise from the design of the trial, for example an assignment of treatments such that subjects at lower risk are systematically assigned to one treatment. Other sources of bias arise during the conduct and analysis of a clinical trial. For example, protocol violations and exclusion of subjects from analysis based upon knowledge of subject outcomes are possible sources of bias that may affect the accurate assessment of the treatment effect. Because bias can occur in subtle or unknown ways and its effect is not measurable directly, it is important to evaluate the robustness of the results and primary conclusions of the trial. Robustness is a concept that refers to the sensitivity of the overall conclusions to various limitations of the data, assumptions, and analytic approaches to data analysis. Robustness implies that the treatment effect and primary conclusions of the trial are not substantially affected when analyses are carried out based on alternative assumptions or analytic approaches. The interpretation of statistical measures of uncertainty of the treatment effect and treatment comparisons should involve consideration of the potential contribution of bias to the p-value, confidence interval, or inference. Because the predominant approaches to the design and analysis of clinical trials have been based on frequentist statistical methods, the guidance largely refers to the use of frequentist methods (see Glossary) when discussing hypothesis testing and/or confidence intervals. This should not be taken to imply that other approaches are not appropriate: the use of Bayesian (see Glossary) and other approaches may be considered when the reasons for their use are clear and when the resulting conclusions are sufficiently robust. II. CONSIDERATIONS FOR OVERALL CLINICAL DEVELOPMENT 2.1 Trial Context 2.1.1 Development Plan The broad aim of the process of clinical development of a new drug is to find out whether there is a dose range and schedule at which the drug can be shown to be simultaneously safe and effective, to the extent that the risk-benefit relationship is acceptable. The particular subjects who may benefit from the drug, and the specific indications for its use, also need to be defined. Satisfying these broad aims usually requires an ordered programme of clinical trials, each with its own specific objectives (see ICH E8). This should be specified in a clinical plan, or a series of plans, with appropriate decision points and flexibility to allow modification as knowledge accumulates. A marketing application should clearly describe the main content of such plans, and the contribution made by each trial. Interpretation and assessment of the evidence from the total programme of trials involves synthesis of the evidence from the individual trials (see Section 7.2). This is facilitated by ensuring that common standards are adopted for a number of features of the trials such as dictionaries of medical terms, definition and timing of the main measurements, handling of protocol deviations and so on. A statistical summary, overview or meta-analysis (see Glossary) may be informative when medical questions are addressed in more than one trial. Where possible this should be envisaged in the plan so that the relevant trials are clearly identified and any necessary common features of their designs are specified in advance. Other major statistical issues (if any) that are expected to affect a number of trials in a common plan should be addressed in that plan. 3 Statistical Principles for Clinical Trials 2.1.2 Confirmatory Trial A confirmatory trial is an adequately controlled trial in which the hypotheses are stated in advance and evaluated. As a rule, confirmatory trials are necessary to provide firm evidence of efficacy or safety. In such trials the key hypothesis of interest follows directly from the trial’s primary objective, is always pre-defined, and is the hypothesis that is subsequently tested when the trial is complete. In a confirmatory trial it is equally important to estimate with due precision the size of the effects attributable to the treatment of interest and to relate these effects to their clinical significance. Confirmatory trials are intended to provide firm evidence in support of claims and hence adherence to protocols and standard operating procedures is particularly important; unavoidable changes should be explained and documented, and their effect examined. A justification of the design of each such trial, and of other important statistical aspects such as the principal features of the planned analysis, should be set out in the protocol. Each trial should address only a limited number of questions. Firm evidence in support of claims requires that the results of the confirmatory trials demonstrate that the investigational product under test has clinical benefits. The confirmatory trials should therefore be sufficient to answer each key clinical question relevant to the efficacy or safety claim clearly and definitively. In addition, it is important that the basis for generalisation (see Glossary) to the intended patient population is understood and explained; this may also influence the number and type (e.g. specialist or general practitioner) of centres and/or trials needed. The results of the confirmatory trial(s) should be robust. In some circumstances the weight of evidence from a single confirmatory trial may be sufficient. 2.1.3 Exploratory Trial The rationale and design of confirmatory trials nearly always rests on earlier clinical work carried out in a series of exploratory studies. Like all clinical trials, these exploratory studies should have clear and precise objectives. However, in contrast to confirmatory trials, their objectives may not always lead to simple tests of pre-defined hypotheses. In addition, exploratory trials may sometimes require a more flexible approach to design so that changes can be made in response to accumulating results. Their analysis may entail data exploration; tests of hypothesis may be carried out, but the choice of hypothesis may be data dependent. Such trials cannot be the basis of the formal proof of efficacy, although they may contribute to the total body of relevant evidence. Any individual trial may have both confirmatory and exploratory aspects. For example, in most confirmatory trials the data are also subjected to exploratory analyses which serve as a basis for explaining or supporting their findings and for suggesting further hypotheses for later research. The protocol should make a clear distinction between the aspects of a trial which will be used for confirmatory proof and the aspects which will provide data for exploratory analysis. 2.2 Scope of Trials 2.2.1 Population In the earlier phases of drug development the choice of subjects for a clinical trial may be heavily influenced by the wish to maximise the chance of observing specific clinical effects of interest, and hence they may come from a very narrow subgroup of the total patient population for which the drug may eventually be indicated. However by the time the confirmatory trials are undertaken, the subjects in the trials should more closely mirror the target population. Hence, in these trials it is generally helpful to 4 Statistical Principles for Clinical Trials relax the inclusion and exclusion criteria as much as possible within the target population, while maintaining sufficient homogeneity to permit precise estimation of treatment effects. No individual clinical trial can be expected to be totally representative of future users, because of the possible influences of geographical location, the time when it is conducted, the medical practices of the particular investigator(s) and clinics, and so on. However the influence of such factors should be reduced wherever possible, and subsequently discussed during the interpretation of the trial results. 2.2.2 Primary and Secondary Variables The primary variable (‘target’ variable, primary endpoint) should be the variable capable of providing the most clinically relevant and convincing evidence directly related to the primary objective of the trial. There should generally be only one primary variable. This will usually be an efficacy variable, because the primary objective of most confirmatory trials is to provide strong scientific evidence regarding efficacy. Safety/tolerability may sometimes be the primary variable, and will always be an important consideration. Measurements relating to quality of life and health economics are further potential primary variables. The selection of the primary variable should reflect the accepted norms and standards in the relevant field of research. The use of a reliable and validated variable with which experience has been gained either in earlier studies or in published literature is recommended. There should be sufficient evidence that the primary variable can provide a valid and reliable measure of some clinically relevant and important treatment benefit in the patient population described by the inclusion and exclusion criteria. The primary variable should generally be the one used when estimating the sample size (see section 3.5). In many cases, the approach to assessing subject outcome may not be straightforward and should be carefully defined. For example, it is inadequate to specify mortality as a primary variable without further clarification; mortality may be assessed by comparing proportions alive at fixed points in time, or by comparing overall distributions of survival times over a specified interval. Another common example is a recurring event; the measure of treatment effect may again be a simple dichotomous variable (any occurrence during a specified interval), time to first occurrence, rate of occurrence (events per time units of observation), etc. The assessment of functional status over time in studying treatment for chronic disease presents other challenges in selection of the primary variable. There are many possible approaches, such as comparisons of the assessments done at the beginning and end of the interval of observation, comparisons of slopes calculated from all assessments throughout the interval, comparisons of the proportions of subjects exceeding or declining beyond a specified threshold, or comparisons based on methods for repeated measures data. To avoid multiplicity concerns arising from post hoc definitions, it is critical to specify in the protocol the precise definition of the primary variable as it will be used in the statistical analysis. In addition, the clinical relevance of the specific primary variable selected and the validity of the associated measurement procedures will generally need to be addressed and justified in the protocol. The primary variable should be specified in the protocol, along with the rationale for its selection. Redefinition of the primary variable after unblinding will almost always be unacceptable, since the biases this introduces are difficult to assess. When the clinical effect defined by the primary objective is to be measured in more than one way, the protocol should identify one of the measurements as the primary variable on the basis of clinical relevance, importance, objectivity, and/or other relevant characteristics, whenever such selection is feasible. 5 Statistical Principles for Clinical Trials Secondary variables are either supportive measurements related to the primary objective or measurements of effects related to the secondary objectives. Their pre- definition in the protocol is also important, as well as an explanation of their relative importance and roles in interpretation of trial results. The number of secondary variables should be limited and should be related to the limited number of questions to be answered in the trial. 2.2.3 Composite Variables If a single primary variable cannot be selected from multiple measurements associated with the primary objective, another useful strategy is to integrate or combine the multiple measurements into a single or 'composite' variable, using a pre- defined algorithm. Indeed, the primary variable sometimes arises as a combination of multiple clinical measurements (e.g. the rating scales used in arthritis, psychiatric disorders and elsewhere). This approach addresses the multiplicity problem without requiring adjustment to the type I error. The method of combining the multiple measurements should be specified in the protocol, and an interpretation of the resulting scale should be provided in terms of the size of a clinically relevant benefit. When a composite variable is used as a primary variable, the components of this variable may sometimes be analysed separately, where clinically meaningful and validated. When a rating scale is used as a primary variable, it is especially important to address such factors as content validity (see Glossary), inter- and intra-rater reliability (see Glossary) and responsiveness for detecting changes in the severity of disease. 2.2.4 Global Assessment Variables In some cases, 'global assessment' variables (see Glossary) are developed to measure the overall safety, overall efficacy, and/or overall usefulness of a treatment. This type of variable integrates objective variables and the investigator’s overall impression about the state or change in the state of the subject, and is usually a scale of ordered categorical ratings. Global assessments of overall efficacy are well established in some therapeutic areas, such as neurology and psychiatry. Global assessment variables generally have a subjective component. When a global assessment variable is used as a primary or secondary variable, fuller details of the scale should be included in the protocol with respect to: 1) the relevance of the scale to the primary objective of the trial; 2) the basis for the validity and reliability of the scale; 3) how to utilise the data collected on an individual subject to assign him/her to a unique category of the scale; 4) how to assign subjects with missing data to a unique category of the scale, or otherwise evaluate them. If objective variables are considered by the investigator when making a global assessment, then those objective variables should be considered as additional primary, or at least important secondary, variables. Global assessment of usefulness integrates components of both benefit and risk and reflects the decision making process of the treating physician, who must weigh benefit and risk in making product use decisions. A problem with global usefulness variables is that their use could in some cases lead to the result of two products being declared equivalent despite having very different profiles of beneficial and adverse effects. For example, judging the global usefulness of a treatment as equivalent or superior to an 6 Statistical Principles for Clinical Trials alternative may mask the fact that it has little or no efficacy but fewer adverse effects. Therefore it is not advisable to use a global usefulness variable as a primary variable. If global usefulness is specified as primary, it is important to consider specific efficacy and safety outcomes separately as additional primary variables. 2.2.5 Multiple Primary Variables It may sometimes be desirable to use more than one primary variable, each of which (or a subset of which) could be sufficient to cover the range of effects of the therapies. The planned manner of interpretation of this type of evidence should be carefully spelled out. It should be clear whether an impact on any of the variables, some minimum number of them, or all of them, would be considered necessary to achieve the trial objectives. The primary hypothesis or hypotheses and parameters of interest (e.g. mean, percentage, distribution) should be clearly stated with respect to the primary variables identified, and the approach to statistical inference described. The effect on the type I error should be explained because of the potential for multiplicity problems (see Section 5.6); the method of controlling type I error should be given in the protocol. The extent of intercorrelation among the proposed primary variables may be considered in evaluating the impact on type I error. If the purpose of the trial is to demonstrate effects on all of the designated primary variables, then there is no need for adjustment of the type I error, but the impact on type II error and sample size should be carefully considered. 2.2.6 Surrogate Variables When direct assessment of the clinical benefit to the subject through observing actual clinical efficacy is not practical, indirect criteria (surrogate variables - see Glossary) may be considered. Commonly accepted surrogate variables are used in a number of indications where they are believed to be reliable predictors of clinical benefit. There are two principal concerns with the introduction of any proposed surrogate variable. First, it may not be a true predictor of the clinical outcome of interest. For example it may measure treatment activity associated with one specific pharmacological mechanism, but may not provide full information on the range of actions and ultimate effects of the treatment, whether positive or negative. There have been many instances where treatments showing a highly positive effect on a proposed surrogate have ultimately been shown to be detrimental to the subjects' clinical outcome; conversely, there are cases of treatments conferring clinical benefit without measurable impact on proposed surrogates. Secondly, proposed surrogate variables may not yield a quantitative measure of clinical benefit that can be weighed directly against adverse effects. Statistical criteria for validating surrogate variables have been proposed but the experience with their use is relatively limited. In practice, the strength of the evidence for surrogacy depends upon (i) the biological plausibility of the relationship, (ii) the demonstration in epidemiological studies of the prognostic value of the surrogate for the clinical outcome and (iii) evidence from clinical trials that treatment effects on the surrogate correspond to effects on the clinical outcome. Relationships between clinical and surrogate variables for one product do not necessarily apply to a product with a different mode of action for treating the same disease. 2.2.7 Categorised Variables Dichotomisation or other categorisation of continuous or ordinal variables may sometimes be desirable. Criteria of 'success' and 'response' are common examples of dichotomies which require precise specification in terms of, for example, a minimum percentage improvement (relative to baseline) in a continuous variable, or a ranking categorised as at or above some threshold level (e.g., 'good') on an ordinal rating scale. 7 Statistical Principles for Clinical Trials The reduction of diastolic blood pressure below 90mmHg is a common dichotomisation. Categorisations are most useful when they have clear clinical relevance. The criteria for categorisation should be pre-defined and specified in the protocol, as knowledge of trial results could easily bias the choice of such criteria. Because categorisation normally implies a loss of information, a consequence will be a loss of power in the analysis; this should be accounted for in the sample size calculation. 2.3 Design Techniques to Avoid Bias The most important design techniques for avoiding bias in clinical trials are blinding and randomisation, and these should be normal features of most controlled clinical trials intended to be included in a marketing application. Most such trials follow a double-blind approach in which treatments are pre-packed in accordance with a suitable randomisation schedule, and supplied to the trial centre(s) labelled only with the subject number and the treatment period so that no one involved in the conduct of the trial is aware of the specific treatment allocated to any particular subject, not even as a code letter. This approach will be assumed in Section 2.3.1 and most of Section 2.3.2, exceptions being considered at the end. Bias can also be reduced at the design stage by specifying procedures in the protocol aimed at minimising any anticipated irregularities in trial conduct that might impair a satisfactory analysis, including various types of protocol violations, withdrawals and missing values. The protocol should consider ways both to reduce the frequency of such problems, and also to handle the problems that do occur in the analysis of data. 2.3.1 Blinding Blinding or masking is intended to limit the occurrence of conscious and unconscious bias in the conduct and interpretation of a clinical trial arising from the influence which the knowledge of treatment may have on the recruitment and allocation of subjects, their subsequent care, the attitudes of subjects to the treatments, the assessment of end-points, the handling of withdrawals, the exclusion of data from analysis, and so on. The essential aim is to prevent identification of the treatments until all such opportunities for bias have passed. A double-blind trial is one in which neither the subject nor any of the investigator or sponsor staff who are involved in the treatment or clinical evaluation of the subjects are aware of the treatment received. This includes anyone determining subject eligibility, evaluating endpoints, or assessing compliance with the protocol. This level of blinding is maintained throughout the conduct of the trial, and only when the data are cleaned to an acceptable level of quality will appropriate personnel be unblinded. If any of the sponsor staff who are not involved in the treatment or clinical evaluation of the subjects are required to be unblinded to the treatment code (e.g. bioanalytical scientists, auditors, those involved in serious adverse event reporting), the sponsor should have adequate standard operating procedures to guard against inappropriate dissemination of treatment codes. In a single-blind trial the investigator and/or his staff are aware of the treatment but the subject is not, or vice versa. In an open-label trial the identity of treatment is known to all. The double-blind trial is the optimal approach. This requires that the treatments to be applied during the trial cannot be distinguished (appearance, taste, etc.) either before or during administration, and that the blind is maintained appropriately during the whole trial. Difficulties in achieving the double-blind ideal can arise: the treatments may be of a completely different nature, for example, surgery and drug therapy; two drugs may have different formulations and, although they could be made indistinguishable by the use of capsules, changing the formulation might also change the pharmacokinetic 8 Statistical Principles for Clinical Trials and/or pharmacodynamic properties and hence require that bioequivalence of the formulations be established; the daily pattern of administration of two treatments may differ. One way of achieving double-blind conditions under these circumstances is to use a 'double-dummy' (see Glossary) technique. This technique may sometimes force an administration scheme that is sufficiently unusual to influence adversely the motivation and compliance of the subjects. Ethical difficulties may also interfere with its use when, for example, it entails dummy operative procedures. Nevertheless, extensive efforts should be made to overcome these difficulties. The double-blind nature of some clinical trials may be partially compromised by apparent treatment induced effects. In such cases, blinding may be improved by blinding investigators and relevant sponsor staff to certain test results (e.g. selected clinical laboratory measures). Similar approaches (see below) to minimising bias in open-label trials should be considered in trials where unique or specific treatment effects may lead to unblinding individual patients. If a double-blind trial is not feasible, then the single-blind option should be considered. In some cases only an open-label trial is practically or ethically possible. Single-blind and open-label trials provide additional flexibility, but it is particularly important that the investigator's knowledge of the next treatment should not influence the decision to enter the subject; this decision should precede knowledge of the randomised treatment. For these trials, consideration should be given to the use of a centralised randomisation method, such as telephone randomisation, to administer the assignment of randomised treatment. In addition, clinical assessments should be made by medical staff who are not involved in treating the subjects and who remain blind to treatment. In single-blind or open-label trials every effort should be made to minimise the various known sources of bias and primary variables should be as objective as possible. The reasons for the degree of blinding adopted should be explained in the protocol, together with steps taken to minimise bias by other means. For example, the sponsor should have adequate standard operating procedures to ensure that access to the treatment code is appropriately restricted during the process of cleaning the database prior to its release for analysis. Breaking the blind (for a single subject) should be considered only when knowledge of the treatment assignment is deemed essential by the subject’s physician for the subject’s care. Any intentional or unintentional breaking of the blind should be reported and explained at the end of the trial, irrespective of the reason for its occurrence. The procedure and timing for revealing the treatment assignments should be documented. In this document, the blind review (see Glossary) of data refers to the checking of data during the period of time between trial completion (the last observation on the last subject) and the breaking of the blind. 2.3.2 Randomisation Randomisation introduces a deliberate element of chance into the assignment of treatments to subjects in a clinical trial. During subsequent analysis of the trial data, it provides a sound statistical basis for the quantitative evaluation of the evidence relating to treatment effects. It also tends to produce treatment groups in which the distributions of prognostic factors, known and unknown, are similar. In combination with blinding, randomisation helps to avoid possible bias in the selection and allocation of subjects arising from the predictability of treatment assignments. The randomisation schedule of a clinical trial documents the random allocation of treatments to subjects. In the simplest situation it is a sequential list of treatments (or treatment sequences in a crossover trial) or corresponding codes by subject 9 Statistical Principles for Clinical Trials number. The logistics of some trials, such as those with a screening phase, may make matters more complicated, but the unique pre-planned assignment of treatment, or treatment sequence, to subject should be clear. Different trial designs will require different procedures for generating randomisation schedules. The randomisation schedule should be reproducible (if the need arises). Although unrestricted randomisation is an acceptable approach, some advantages can generally be gained by randomising subjects in blocks. This helps to increase the comparability of the treatment groups, particularly when subject characteristics may change over time, as a result, for example, of changes in recruitment policy. It also provides a better guarantee that the treatment groups will be of nearly equal size. In crossover trials it provides the means of obtaining balanced designs with their greater efficiency and easier interpretation. Care should be taken to choose block lengths that are sufficiently short to limit possible imbalance, but that are long enough to avoid predictability towards the end of the sequence in a block. Investigators and other relevant staff should generally be blind to the block length; the use of two or more block lengths, randomly selected for each block, can achieve the same purpose. (Theoretically, in a double-blind trial predictability does not matter, but the pharmacological effects of drugs may provide the opportunity for intelligent guesswork.) In multicentre trials (see Glossary) the randomisation procedures should be organised centrally. It is advisable to have a separate random scheme for each centre, i.e. to stratify by centre or to allocate several whole blocks to each centre. More generally, stratification by important prognostic factors measured at baseline (e.g. severity of disease, age, sex, etc.) may sometimes be valuable in order to promote balanced allocation within strata; this has greater potential benefit in small trials. The use of more than two or three stratification factors is rarely necessary, is less successful at achieving balance and is logistically troublesome. The use of a dynamic allocation procedure (see below) may help to achieve balance across a number of stratification factors simultaneously provided the rest of the trial procedures can be adjusted to accommodate an approach of this type. Factors on which randomisation has been stratified should be accounted for later in the analysis. The next subject to be randomised into a trial should always receive the treatment corresponding to the next free number in the appropriate randomisation schedule (in the respective stratum, if randomisation is stratified). The appropriate number and associated treatment for the next subject should only be allocated when entry of that subject to the randomised part of the trial has been confirmed. Details of the randomisation that facilitate predictability (e.g. block length) should not be contained in the trial protocol. The randomisation schedule itself should be filed securely by the sponsor or an independent party in a manner that ensures that blindness is properly maintained throughout the trial. Access to the randomisation schedule during the trial should take into account the possibility that, in an emergency, the blind may have to be broken for any subject. The procedure to be followed, the necessary documentation, and the subsequent treatment and assessment of the subject should all be described in the protocol. Dynamic allocation is an alternative procedure in which the allocation of treatment to a subject is influenced by the current balance of allocated treatments and, in a stratified trial, by the stratum to which the subject belongs and the balance within that stratum. Deterministic dynamic allocation procedures should be avoided and an appropriate element of randomisation should be incorporated for each treatment allocation. Every effort should be made to retain the double-blind status of the trial. For example, knowledge of the treatment code may be restricted to a central trial office from where the dynamic allocation is controlled, generally through telephone 10 Statistical Principles for Clinical Trials contact. This in turn permits additional checks of eligibility criteria and establishes entry into the trial, features that can be valuable in certain types of multicentre trial. The usual system of pre-packing and labelling drug supplies for double-blind trials can then be followed, but the order of their use is no longer sequential. It is desirable to use appropriate computer algorithms to keep personnel at the central trial office blind to the treatment code. The complexity of the logistics and potential impact on the analysis should be carefully evaluated when considering dynamic allocation. III. TRIAL DESIGN CONSIDERATIONS 3.1 Design Configuration 3.1.1 Parallel Group Design The most common clinical trial design for confirmatory trials is the parallel group design in which subjects are randomised to one of two or more arms, each arm being allocated a different treatment. These treatments will include the investigational product at one or more doses, and one or more control treatments, such as placebo and/or an active comparator. The assumptions underlying this design are less complex than for most other designs. However, as with other designs, there may be additional features of the trial that complicate the analysis and interpretation (e.g. covariates, repeated measurements over time, interactions between design factors, protocol violations, dropouts (see Glossary) and withdrawals). 3.1.2 Crossover Design In the crossover design, each subject is randomised to a sequence of two or more treatments, and hence acts as his own control for treatment comparisons. This simple manoeuvre is attractive primarily because it reduces the number of subjects and usually the number of assessments needed to achieve a specific power, sometimes to a marked extent. In the simplest 2×2 crossover design each subject receives each of two treatments in randomised order in two successive treatment periods, often separated by a washout period. The most common extension of this entails comparing n(>2) treatments in n periods, each subject receiving all n treatments. Numerous variations exist, such as designs in which each subject receives a subset of n(>2) treatments, or ones in which treatments are repeated within a subject. Crossover designs have a number of problems that can invalidate their results. The chief difficulty concerns carryover, that is, the residual influence of treatments in subsequent treatment periods. In an additive model the effect of unequal carryover will be to bias direct treatment comparisons. In the 2×2 design the carryover effect cannot be statistically distinguished from the interaction between treatment and period and the test for either of these effects lacks power because the corresponding contrast is 'between subject'. This problem is less acute in higher order designs, but cannot be entirely dismissed. When the crossover design is used it is therefore important to avoid carryover. This is best done by selective and careful use of the design on the basis of adequate knowledge of both the disease area and the new medication. The disease under study should be chronic and stable. The relevant effects of the medication should develop fully within the treatment period. The washout periods should be sufficiently long for complete reversibility of drug effect. The fact that these conditions are likely to be met should be established in advance of the trial by means of prior information and data. There are additional problems that need careful attention in crossover trials. The most notable of these are the complications of analysis and interpretation arising from the loss of subjects. Also, the potential for carryover leads to difficulties in assigning adverse events which occur in later treatment periods to the appropriate 11 Statistical Principles for Clinical Trials treatment. These, and other issues, are described in ICH E4. The crossover design should generally be restricted to situations where losses of subjects from the trial are expected to be small. A common, and generally satisfactory, use of the 2×2 crossover design is to demonstrate the bioequivalence of two formulations of the same medication. In this particular application in healthy volunteers, carryover effects on the relevant pharmacokinetic variable are most unlikely to occur if the wash-out time between the two periods is sufficiently long. However it is still important to check this assumption during analysis on the basis of the data obtained, for example by demonstrating that no drug is detectable at the start of each period. 3.1.3 Factorial Designs In a factorial design two or more treatments are evaluated simultaneously through the use of varying combinations of the treatments. The simplest example is the 2×2 factorial design in which subjects are randomly allocated to one of the four possible combinations of two treatments, A and B say. These are: A alone; B alone; both A and B; neither A nor B. In many cases this design is used for the specific purpose of examining the interaction of A and B. The statistical test of interaction may lack power to detect an interaction if the sample size was calculated based on the test for main effects. This consideration is important when this design is used for examining the joint effects of A and B, in particular, if the treatments are likely to be used together. Another important use of the factorial design is to establish the dose-response characteristics of the simultaneous use of treatments C and D, especially when the efficacy of each monotherapy has been established at some dose in prior trials. A number, m, of doses of C is selected, usually including a zero dose (placebo), and a similar number, n, of doses of D. The full design then consists of m×n treatment groups, each receiving a different combination of doses of C and D. The resulting estimate of the response surface may then be used to help to identify an appropriate combination of doses of C and D for clinical use (see ICH E4). In some cases, the 2×2 design may be used to make efficient use of clinical trial subjects by evaluating the efficacy of the two treatments with the same number of subjects as would be required to evaluate the efficacy of either one alone. This strategy has proved to be particularly valuable for very large mortality trials. The efficiency and validity of this approach depends upon the absence of interaction between treatments A and B so that the effects of A and B on the primary efficacy variables follow an additive model, and hence the effect of A is virtually identical whether or not it is additional to the effect of B. As for the crossover trial, evidence that this condition is likely to be met should be established in advance of the trial by means of prior information and data. 3.2 Multicentre Trials Multicentre trials are carried out for two main reasons. Firstly, a multicentre trial is an accepted way of evaluating a new medication more efficiently; under some circumstances, it may present the only practical means of accruing sufficient subjects to satisfy the trial objective within a reasonable time-frame. Multicentre trials of this nature may, in principle, be carried out at any stage of clinical development. They may have several centres with a large number of subjects per centre or, in the case of a rare disease, they may have a large number of centres with very few subjects per centre. Secondly, a trial may be designed as a multicentre (and multi-investigator) trial primarily to provide a better basis for the subsequent generalisation of its findings. 12 Statistical Principles for Clinical Trials This arises from the possibility of recruiting the subjects from a wider population and of administering the medication in a broader range of clinical settings, thus presenting an experimental situation that is more typical of future use. In this case the involvement of a number of investigators also gives the potential for a wider range of clinical judgement concerning the value of the medication. Such a trial would be a confirmatory trial in the later phases of drug development and would be likely to involve a large number of investigators and centres. It might sometimes be conducted in a number of different countries in order to facilitate generalisability (see Glossary) even further. If a multicentre trial is to be meaningfully interpreted and extrapolated, then the manner in which the protocol is implemented should be clear and similar at all centres. Furthermore the usual sample size and power calculations depend upon the assumption that the differences between the compared treatments in the centres are unbiased estimates of the same quantity. It is important to design the common protocol and to conduct the trial with this background in mind. Procedures should be standardised as completely as possible. Variation of evaluation criteria and schemes can be reduced by investigator meetings, by the training of personnel in advance of the trial and by careful monitoring during the trial. Good design should generally aim to achieve the same distribution of subjects to treatments within each centre and good management should maintain this design objective. Trials that avoid excessive variation in the numbers of subjects per centre and trials that avoid a few very small centres have advantages if it is later found necessary to take into account the heterogeneity of the treatment effect from centre to centre, because they reduce the differences between different weighted estimates of the treatment effect. (This point does not apply to trials in which all centres are very small and in which centre does not feature in the analysis.) Failure to take these precautions, combined with doubts about the homogeneity of the results may, in severe cases, reduce the value of a multicentre trial to such a degree that it cannot be regarded as giving convincing evidence for the sponsor’s claims. In the simplest multicentre trial, each investigator will be responsible for the subjects recruited at one hospital, so that ‘centre’ is identified uniquely by either investigator or hospital. In many trials, however, the situation is more complex. One investigator may recruit subjects from several hospitals; one investigator may represent a team of clinicians (subinvestigators) who all recruit subjects from their own clinics at one hospital or at several associated hospitals. Whenever there is room for doubt about the definition of centre in a statistical model, the statistical section of the protocol (see Section 5.1) should clearly define the term (e.g. by investigator, location or region) in the context of the particular trial. In most instances centres can be satisfactorily defined through the investigators and ICH E6 provides relevant guidance in this respect. In cases of doubt the aim should be to define centres so as to achieve homogeneity in the important factors affecting the measurements of the primary variables and the influence of the treatments. Any rules for combining centres in the analysis should be justified and specified prospectively in the protocol where possible, but in any case decisions concerning this approach should always be taken blind to treatment, for example at the time of the blind review. The statistical model to be adopted for the estimation and testing of treatment effects should be described in the protocol. The main treatment effect may be investigated first using a model which allows for centre differences, but does not include a term for treatment-by-centre interaction. If the treatment effect is homogeneous across centres, the routine inclusion of interaction terms in the model reduces the efficiency of the test for the main effects. In the presence of true heterogeneity of treatment effects, the interpretation of the main treatment effect is controversial. 13 Statistical Principles for Clinical Trials In some trials, for example some large mortality trials with very few subjects per centre, there may be no reason to expect the centres to have any influence on the primary or secondary variables because they are unlikely to represent influences of clinical importance. In other trials it may be recognised from the start that the limited numbers of subjects per centre will make it impracticable to include the centre effects in the statistical model. In these cases it is not appropriate to include a term for centre in the model, and it is not necessary to stratify the randomisation by centre in this situation. If positive treatment effects are found in a trial with appreciable numbers of subjects per centre, there should generally be an exploration of the heterogeneity of treatment effects across centres, as this may affect the generalisability of the conclusions. Marked heterogeneity may be identified by graphical display of the results of individual centres or by analytical methods, such as a significance test of the treatment-by-centre interaction. When using such a statistical significance test, it is important to recognise that this generally has low power in a trial designed to detect the main effect of treatment. If heterogeneity of treatment effects is found, this should be interpreted with care and vigorous attempts should be made to find an explanation in terms of other features of trial management or subject characteristics. Such an explanation will usually suggest appropriate further analysis and interpretation. In the absence of an explanation, heterogeneity of treatment effect as evidenced, for example, by marked quantitative interactions (see Glossary) implies that alternative estimates of the treatment effect may be required, giving different weights to the centres, in order to substantiate the robustness of the estimates of treatment effect. It is even more important to understand the basis of any heterogeneity characterised by marked qualitative interactions (see Glossary), and failure to find an explanation may necessitate further clinical trials before the treatment effect can be reliably predicted. Up to this point the discussion of multicentre trials has been based on the use of fixed effect models. Mixed models may also be used to explore the heterogeneity of the treatment effect. These models consider centre and treatment-by-centre effects to be random, and are especially relevant when the number of sites is large. 3.3 Type of Comparison 3.3.1 Trials to Show Superiority Scientifically, efficacy is most convincingly established by demonstrating superiority to placebo in a placebo-controlled trial, by showing superiority to an active control treatment or by demonstrating a dose-response relationship. This type of trial is referred to as a ‘superiority’ trial (see Glossary). Generally in this guidance superiority trials are assumed, unless it is explicitly stated otherwise. For serious illnesses, when a therapeutic treatment which has been shown to be efficacious by superiority trial(s) exists, a placebo-controlled trial may be considered unethical. In that case the scientifically sound use of an active treatment as a control should be considered. The appropriateness of placebo control vs. active control should be considered on a trial by trial basis. 3.3.2 Trials to Show Equivalence or Non-inferiority In some cases, an investigational product is compared to a reference treatment without the objective of showing superiority. This type of trial is divided into two major categories according to its objective; one is an 'equivalence' trial (see Glossary) and the other is a 'non-inferiority' trial (see Glossary). 14 Statistical Principles for Clinical Trials Bioequivalence trials fall into the former category. In some situations, clinical equivalence trials are also undertaken for other regulatory reasons such as demonstrating the clinical equivalence of a generic product to the marketed product when the compound is not absorbed and therefore not present in the blood stream. Many active control trials are designed to show that the efficacy of an investigational product is no worse than that of the active comparator, and hence fall into the latter category. Another possibility is a trial in which multiple doses of the investigational drug are compared with the recommended dose or multiple doses of the standard drug. The purpose of this design is simultaneously to show a dose-response relationship for the investigational product and to compare the investigational product with the active control. Active control equivalence or non-inferiority trials may also incorporate a placebo, thus pursuing multiple goals in one trial; for example, they may establish superiority to placebo and hence validate the trial design and simultaneously evaluate the degree of similarity of efficacy and safety to the active comparator. There are well known difficulties associated with the use of the active control equivalence (or non- inferiority) trials that do not incorporate a placebo or do not use multiple doses of the new drug. These relate to the implicit lack of any measure of internal validity (in contrast to superiority trials), thus making external validation necessary. The equivalence (or non-inferiority) trial is not conservative in nature, so that many flaws in the design or conduct of the trial will tend to bias the results towards a conclusion of equivalence. For these reasons, the design features of such trials should receive special attention and their conduct needs special care. For example, it is especially important to minimise the incidence of violations of the entry criteria, non- compliance, withdrawals, losses to follow-up, missing data and other deviations from the protocol, and also to minimise their impact on the subsequent analyses. Active comparators should be chosen with care. An example of a suitable active comparator would be a widely used therapy whose efficacy in the relevant indication has been clearly established and quantified in well designed and well documented superiority trial(s) and which can be reliably expected to exhibit similar efficacy in the contemplated active control trial. To this end, the new trial should have the same important design features (primary variables, the dose of the active comparator, eligibility criteria, etc.) as the previously conducted superiority trials in which the active comparator clearly demonstrated clinically relevant efficacy, taking into account advances in medical or statistical practice relevant to the new trial. It is vital that the protocol of a trial designed to demonstrate equivalence or non- inferiority contain a clear statement that this is its explicit intention. An equivalence margin should be specified in the protocol; this margin is the largest difference that can be judged as being clinically acceptable and should be smaller than differences observed in superiority trials of the active comparator. For the active control equivalence trial, both the upper and the lower equivalence margins are needed, while only the lower margin is needed for the active control non-inferiority trial. The choice of equivalence margins should be justified clinically. Statistical analysis is generally based on the use of confidence intervals (see Section 5.5). For equivalence trials, two-sided confidence intervals should be used. Equivalence is inferred when the entire confidence interval falls within the equivalence margins. Operationally, this is equivalent to the method of using two simultaneous one-sided tests to test the (composite) null hypothesis that the treatment difference is outside the equivalence margins versus the (composite) alternative hypothesis that the treatment difference is within the margins. Because the two null hypotheses are disjoint, the type I error is appropriately controlled. For 15 Statistical Principles for Clinical Trials non-inferiority trials a one-sided interval should be used. The confidence interval approach has a one-sided hypothesis test counterpart for testing the null hypothesis that the treatment difference (investigational product minus control) is equal to the lower equivalence margin versus the alternative that the treatment difference is greater than the lower equivalence margin. The choice of type I error should be a consideration separate from the use of a one-sided or two-sided procedure. Sample size calculations should be based on these methods (see Section 3.5). Concluding equivalence or non-inferiority based on observing a non-significant test result of the null hypothesis that there is no difference between the investigational product and the active comparator is inappropriate. There are also special issues in the choice of analysis sets. Subjects who withdraw or dropout of the treatment group or the comparator group will tend to have a lack of response, and hence the results of using the full analysis set (see Glossary) may be biased toward demonstrating equivalence (see Section 5.2.3). 3.3.3 Trials to Show Dose-response Relationship How response is related to the dose of a new investigational product is a question to which answers may be obtained in all phases of development, and by a variety of approaches (see ICH E4). Dose-response trials may serve a number of objectives, amongst which the following are of particular importance: the confirmation of efficacy; the investigation of the shape and location of the dose-response curve; the estimation of an appropriate starting dose; the identification of optimal strategies for individual dose adjustments; the determination of a maximal dose beyond which additional benefit would be unlikely to occur. These objectives should be addressed using the data collected at a number of doses under investigation, including a placebo (zero dose) wherever appropriate. For this purpose the application of procedures to estimate the relationship between dose and response, including the construction of confidence intervals and the use of graphical methods, is as important as the use of statistical tests. The hypothesis tests that are used may need to be tailored to the natural ordering of doses or to particular questions regarding the shape of the dose-response curve (e.g. monotonicity). The details of the planned statistical procedures should be given in the protocol. 3.4 Group Sequential Designs Group sequential designs are used to facilitate the conduct of interim analysis (see section 4.5 and Glossary). While group sequential designs are not the only acceptable types of designs permitting interim analysis, they are the most commonly applied because it is more practicable to assess grouped subject outcomes at periodic intervals during the trial than on a continuous basis as data from each subject become available. The statistical methods should be fully specified in advance of the availability of information on treatment outcomes and subject treatment assignments (i.e. blind breaking, see Section 4.5). An Independent Data Monitoring Committee (see Glossary) may be used to review or to conduct the interim analysis of data arising from a group sequential design (see Section 4.6). While the design has been most widely and successfully used in large, long-term trials of mortality or major non-fatal endpoints, its use is growing in other circumstances. In particular, it is recognised that safety must be monitored in all trials and therefore the need for formal procedures to cover early stopping for safety reasons should always be considered. 3.5 Sample Size The number of subjects in a clinical trial should always be large enough to provide a reliable answer to the questions addressed. This number is usually determined by the 16 Statistical Principles for Clinical Trials primary objective of the trial. If the sample size is determined on some other basis, then this should be made clear and justified. For example, a trial sized on the basis of safety questions or requirements or important secondary objectives may need larger numbers of subjects than a trial sized on the basis of the primary efficacy question (see, for example, ICH E1a). Using the usual method for determining the appropriate sample size, the following items should be specified: a primary variable, the test statistic, the null hypothesis, the alternative ('working') hypothesis at the chosen dose(s) (embodying consideration of the treatment difference to be detected or rejected at the dose and in the subject population selected), the probability of erroneously rejecting the null hypothesis (the type I error), and the probability of erroneously failing to reject the null hypothesis (the type II error), as well as the approach to dealing with treatment withdrawals and protocol violations. In some instances, the event rate is of primary interest for evaluating power, and assumptions should be made to extrapolate from the required number of events to the eventual sample size for the trial. The method by which the sample size is calculated should be given in the protocol, together with the estimates of any quantities used in the calculations (such as variances, mean values, response rates, event rates, difference to be detected). The basis of these estimates should also be given. It is important to investigate the sensitivity of the sample size estimate to a variety of deviations from these assumptions and this may be facilitated by providing a range of sample sizes appropriate for a reasonable range of deviations from assumptions. In confirmatory trials, assumptions should normally be based on published data or on the results of earlier trials. The treatment difference to be detected may be based on a judgement concerning the minimal effect which has clinical relevance in the management of patients or on a judgement concerning the anticipated effect of the new treatment, where this is larger. Conventionally the probability of type I error is set at 5% or less or as dictated by any adjustments made necessary for multiplicity considerations; the precise choice may be influenced by the prior plausibility of the hypothesis under test and the desired impact of the results. The probability of type II error is conventionally set at 10% to 20%; it is in the sponsor’s interest to keep this figure as low as feasible especially in the case of trials that are difficult or impossible to repeat. Alternative values to the conventional levels of type I and type II error may be acceptable or even preferable in some cases. Sample size calculations should refer to the number of subjects required for the primary analysis. If this is the 'full analysis set', estimates of the effect size may need to be reduced compared to the per protocol set (see Glossary). This is to allow for the dilution of the treatment effect arising from the inclusion of data from patients who have withdrawn from treatment or whose compliance is poor. The assumptions about variability may also need to be revised. The sample size of an equivalence trial or a non-inferiority trial (see Section 3.3.2) should normally be based on the objective of obtaining a confidence interval for the treatment difference that shows that the treatments differ at most by a clinically acceptable difference. When the power of an equivalence trial is assessed at a true difference of zero, then the sample size necessary to achieve this power is underestimated if the true difference is not zero. When the power of a non-inferiority trial is assessed at a zero difference, then the sample size needed to achieve that power will be underestimated if the effect of the investigational product is less than that of the active control. The choice of a 'clinically acceptable’ difference needs justification with respect to its meaning for future patients, and may be smaller than the 'clinically relevant' difference referred to above in the context of superiority trials designed to establish that a difference exists. 17 Statistical Principles for Clinical Trials The exact sample size in a group sequential trial cannot be fixed in advance because it depends upon the play of chance in combination with the chosen stopping guideline and the true treatment difference. The design of the stopping guideline should take into account the consequent distribution of the sample size, usually embodied in the expected and maximum sample sizes. When event rates are lower than anticipated or variability is larger than expected, methods for sample size re-estimation are available without unblinding data or making treatment comparisons (see Section 4.4). 3.6 Data Capture and Processing The collection of data and transfer of data from the investigator to the sponsor can take place through a variety of media, including paper case record forms, remote site monitoring systems, medical computer systems and electronic transfer. Whatever data capture instrument is used, the form and content of the information collected should be in full accordance with the protocol and should be established in advance of the conduct of the clinical trial. It should focus on the data necessary to implement the planned analysis, including the context information (such as timing assessments relative to dosing) necessary to confirm protocol compliance or identify important protocol deviations. ‘Missing values’ should be distinguishable from the ‘value zero’ or ‘characteristic absent’. The process of data capture through to database finalisation should be carried out in accordance with GCP (see ICH E6, Section 5). Specifically, timely and reliable processes for recording data and rectifying errors and omissions are necessary to ensure delivery of a quality database and the achievement of the trial objectives through the implementation of the planned analysis. IV. TRIAL CONDUCT CONSIDERATIONS 4.1 Trial Monitoring and Interim Analysis Careful conduct of a clinical trial according to the protocol has a major impact on the credibility of the results (see ICH E6). Careful monitoring can ensure that difficulties are noticed early and their occurrence or recurrence minimised. There are two distinct types of monitoring that generally characterise confirmatory clinical trials sponsored by the pharmaceutical industry. One type of monitoring concerns the oversight of the quality of the trial, while the other type involves breaking the blind to make treatment comparisons (i.e. interim analysis). Both types of trial monitoring, in addition to entailing different staff responsibilities, involve access to different types of trial data and information, and thus different principles apply for the control of potential statistical and operational bias. For the purpose of overseeing the quality of the trial the checks involved in trial monitoring may include whether the protocol is being followed, the acceptability of data being accrued, the success of planned accrual targets, the appropriateness of the design assumptions, success in keeping patients in the trials, etc. (see Sections 4.2 to 4.4). This type of monitoring does not require access to information on comparative treatment effects, nor unblinding of data and therefore has no impact on type I error. The monitoring of a trial for this purpose is the responsibility of the sponsor (see ICH E6) and can be carried out by the sponsor or an independent group selected by the sponsor. The period for this type of monitoring usually starts with the selection of the trial sites and ends with the collection and cleaning of the last subject’s data. The other type of trial monitoring (interim analysis) involves the accruing of comparative treatment results. Interim analysis requires unblinded (i.e. key 18 Statistical Principles for Clinical Trials breaking) access to treatment group assignment (actual treatment assignment or identification of group assignment) and comparative treatment group summary information. This necessitates that the protocol (or appropriate amendments prior to a first analysis) contains statistical plans for the interim analysis to prevent certain types of bias. This is discussed in Sections 4.5 & 4.6. 4.2 Changes in Inclusion and Exclusion Criteria Inclusion and exclusion criteria should remain constant, as specified in the protocol, throughout the period of subject recruitment. Changes may occasionally be appropriate, for example, in long term trials, where growing medical knowledge either from outside the trial or from interim analyses may suggest a change of entry criteria. Changes may also result from the discovery by monitoring staff that regular violations of the entry criteria are occurring, or that seriously low recruitment rates are due to over-restrictive criteria. Changes should be made without breaking the blind and should always be described by a protocol amendment which should cover any statistical consequences, such as sample size adjustments arising from different event rates, or modifications to the planned analysis, such as stratifying the analysis according to modified inclusion/exclusion criteria. 4.3 Accrual Rates In trials with a long time-scale for the accrual of subjects, the rate of accrual should be monitored and, if it falls appreciably below the projected level, the reasons should be identified and remedial actions taken in order to protect the power of the trial and alleviate concerns about selective entry and other aspects of quality. In a multicentre trial these considerations apply to the individual centres. 4.4 Sample Size Adjustment In long term trials there will usually be an opportunity to check the assumptions which underlay the original design and sample size calculations. This may be particularly important if the trial specifications have been made on preliminary and/or uncertain information. An interim check conducted on the blinded data may reveal that overall response variances, event rates or survival experience are not as anticipated. A revised sample size may then be calculated using suitably modified assumptions, and should be justified and documented in a protocol amendment and in the clinical study report. The steps taken to preserve blindness and the consequences, if any, for the type I error and the width of confidence intervals should be explained. The potential need for re-estimation of the sample size should be envisaged in the protocol whenever possible (see Section 3.5). 4.5 Interim Analysis and Early Stopping An interim analysis is any analysis intended to compare treatment arms with respect to efficacy or safety at any time prior to formal completion of a trial. Because the number, methods and consequences of these comparisons affect the interpretation of the trial, all interim analyses should be carefully planned in advance and described in the protocol. Special circumstances may dictate the need for an interim analysis that was not defined at the start of a trial. In these cases, a protocol amendment describing the interim analysis should be completed prior to unblinded access to treatment comparison data. When an interim analysis is planned with the intention of deciding whether or not to terminate a trial, this is usually accomplished by the use of a group sequential design which employs statistical monitoring schemes as guidelines (see Section 3.4). The goal of such an interim analysis is to stop the trial early if the superiority of the treatment under study is clearly established, if the demonstration of 19 Statistical Principles for Clinical Trials a relevant treatment difference has become unlikely or if unacceptable adverse effects are apparent. Generally, boundaries for monitoring efficacy require more evidence to terminate a trial early (i.e. they are more conservative) than boundaries for monitoring safety. When the trial design and monitoring objective involve multiple endpoints then this aspect of multiplicity may also need to be taken into account. The protocol should describe the schedule of interim analyses, or at least the considerations which will govern its generation, for example if flexible alpha spending function approaches are to be employed; further details may be given in a protocol amendment before the time of the first interim analysis. The stopping guidelines and their properties should be clearly described in the protocol or amendments. The potential effects of early stopping on the analysis of other important variables should also be considered. This material should be written or approved by the Data Monitoring Committee (see Section 4.6), when the trial has one. Deviations from the planned procedure always bear the potential of invalidating the trial results. If it becomes necessary to make changes to the trial, any consequent changes to the statistical procedures should be specified in an amendment to the protocol at the earliest opportunity, especially discussing the impact on any analysis and inferences that such changes may cause. The procedures selected should always ensure that the overall probability of type I error is controlled. The execution of an interim analysis should be a completely confidential process because unblinded data and results are potentially involved. All staff involved in the conduct of the trial should remain blind to the results of such analyses, because of the possibility that their attitudes to the trial will be modified and cause changes in the characteristics of patients to be recruited or biases in treatment comparisons. This principle may be applied to all investigator staff and to staff employed by the sponsor except for those who are directly involved in the execution of the interim analysis. Investigators should only be informed about the decision to continue or to discontinue the trial, or to implement modifications to trial procedures. Most clinical trials intended to support the efficacy and safety of an investigational product should proceed to full completion of planned sample size accrual; trials should be stopped early only for ethical reasons or if the power is no longer acceptable. However, it is recognised that drug development plans involve the need for sponsor access to comparative treatment data for a variety of reasons, such as planning other trials. It is also recognised that only a subset of trials will involve the study of serious life-threatening outcomes or mortality which may need sequential monitoring of accruing comparative treatment effects for ethical reasons. In either of these situations, plans for interim statistical analysis should be in place in the protocol or in protocol amendments prior to the unblinded access to comparative treatment data in order to deal with the potential statistical and operational bias that may be introduced. For many clinical trials of investigational products, especially those that have major public health significance, the responsibility for monitoring comparisons of efficacy and/or safety outcomes should be assigned to an external independent group, often called an Independent Data Monitoring Committee (IDMC), a Data and Safety Monitoring Board or a Data Monitoring Committee whose responsibilities should be clearly described. When a sponsor assumes the role of monitoring efficacy or safety comparisons and therefore has access to unblinded comparative information, particular care should be taken to protect the integrity of the trial and to manage and limit appropriately the sharing of information. The sponsor should assure and document that the internal monitoring committee has complied with written standard operating procedures and 20 Statistical Principles for Clinical Trials that minutes of decision making meetings including records of interim results are maintained. Any interim analysis that is not planned appropriately (with or without the consequences of stopping the trial early) may flaw the results of a trial and possibly weaken confidence in the conclusions drawn. Therefore, such analyses should be avoided. If unplanned interim analysis is conducted, the clinical study report should explain why it was necessary, the degree to which blindness had to be broken, provide an assessment of the potential magnitude of bias introduced, and the impact on the interpretation of the results. 4.6 Role of Independent Data Monitoring Committee (IDMC) (see Sections 1.25 and 5.52 of ICH E6) An IDMC may be established by the sponsor to assess at intervals the progress of a clinical trial, safety data, and critical efficacy variables and recommend to the sponsor whether to continue, modify or terminate a trial. The IDMC should have written operating procedures and maintain records of all its meetings, including interim results; these should be available for review when the trial is complete. The independence of the IDMC is intended to control the sharing of important comparative information and to protect the integrity of the clinical trial from adverse impact resulting from access to trial information. The IDMC is a separate entity from an Institutional Review Board (IRB) or an Independent Ethics Committee (IEC), and its composition should include clinical trial scientists knowledgeable in the appropriate disciplines including statistics. When there are sponsor representatives on the IDMC, their role should be clearly defined in the operating procedures of the committee (for example, covering whether or not they can vote on key issues). Since these sponsor staff would have access to unblinded information, the procedures should also address the control of dissemination of interim trial results within the sponsor organisation. V. DATA ANALYSIS CONSIDERATIONS 5.1 Prespecification of the Analysis When designing a clinical trial the principal features of the eventual statistical analysis of the data should be described in the statistical section of the protocol. This section should include all the principal features of the proposed confirmatory analysis of the primary variable(s) and the way in which anticipated analysis problems will be handled. In case of exploratory trials this section could describe more general principles and directions. The statistical analysis plan (see Glossary) may be written as a separate document to be completed after finalising the protocol. In this document, a more technical and detailed elaboration of the principal features stated in the protocol may be included (see section 7.1). The plan may include detailed procedures for executing the statistical analysis of the primary and secondary variables and other data. The plan should be reviewed and possibly updated as a result of the blind review of the data (see 7.1 for definition) and should be finalised before breaking the blind. Formal records should be kept of when the statistical analysis plan was finalised as well as when the blind was subsequently broken. If the blind review suggests changes to the principal features stated in the protocol, these should be documented in a protocol amendment. Otherwise, it will suffice to update the statistical analysis plan with the considerations suggested from the blind review. Only results from analyses envisaged in the protocol (including amendments) can be regarded as confirmatory. 21 Statistical Principles for Clinical Trials In the statistical section of the clinical study report the statistical methodology should be clearly described including when in the clinical trial process methodology decisions were made (see ICH E3). 5.2 Analysis Sets The set of subjects whose data are to be included in the main analyses should be defined in the statistical section of the protocol. In addition, documentation for all subjects for whom trial procedures (e.g. run-in period) were initiated may be useful. The content of this subject documentation depends on detailed features of the particular trial, but at least demographic and baseline data on disease status should be collected whenever possible. If all subjects randomised into a clinical trial satisfied all entry criteria, followed all trial procedures perfectly with no losses to follow-up, and provided complete data records, then the set of subjects to be included in the analysis would be self-evident. The design and conduct of a trial should aim to approach this ideal as closely as possible, but, in practice, it is doubtful if it can ever be fully achieved. Hence, the statistical section of the protocol should address anticipated problems prospectively in terms of how these affect the subjects and data to be analysed. The protocol should also specify procedures aimed at minimising any anticipated irregularities in study conduct that might impair a satisfactory analysis, including various types of protocol violations, withdrawals and missing values. The protocol should consider ways both to reduce the frequency of such problems, and also to handle the problems that do occur in the analysis of data. Possible amendments to the way in which the analysis will deal with protocol violations should be identified during the blind review. It is desirable to identify any important protocol violation with respect to the time when it occurred, its cause and influence on the trial result. The frequency and type of protocol violations, missing values, and other problems should be documented in the clinical study report and their potential influence on the trial results should be described (see ICH E3). Decisions concerning the analysis set should be guided by the following principles : 1) to minimise bias, and 2) to avoid inflation of type I error. 5.2.1 Full Analysis Set The intention-to-treat (see Glossary) principle implies that the primary analysis should include all randomised subjects. Compliance with this principle would necessitate complete follow-up of all randomised subjects for study outcomes. In practice this ideal may be difficult to achieve, for reasons to be described. In this document the term 'full analysis set' is used to describe the analysis set which is as complete as possible and as close as possible to the intention-to-treat ideal of including all randomised subjects. Preservation of the initial randomisation in analysis is important in preventing bias and in providing a secure foundation for statistical tests. In many clinical trials the use of the full analysis set provides a conservative strategy. Under many circumstances it may also provide estimates of treatment effects which are more likely to mirror those observed in subsequent practice. There are a limited number of circumstances that might lead to excluding randomised subjects from the full analysis set including the failure to satisfy major entry criteria (eligibility violations), the failure to take at least one dose of trial medication and the lack of any data post randomisation. Such exclusions should always be justified. Subjects who fail to satisfy an entry criterion may be excluded from the analysis without the possibility of introducing bias only under the following circumstances: (i) the entry criterion was measured prior to randomisation; 22 Statistical Principles for Clinical Trials (ii) the detection of the relevant eligibility violations can be made completely objectively; (iii) all subjects receive equal scrutiny for eligibility violations; (This may be difficult to ensure in an open-label study, or even in a double-blind study if the data are unblinded prior to this scrutiny, emphasising the importance of the blind review.) (iv) all detected violations of the particular entry criterion are excluded. In some situations, it may be reasonable to eliminate from the set of all randomised subjects any subject who took no trial medication. The intention-to-treat principle would be preserved despite the exclusion of these patients provided, for example, that the decision of whether or not to begin treatment could not be influenced by knowledge of the assigned treatment. In other situations it may be necessary to eliminate from the set of all randomised subjects any subject without data post randomisation. No analysis is complete unless the potential biases arising from these specific exclusions, or any others, are addressed. When the full analysis set of subjects is used, violations of the protocol that occur after randomisation may have an impact on the data and conclusions, particularly if their occurrence is related to treatment assignment. In most respects it is appropriate to include the data from such subjects in the analysis, consistent with the intention- to-treat principle. Special problems arise in connection with subjects withdrawn from treatment after receiving one or more doses who provide no data after this point, and subjects otherwise lost to follow-up, because failure to include these subjects in the full analysis set may seriously undermine the approach. Measurements of primary variables made at the time of the loss to follow-up of a subject for any reason, or subsequently collected in accordance with the intended schedule of assessments in the protocol, are valuable in this context; subsequent collection is especially important in studies where the primary variable is mortality or serious morbidity. The intention to collect data in this way should be described in the protocol. Imputation techniques, ranging from the carrying forward of the last observation to the use of complex mathematical models, may also be used in an attempt to compensate for missing data. Other methods employed to ensure the availability of measurements of primary variables for every subject in the full analysis set may require some assumptions about the subjects' outcomes or a simpler choice of outcome (e.g. success / failure). The use of any of these strategies should be described and justified in the statistical section of the protocol and the assumptions underlying any mathematical models employed should be clearly explained. It is also important to demonstrate the robustness of the corresponding results of analysis especially when the strategy in question could itself lead to biased estimates of treatment effects. Because of the unpredictability of some problems, it may sometimes be preferable to defer detailed consideration of the manner of dealing with irregularities until the blind review of the data at the end of the trial, and, if so, this should be stated in the protocol. 5.2.2 Per Protocol Set The 'per protocol' set of subjects, sometimes described as the 'valid cases', the 'efficacy' sample or the 'evaluable subjects' sample, defines a subset of the subjects in the full analysis set who are more compliant with the protocol and is characterised by criteria such as the following: (i) the completion of a certain pre-specified minimal exposure to the treatment regimen; 23 Statistical Principles for Clinical Trials (ii) the availability of measurements of the primary variable(s); (iii) the absence of any major protocol violations including the violation of entry criteria. The precise reasons for excluding subjects from the per protocol set should be fully defined and documented before breaking the blind in a manner appropriate to the circumstances of the specific trial. The use of the per protocol set may maximise the opportunity for a new treatment to show additional efficacy in the analysis, and most closely reflects the scientific model underlying the protocol. However, the corresponding test of the hypothesis and estimate of the treatment effect may or may not be conservative depending on the trial; the bias, which may be severe, arises from the fact that adherence to the study protocol may be related to treatment and outcome. The problems that lead to the exclusion of subjects to create the per protocol set, and other protocol violations, should be fully identified and summarised. Relevant protocol violations may include errors in treatment assignment, the use of excluded medication, poor compliance, loss to follow-up and missing data. It is good practice to assess the pattern of such problems among the treatment groups with respect to frequency and time to occurrence. 5.2.3 Roles of the Different Analysis Sets In general, it is advantageous to demonstrate a lack of sensitivity of the principal trial results to alternative choices of the set of subjects analysed. In confirmatory trials it is usually appropriate to plan to conduct both an analysis of the full analysis set and a per protocol analysis, so that any differences between them can be the subject of explicit discussion and interpretation. In some cases, it may be desirable to plan further exploration of the sensitivity of conclusions to the choice of the set of subjects analysed. When the full analysis set and the per protocol set lead to essentially the same conclusions, confidence in the trial results is increased, bearing in mind, however, that the need to exclude a substantial proportion of subjects from the per protocol analysis throws some doubt on the overall validity of the trial. The full analysis set and the per protocol set play different roles in superiority trials (which seek to show the investigational product to be superior), and in equivalence or non-inferiority trials (which seek to show the investigational product to be comparable, see section 3.3.2). In superiority trials the full analysis set is used in the primary analysis (apart from exceptional circumstances) because it tends to avoid over-optimistic estimates of efficacy resulting from a per protocol analysis, since the non-compliers included in the full analysis set will generally diminish the estimated treatment effect. However, in an equivalence or non-inferiority trial use of the full analysis set is generally not conservative and its role should be considered very carefully. 5.3 Missing Values and Outliers Missing values represent a potential source of bias in a clinical trial. Hence, every effort should be undertaken to fulfil all the requirements of the protocol concerning the collection and management of data. In reality, however, there will almost always be some missing data. A trial may be regarded as valid, nonetheless, provided the methods of dealing with missing values are sensible, and particularly if those methods are pre-defined in the protocol. Definition of methods may be refined by updating this aspect in the statistical analysis plan during the blind review. Unfortunately, no universally applicable methods of handling missing values can be recommended. An investigation should be made concerning the sensitivity of the 24 Statistical Principles for Clinical Trials results of analysis to the method of handling missing values, especially if the number of missing values is substantial. A similar approach should be adopted to exploring the influence of outliers, the statistical definition of which is, to some extent, arbitrary. Clear identification of a particular value as an outlier is most convincing when justified medically as well as statistically, and the medical context will then often define the appropriate action. Any outlier procedure set out in the protocol or the statistical analysis plan should be such as not to favour any treatment group a priori. Once again, this aspect of the analysis can be usefully updated during blind review. If no procedure for dealing with outliers was foreseen in the trial protocol, one analysis with the actual values and at least one other analysis eliminating or reducing the outlier effect should be performed and differences between their results discussed. 5.4 Data Transformation The decision to transform key variables prior to analysis is best made during the design of the trial on the basis of similar data from earlier clinical trials. Transformations (e.g. square root, logarithm) should be specified in the protocol and a rationale provided, especially for the primary variable(s). The general principles guiding the use of transformations to ensure that the assumptions underlying the statistical methods are met are to be found in standard texts; conventions for particular variables have been developed in a number of specific clinical areas. The decision on whether and how to transform a variable should be influenced by the preference for a scale which facilitates clinical interpretation. Similar considerations apply to other derived variables, such as the use of change from baseline, percentage change from baseline, the 'area under the curve' of repeated measures, or the ratio of two different variables. Subsequent clinical interpretation should be carefully considered, and the derivation should be justified in the protocol. Closely related points are made in Section 2.2.2. 5.5 Estimation, Confidence Intervals and Hypothesis Testing The statistical section of the protocol should specify the hypotheses that are to be tested and/or the treatment effects which are to be estimated in order to satisfy the primary objectives of the trial. The statistical methods to be used to accomplish these tasks should be described for the primary (and preferably the secondary) variables, and the underlying statistical model should be made clear. Estimates of treatment effects should be accompanied by confidence intervals, whenever possible, and the way in which these will be calculated should be identified. A description should be given of any intentions to use baseline data to improve precision or to adjust estimates for potential baseline differences, for example by means of analysis of covariance. It is important to clarify whether one- or two-sided tests of statistical significance will be used, and in particular to justify prospectively the use of one-sided tests. If hypothesis tests are not considered appropriate, then the alternative process for arriving at statistical conclusions should be given. The issue of one-sided or two-sided approaches to inference is controversial and a diversity of views can be found in the statistical literature. The approach of setting type I errors for one-sided tests at half the conventional type I error used in two-sided tests is preferable in regulatory settings. This promotes consistency with the two-sided confidence intervals that are generally appropriate for estimating the possible size of the difference between two treatments. The particular statistical model chosen should reflect the current state of medical and statistical knowledge about the variables to be analysed as well as the statistical 25 Statistical Principles for Clinical Trials design of the trial. All effects to be fitted in the analysis (for example in analysis of variance models) should be fully specified, and the manner, if any, in which this set of effects might be modified in response to preliminary results should be explained. The same considerations apply to the set of covariates fitted in an analysis of covariance. (See also Section 5.7.). In the choice of statistical methods due attention should be paid to the statistical distribution of both primary and secondary variables. When making this choice (for example between parametric and non-parametric methods) it is important to bear in mind the need to provide statistical estimates of the size of treatment effects together with confidence intervals (in addition to significance tests). The primary analysis of the primary variable should be clearly distinguished from supporting analyses of the primary or secondary variables. Within the statistical section of the protocol or the statistical analysis plan there should also be an outline of the way in which data other than the primary and secondary variables will be summarised and reported. This should include a reference to any approaches adopted for the purpose of achieving consistency of analysis across a range of trials, for example for safety data. Modelling approaches that incorporate information on known pharmacological parameters, the extent of protocol compliance for individual subjects or other biologically based data may provide valuable insights into actual or potential efficacy, especially with regard to estimation of treatment effects. The assumptions underlying such models should always be clearly identified, and the limitations of any conclusions should be carefully described. 5.6 Adjustment of Significance and Confidence Levels When multiplicity is present, the usual frequentist approach to the analysis of clinical trial data may necessitate an adjustment to the type I error. Multiplicity may arise, for example, from multiple primary variables (see Section 2.2.2), multiple comparisons of treatments, repeated evaluation over time and/or interim analyses (see Section 4.5). Methods to avoid or reduce multiplicity are sometimes preferable when available, such as the identification of the key primary variable (multiple variables), the choice of a critical treatment contrast (multiple comparisons), the use of a summary measure such as ‘area under the curve’ (repeated measures). In confirmatory analyses, any aspects of multiplicity which remain after steps of this kind have been taken should be identified in the protocol; adjustment should always be considered and the details of any adjustment procedure or an explanation of why adjustment is not thought to be necessary should be set out in the analysis plan. 5.7 Subgroups, Interactions and Covariates The primary variable(s) is often systematically related to other influences apart from treatment. For example, there may be relationships to covariates such as age and sex, or there may be differences between specific subgroups of subjects such as those treated at the different centres of a multicentre trial. In some instances an adjustment for the influence of covariates or for subgroup effects is an integral part of the planned analysis and hence should be set out in the protocol. Pre-trial deliberations should identify those covariates and factors expected to have an important influence on the primary variable(s), and should consider how to account for these in the analysis in order to improve precision and to compensate for any lack of balance between treatment groups. If one or more factors are used to stratify the design, it is appropriate to account for those factors in the analysis. When the potential value of an adjustment is in doubt, it is often advisable to nominate the unadjusted analysis as the one for primary attention, the adjusted analysis being supportive. Special attention should be paid to centre effects and to the role of 26 Statistical Principles for Clinical Trials baseline measurements of the primary variable. It is not advisable to adjust the main analyses for covariates measured after randomisation because they may be affected by the treatments. The treatment effect itself may also vary with subgroup or covariate - for example, the effect may decrease with age or may be larger in a particular diagnostic category of subjects. In some cases such interactions are anticipated or are of particular prior interest (e.g. geriatrics), and hence a subgroup analysis, or a statistical model including interactions, is part of the planned confirmatory analysis. In most cases, however, subgroup or interaction analyses are exploratory and should be clearly identified as such; they should explore the uniformity of any treatment effects found overall. In general, such analyses should proceed first through the addition of interaction terms to the statistical model in question, complemented by additional exploratory analysis within relevant subgroups of subjects, or within strata defined by the covariates. When exploratory, these analyses should be interpreted cautiously; any conclusion of treatment efficacy (or lack thereof) or safety based solely on exploratory subgroup analyses are unlikely to be accepted. 5.8 Integrity of Data and Computer Software Validity The credibility of the numerical results of the analysis depends on the quality and validity of the methods and software (both internally and externally written) used both for data management (data entry, storage, verification, correction and retrieval) and also for processing the data statistically. Data management activities should therefore be based on thorough and effective standard operating procedures. The computer software used for data management and statistical analysis should be reliable, and documentation of appropriate software testing procedures should be available. VI. EVALUATION OF SAFETY AND TOLERABILITY 6.1 Scope of Evaluation In all clinical trials evaluation of safety and tolerability (see Glossary) constitutes an important element. In early phases this evaluation is mostly of an exploratory nature, and is only sensitive to frank expressions of toxicity, whereas in later phases the establishment of the safety and tolerability profile of a drug can be characterised more fully in larger samples of subjects. Later phase controlled trials represent an important means of exploring in an unbiased manner any new potential adverse effects, even if such trials generally lack power in this respect. Certain trials may be designed with the purpose of making specific claims about superiority or equivalence with regard to safety and tolerability compared to another drug or to another dose of the investigational drug. Such specific claims should be supported by relevant evidence from confirmatory trials, similar to that necessary for corresponding efficacy claims. 6.2 Choice of Variables and Data Collection In any clinical trial the methods and measurements chosen to evaluate the safety and tolerability of a drug will depend on a number of factors, including knowledge of the adverse effects of closely related drugs, information from non-clinical and earlier clinical trials and possible consequences of the pharmacodynamic/pharmacokinetic properties of the particular drug, the mode of administration, the type of subjects to be studied, and the duration of the trial. Laboratory tests concerning clinical chemistry and haematology, vital signs, and clinical adverse events (diseases, signs and symptoms) usually form the main body of the safety and tolerability data. The 27 Statistical Principles for Clinical Trials occurrence of serious adverse events and treatment discontinuations due to adverse events are particularly important to register (see ICH E2A and ICH E3). Furthermore, it is recommended that a consistent methodology be used for the data collection and evaluation throughout a clinical trial program in order to facilitate the combining of data from different trials. The use of a common adverse event dictionary is particularly important. This dictionary has a structure which gives the possibility to summarise the adverse event data on three different levels; system-organ class, preferred term or included term (see Glossary). The preferred term is the level on which adverse events usually are summarised, and preferred terms belonging to the same system-organ class could then be brought together in the descriptive presentation of data (see ICH M1). 6.3 Set of Subjects to be Evaluated and Presentation of Data For the overall safety and tolerability assessment, the set of subjects to be summarised is usually defined as those subjects who received at least one dose of the investigational drug. Safety and tolerability variables should be collected as comprehensively as possible from these subjects, including type of adverse event, severity, onset and duration (see ICH E2B). Additional safety and tolerability evaluations may be needed in specific subpopulations, such as females, the elderly (see ICH E7), the severely ill, or those who have a common concomitant treatment. These evaluations may need to address more specific issues (see ICH E3). All safety and tolerability variables will need attention during evaluation, and the broad approach should be indicated in the protocol. All adverse events should be reported, whether or not they are considered to be related to treatment. All available data in the study population should be accounted for in the evaluation. Definitions of measurement units and reference ranges of laboratory variables should be made with care; if different units or different reference ranges appear in the same trial (e.g. if more than one laboratory is involved), then measurements should be appropriately standardised to allow a unified evaluation. Use of a toxicity grading scale should be prespecified and justified. The incidence of a certain adverse event is usually expressed in the form of a proportion relating number of subjects experiencing events to number of subjects at risk. However, it is not always self-evident how to assess incidence. For example, depending on the situation the number of exposed subjects or the extent of exposure (in person-years) could be considered for the denominator. Whether the purpose of the calculation is to estimate a risk or to make a comparison between treatment groups it is important that the definition is given in the protocol. This is especially important if long-term treatment is planned and a substantial proportion of treatment withdrawals or deaths are expected. For such situations survival analysis methods should be considered and cumulative adverse event rates calculated in order to avoid the risk of underestimation. In situations when there is a substantial background noise of signs and symptoms (e.g. in psychiatric trials) one should consider ways of accounting for this in the estimation of risk for different adverse events. One such method is to make use of the 'treatment emergent' (see Glossary) concept in which adverse events are recorded only if they emerge or worsen relative to pretreatment baseline. Other methods to reduce the effect of the background noise may also be appropriate such as ignoring adverse events of mild severity or requiring that an event should have been observed at repeated visits to qualify for inclusion in the numerator. Such methods should be explained and justified in the protocol. 28 Statistical Principles for Clinical Trials 6.4 Statistical Evaluation The investigation of safety and tolerability is a multidimensional problem. Although some specific adverse effects can usually be anticipated and specifically monitored for any drug, the range of possible adverse effects is very large, and new and unforeseeable effects are always possible. Further, an adverse event experienced after a protocol violation, such as use of an excluded medication, may introduce a bias. This background underlies the statistical difficulties associated with the analytical evaluation of safety and tolerability of drugs, and means that conclusive information from confirmatory clinical trials is the exception rather than the rule. In most trials the safety and tolerability implications are best addressed by applying descriptive statistical methods to the data, supplemented by calculation of confidence intervals wherever this aids interpretation. It is also valuable to make use of graphical presentations in which patterns of adverse events are displayed both within treatment groups and within subjects. The calculation of p-values is sometimes useful either as an aid to evaluating a specific difference of interest, or as a 'flagging' device applied to a large number of safety and tolerability variables to highlight differences worth further attention. This is particularly useful for laboratory data, which otherwise can be difficult to summarise appropriately. It is recommended that laboratory data be subjected to both a quantitative analysis, e.g. evaluation of treatment means, and a qualitative analysis where counting of numbers above or below certain thresholds are calculated. If hypothesis tests are used, statistical adjustments for multiplicity to quantify the type I error are appropriate, but the type II error is usually of more concern. Care should be taken when interpreting putative statistically significant findings when there is no multiplicity adjustment. In the majority of trials investigators are seeking to establish that there are no clinically unacceptable differences in safety and tolerability compared with either a comparator drug or a placebo. As is the case for non-inferiority or equivalence evaluation of efficacy the use of confidence intervals is preferred to hypothesis testing in this situation. In this way, the considerable imprecision often arising from low frequencies of occurrence is clearly demonstrated. 6.5 Integrated Summary The safety and tolerability properties of a drug are commonly summarised across trials continuously during an investigational product’s development and in particular at the time of a marketing application. The usefulness of this summary, however, is dependent on adequate and well-controlled individual trials with high data quality. The overall usefulness of a drug is always a question of balance between risk and benefit and in a single trial such a perspective could also be considered, even if the assessment of risk/benefit usually is performed in the summary of the entire clinical trial program. (See section 7.2.2) For more details on the reporting of safety and tolerability, see Chapter 12 of ICH E3. VII. REPORTING 7.1 Evaluation and Reporting As stated in the Introduction, the structure and content of clinical study reports is the subject of ICH E3. That ICH guidance fully covers the reporting of statistical work, appropriately integrated with clinical and other material. The current section is therefore relatively brief. 29 Statistical Principles for Clinical Trials During the planning phase of a trial the principal features of the analysis should have been specified in the protocol as described in Section 5. When the conduct of the trial is over and the data are assembled and available for preliminary inspection, it is valuable to carry out the blind review of the planned analysis also described in Section 5. This pre-analysis review, blinded to treatment, should cover decisions concerning, for example, the exclusion of subjects or data from the analysis sets; possible transformations may also be checked, and outliers defined; important covariates identified in other recent research may be added to the model; the use of parametric or non-parametric methods may be reconsidered. Decisions made at this time should be described in the report, and should be distinguished from those made after the statistician has had access to the treatment codes, as blind decisions will generally introduce less potential for bias. Statisticians or other staff involved in unblinded interim analysis should not participate in the blind review or in making modifications to the statistical analysis plan. When the blinding is compromised by the possibility that treatment induced effects may be apparent in the data, special care will be needed for the blind review. Many of the more detailed aspects of presentation and tabulation should be finalised at or about the time of the blind review so that by the time of the actual analysis full plans exist for all its aspects including subject selection, data selection and modification, data summary and tabulation, estimation and hypothesis testing. Once data validation is complete, the analysis should proceed according to the pre-defined plans; the more these plans are adhered to, the greater the credibility of the results. Particular attention should be paid to any differences between the planned analysis and the actual analysis as described in the protocol, protocol amendments or the updated statistical analysis plan based on a blind review of data. A careful explanation should be provided for deviations from the planned analysis. All subjects who entered the trial should be accounted for in the report, whether or not they are included in the analysis. All reasons for exclusion from analysis should be documented; for any subject included in the full analysis set but not in the per protocol set, the reasons for exclusion from the latter should also be documented. Similarly, for all subjects included in an analysis set, the measurements of all important variables should be accounted for at all relevant time-points. The effect of all losses of subjects or data, withdrawals from treatment and major protocol violations on the main analyses of the primary variable(s) should be considered carefully. Subjects lost to follow up, withdrawn from treatment, or with a severe protocol violation should be identified, and a descriptive analysis of them provided, including the reasons for their loss and its relationship to treatment and outcome. Descriptive statistics form an indispensable part of reports. Suitable tables and/or graphical presentations should illustrate clearly the important features of the primary and secondary variables and of key prognostic and demographic variables. The results of the main analyses relating to the objectives of the trial should be the subject of particularly careful descriptive presentation. When reporting the results of significance tests, precise p-values (e.g.'p=0.034') should be reported rather than making exclusive reference to critical values. Although the primary goal of the analysis of a clinical trial should be to answer the questions posed by its main objectives, new questions based on the observed data may well emerge during the unblinded analysis. Additional and perhaps complex statistical analysis may be the consequence. This additional work should be strictly distinguished in the report from work which was planned in the protocol. 30 Statistical Principles for Clinical Trials The play of chance may lead to unforeseen imbalances between the treatment groups in terms of baseline measurements not pre-defined as covariates in the planned analysis but having some prognostic importance nevertheless. This is best dealt with by showing that an additional analysis which accounts for these imbalances reaches essentially the same conclusions as the planned analysis. If this is not the case, the effect of the imbalances on the conclusions should be discussed. In general, sparing use should be made of unplanned analyses. Such analyses are often carried out when it is thought that the treatment effect may vary according to some other factor or factors. An attempt may then be made to identify subgroups of subjects for whom the effect is particularly beneficial. The potential dangers of over- interpretation of unplanned subgroup analyses are well known (see also Section 5.7), and should be carefully avoided. Although similar problems of interpretation arise if a treatment appears to have no benefit, or an adverse effect, in a subgroup of subjects, such possibilities should be properly assessed and should therefore be reported. Finally statistical judgement should be brought to bear on the analysis, interpretation and presentation of the results of a clinical trial. To this end the trial statistician should be a member of the team responsible for the clinical study report, and should approve the clinical report. 7.2 Summarising the Clinical Database An overall summary and synthesis of the evidence on safety and efficacy from all the reported clinical trials is required for a marketing application (Expert report in EU, integrated summary reports in USA, Gaiyo in Japan). This may be accompanied, when appropriate, by a statistical combination of results. Within the summary a number of areas of specific statistical interest arise: describing the demography and clinical features of the population treated during the course of the clinical trial programme; addressing the key questions of efficacy by considering the results of the relevant (usually controlled) trials and highlighting the degree to which they reinforce or contradict each other; summarising the safety information available from the combined database of all the trials whose results contribute to the marketing application and identifying potential safety issues. During the design of a clinical programme careful attention should be paid to the uniform definition and collection of measurements which will facilitate subsequent interpretation of the series of trials, particularly if they are likely to be combined across trials. A common dictionary for recording the details of medication, medical history and adverse events should be selected and used. A common definition of the primary and secondary variables is nearly always worthwhile, and essential for meta-analysis. The manner of", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "to", "definition": "randomisation/entry, the handling of protocol violators and deviators and perhaps the definition of prognostic factors, should all be kept compatible unless there are valid reasons not to do so. Any statistical procedures used to combine data across trials should be described in detail. Attention should be paid to the possibility of bias associated with the selection of trials, to the homogeneity of their results, and to the proper modelling of the various sources of variation. The sensitivity of conclusions to the assumptions and selections made should be explored. 7.2.1 Efficacy Data Individual clinical trials should always be large enough to satisfy their objectives. Additional valuable information may also be gained by summarising a series of clinical trials which address essentially identical key efficacy questions. The main results of such a set of trials should be presented in an identical form to permit 31 Statistical Principles for Clinical Trials comparison, usually in tables or graphs which focus on estimates plus confidence limits. The use of meta-analytic techniques to combine these estimates is often a useful addition, because it allows a more precise overall estimate of the size of the treatment effects to be generated, and provides a complete and concise summary of the results of the trials. Under exceptional circumstances a meta analytic approach may also be the most appropriate way, or the only way, of providing sufficient overall evidence of efficacy via an overall hypothesis test. When used for this purpose the meta-analysis should have its own prospectively written protocol. 7.2.2 Safety Data In summarising safety data it is important to examine the safety database thoroughly for any indications of potential toxicity, and to follow up any indications by looking for an associated supportive pattern of observations. The combination of the safety data from all human exposure to the drug provides an important source of information, because its larger sample size provides the best chance of detecting the rarer adverse events and, perhaps, of estimating their approximate incidence. However, incidence data from this database are difficult to evaluate because of the lack of a comparator group, and data from comparative trials are especially valuable in overcoming this difficulty. The results from trials which use a common comparator (placebo or specific active comparator) should be combined and presented separately for each comparator providing sufficient data. All indications of potential toxicity arising from exploration of the data should be reported. The evaluation of the reality of these potential adverse effects should take account of the issue of multiplicity arising from the numerous comparisons made. The evaluation should also make appropriate use of survival analysis methods to exploit the potential relationship of the incidence of adverse events to duration of exposure and/or follow-up. The risks associated with identified adverse effects should be appropriately quantified to allow a proper assessment of the risk/benefit relationship.", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Bayesian Approaches", "definition": "Approaches to data analysis that provide a posterior probability distribution for some parameter (e.g. treatment effect), derived from the observed data and a prior probability distribution for the parameter. The posterior distribution is then used as the basis for statistical inference. Bias (Statistical & Operational) The systematic tendency of any factors associated with the design, conduct, analysis and evaluation of the results of a clinical trial to make the estimate of a treatment effect deviate from its true value. Bias introduced through deviations in conduct is referred to as 'operational' bias. The other sources of bias listed above are referred to as 'statistical'.", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Blind Review", "definition": "The checking and assessment of data during the period of time between trial completion (the last observation on the last subject) and the breaking of the blind, for the purpose of finalising the planned analysis.", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Content Validity", "definition": "The extent to which a variable (e.g. a rating scale) measures what it is supposed to measure. 32 Statistical Principles for Clinical Trials", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Double-Dummy", "definition": "A technique for retaining the blind when administering supplies in a clinical trial, when the two treatments cannot be made identical. Supplies are prepared for Treatment A (active and indistinguishable placebo) and for Treatment B (active and indistinguishable placebo). Subjects then take two sets of treatment; either A (active) and B (placebo), or A (placebo) and B (active).", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Dropout", "definition": "A subject in a clinical trial who for any reason fails to continue in the trial until the last visit required of him/her by the study protocol.", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Equivalence Trial", "definition": "A trial with the primary objective of showing that the response to two or more treatments differs by an amount which is clinically unimportant. This is usually demonstrated by showing that the true treatment difference is likely to lie between a lower and an upper equivalence margin of clinically acceptable differences.", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Frequentist Methods", "definition": "Statistical methods, such as significance tests and confidence intervals, which can be interpreted in terms of the frequency of certain outcomes occurring in hypothetical repeated realisations of the same experimental situation.", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Full Analysis Set", "definition": "The set of subjects that is as close as possible to the ideal implied by the intention-to- treat principle. It is derived from the set of all randomised subjects by minimal and justified elimination of subjects. Generalisability, Generalisation The extent to which the findings of a clinical trial can be reliably extrapolated from the subjects who participated in the trial to a broader patient population and a broader range of clinical settings. Global Assessment Variable A single variable, usually a scale of ordered categorical ratings, which integrates objective variables and the investigator's overall impression about the state or change in state of a subject. Independent Data Monitoring Committee (IDMC) (Data and Safety Monitoring Board, Monitoring Committee, Data Monitoring Committee) An independent data-monitoring committee that may be established by the sponsor to assess at intervals the progress of a clinical trial, the safety data, and the critical efficacy endpoints, and to recommend to the sponsor whether to continue, modify, or stop a trial. Intention-To-Treat Principle The principle that asserts that the effect of a treatment policy can be best assessed by evaluating on the basis of the intention to treat a subject (i.e. the planned treatment regimen) rather than the actual treatment given. It has the consequence that subjects allocated to a treatment group should be followed up, assessed and analysed as members of that group irrespective of their compliance to the planned course of treatment. 33 Statistical Principles for Clinical Trials Interaction (Qualitative & Quantitative) The situation in which a treatment contrast (e.g. difference between investigational product and control) is dependent on another factor (e.g. centre). A quantitative interaction refers to the case where the magnitude of the contrast differs at the different levels of the factor, whereas for a qualitative interaction the direction of the contrast differs for at least one level of the factor. Inter-Rater Reliability The property of yielding equivalent results when used by different raters on different occasions. Intra-Rater Reliability The property of yielding equivalent results when used by the same rater on different occasions.", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Interim Analysis", "definition": "Any analysis intended to compare treatment arms with respect to efficacy or safety at any time prior to the formal completion of a trial.", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Meta-Analysis", "definition": "The formal evaluation of the quantitative evidence from two or more trials bearing on the same question. This most commonly involves the statistical combination of summary statistics from the various trials, but the term is sometimes also used to refer to the combination of the raw data.", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Multicentre Trial", "definition": "A clinical trial conducted according to a single protocol but at more than one site, and therefore, carried out by more than one investigator.", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Non-Inferiority Trial", "definition": "A trial with the primary objective of showing that the response to the investigational product is not clinically inferior to a comparative agent (active or placebo control). Preferred and Included Terms In a hierarchical medical dictionary, for example MedDRA, the included term is the lowest level of dictionary term to which the investigator description is coded. The preferred term is the level of grouping of included terms typically used in reporting frequency of occurrence. For example, the investigator text “Pain in the left arm” might be coded to the included term “Joint pain”, which is reported at the preferred term level as “Arthralgia”. Per Protocol Set (Valid Cases, Efficacy Sample, Evaluable Subjects Sample) The set of data generated by the subset of subjects who complied with the protocol sufficiently to ensure that these data would be likely to exhibit the effects of treatment, according to the underlying scientific model. Compliance covers such considerations as exposure to treatment, availability of measurements and absence of major protocol violations. Safety & Tolerability The safety of a medical product concerns the medical risk to the subject, usually assessed in a clinical trial by laboratory tests (including clinical chemistry and haematology), vital signs, clinical adverse events (diseases, signs and symptoms), and 34 Statistical Principles for Clinical Trials other special safety tests (e.g. ECGs, ophthalmology). The tolerability of the medical product represents the degree to which overt adverse effects can be tolerated by the subject. Statistical Analysis Plan A statistical analysis plan is a document that contains a more technical and detailed elaboration of the principal features of the analysis described in the protocol, and includes detailed procedures for executing the statistical analysis of the primary and secondary variables and other data.", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Superiority Trial", "definition": "A trial with the primary objective of showing that the response to the investigational product is superior to a comparative agent (active or placebo control).", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Surrogate Variable", "definition": "A variable that provides an indirect measurement of effect in situations where direct measurement of clinical effect is not feasible or practical.", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Treatment Effect", "definition": "An effect attributed to a treatment in a clinical trial. In most clinical trials the treatment effect of interest is a comparison (or contrast) of two or more treatments.", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Treatment Emergent", "definition": "An event that emerges during treatment having been absent pre-treatment, or worsens relative to the pre-treatment state.", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Trial Statistician", "definition": "A statistician who has a combination of education/training and experience sufficient to implement the principles in this guidance and who is responsible for the statistical aspects of the trial. 35", "sources": [ "ICH_E9.pdf" ], "file": "ICH_E9.pdf", "type": "pdf" }, { "term": "Guidance for Industry", "definition": "Part 11, Electronic Records; Electronic Signatures — Scope", "sources": [ "Part_11_Electronic_Records.pdf" ], "file": "Part_11_Electronic_Records.pdf", "type": "pdf" }, { "term": "and Application", "definition": "Division of Drug Information, HFD-240 Center for Drug Evaluation and Research (CDER) (Tel) 301-827-4573 http://www.fda.gov/cder/guidance/index.htm", "sources": [ "Part_11_Electronic_Records.pdf" ], "file": "Part_11_Electronic_Records.pdf", "type": "pdf" }, { "term": "or", "definition": "Center for Food Safety and Applied Nutrition (CFSAN) http://www.cfsan.fda.gov/~dms/guidance.html. U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) Center for Biologics Evaluation and Research (CBER) Center for Devices and Radiological Health (CDRH) Center for Food Safety and Applied Nutrition (CFSAN) Center for Veterinary Medicine (CVM) Office of Regulatory Affairs (ORA)", "sources": [ "Part_11_Electronic_Records.pdf" ], "file": "Part_11_Electronic_Records.pdf", "type": "pdf" }, { "term": "I.", "definition": "INTRODUCTION............................................................................................................. 1 II. BACKGROUND ............................................................................................................... 2 III. DISCUSSION.................................................................................................................... 3 A. Overall Approach to Part 11 Requirements................................................................................3 B. Details of Approach – Scope of Part 11 .......................................................................................4 1. Narrow Interpretation of Scope .....................................................................................................4 2. Definition of Part 11 Records ........................................................................................................5 C. Approach to Specific Part 11 Requirements ...............................................................................6 1. Validation.........................................................................................................................................6 2. Audit Trail........................................................................................................................................6 3. Legacy Systems ................................................................................................................................7 4. Copies of Records ............................................................................................................................7 5. Record Retention..............................................................................................................................8 IV. REFERENCES.................................................................................................................. 9 Contains Nonbinding Recommendations 1 Guidance for Industry1 1 Part 11, Electronic Records; Electronic Signatures — 2", "sources": [ "Part_11_Electronic_Records.pdf" ], "file": "Part_11_Electronic_Records.pdf", "type": "pdf" }, { "term": "Scope and Application", "definition": "3 4 5 6 This guidance represents the Food and Drug Administration's (FDA's) current thinking on this topic. It 7 does not create or confer any rights for or on any person and does not operate to bind FDA or the public. 8 You can use an alternative approach if the approach satisfies the requirements of the applicable statutes 9 and regulations. If you want to discuss an alternative approach, contact the FDA staff responsible for 10 implementing this guidance. If you cannot identify the appropriate FDA staff, call the appropriate 11 number listed on the title page of this guidance. 12 13 14 15 I.", "sources": [ "Part_11_Electronic_Records.pdf" ], "file": "Part_11_Electronic_Records.pdf", "type": "pdf" }, { "term": "INTRODUCTION", "definition": "16 17 This guidance is intended to describe the Food and Drug Administration's (FDA’s) current 18 thinking regarding the scope and application of part 11 of Title 21 of the Code of Federal 19 Regulations; Electronic Records; Electronic Signatures (21 CFR Part 11).2 20 21 This document provides guidance to persons who, in fulfillment of a requirement in a statute or 22 another part of FDA's regulations to maintain records or submit information to FDA,3 have 23 chosen to maintain the records or submit designated information electronically and, as a result, 24 have become subject to part 11. Part 11 applies to records in electronic form that are created, 25 modified, maintained, archived, retrieved, or transmitted under any records requirements set 26 forth in Agency regulations. Part 11 also applies to electronic records submitted to the Agency 27 under the Federal Food, Drug, and Cosmetic Act (the Act) and the Public Health Service Act (the 28 PHS Act), even if such records are not specifically identified in Agency regulations (§ 11.1). 29 The underlying requirements set forth in the Act, PHS Act, and FDA regulations (other than part 30 11) are referred to in this guidance document as predicate rules. 31 32 1 This guidance has been prepared by the Office of Compliance in the Center for Drug Evaluation and Research (CDER) in consultation with the other Agency centers and the Office of Regulatory Affairs at the Food and Drug Administration. 2 62 FR 13430 3 These requirements include, for example, certain provisions of the Current Good Manufacturing Practice regulations (21 CFR Part 211), the Quality System regulation (21 CFR Part 820), and the Good Laboratory Practice for Nonclinical Laboratory Studies regulations (21 CFR Part 58). Contains Nonbinding Recommendations 2 As an outgrowth of its current good manufacturing practice (CGMP) initiative for human and 33 animal drugs and biologics,4 FDA is re-examining part 11 as it applies to all FDA regulated 34 products. We anticipate initiating rulemaking to change part 11 as a result of that re- 35 examination. This guidance explains that we will narrowly interpret the scope of part 11. While 36 the re-examination of part 11 is under way, we intend to exercise enforcement discretion with 37 respect to certain part 11 requirements. That is, we do not intend to take enforcement action to 38 enforce compliance with the validation, audit trail, record retention, and record copying 39 requirements of part 11 as explained in this guidance. However, records must still be maintained 40 or submitted in accordance with the underlying predicate rules, and the Agency can take 41 regulatory action for noncompliance with such predicate rules. 42 43 In addition, we intend to exercise enforcement discretion and do not intend to take (or 44 recommend) action to enforce any part 11 requirements with regard to systems that were 45 operational before August 20, 1997, the effective date of part 11 (commonly known as legacy 46 systems) under the circumstances described in section III.C.3 of this guidance. 47 48 Note that part 11 remains in effect and that this exercise of enforcement discretion applies only 49 as identified in this guidance. 50 51 FDA's guidance documents, including this guidance, do not establish legally enforceable 52 responsibilities. Instead, guidances describe the Agency's current thinking on a topic and should 53 be viewed only as recommendations, unless specific regulatory or statutory requirements are 54 cited. The use of the word should in Agency guidances means that something is suggested or 55 recommended, but not required. 56 57 58 II.", "sources": [ "Part_11_Electronic_Records.pdf" ], "file": "Part_11_Electronic_Records.pdf", "type": "pdf" }, { "term": "BACKGROUND", "definition": "59 60 In March of 1997, FDA issued final part 11 regulations that provide criteria for acceptance by 61 FDA, under certain circumstances, of electronic records, electronic signatures, and handwritten 62 signatures executed to electronic records as equivalent to paper records and handwritten 63 signatures executed on paper. These regulations, which apply to all FDA program areas, were 64 intended to permit the widest possible use of electronic technology, compatible with FDA's 65 responsibility to protect the public health. 66 67 After part 11 became effective in August 1997, significant discussions ensued among industry, 68 contractors, and the Agency concerning the interpretation and implementation of the regulations. 69 FDA has (1) spoken about part 11 at many conferences and met numerous times with an industry 70 coalition and other interested parties in an effort to hear more about potential part 11 issues; (2) 71 published a compliance policy guide, CPG 7153.17: Enforcement Policy: 21 CFR Part 11; 72 Electronic Records; Electronic Signatures; and (3) published numerous draft guidance 73 documents including the following: 74 4 See Pharmaceutical CGMPs for the 21st Century: A Risk-Based Approach; A Science and Risk-Based Approach to Product Quality Regulation Incorporating an Integrated Quality Systems Approach at www.fda.gov/oc/guidance/gmp.html. Contains Nonbinding Recommendations 3 75 • 21 CFR Part 11; Electronic Records; Electronic Signatures, Validation 76 • 21 CFR Part 11; Electronic Records; Electronic Signatures, Glossary of Terms 77 • 21 CFR Part 11; Electronic Records; Electronic Signatures, Time Stamps 78 • 21 CFR Part 11; Electronic Records; Electronic Signatures, Maintenance of Electronic 79", "sources": [ "Part_11_Electronic_Records.pdf" ], "file": "Part_11_Electronic_Records.pdf", "type": "pdf" }, { "term": "Records", "definition": "80 • 21 CFR Part 11; Electronic Records; Electronic Signatures, Electronic Copies of 81", "sources": [ "Part_11_Electronic_Records.pdf" ], "file": "Part_11_Electronic_Records.pdf", "type": "pdf" }, { "term": "Electronic Records", "definition": "82 83 Throughout all of these communications, concerns have been raised that some interpretations of 84 the part 11 requirements would (1) unnecessarily restrict the use of electronic technology in a 85 manner that is inconsistent with FDA's stated intent in issuing the rule, (2) significantly increase 86 the costs of compliance to an extent that was not contemplated at the time the rule was drafted, 87 and (3) discourage innovation and technological advances without providing a significant public 88 health benefit. These concerns have been raised particularly in the areas of part 11 requirements 89 for validation, audit trails, record retention, record copying, and legacy systems. 90 91 As a result of these concerns, we decided to review the part 11 documents and related issues, 92 particularly in light of the Agency's CGMP initiative. In the Federal Register of February 4, 93 2003 (68 FR 5645), we announced the withdrawal of the draft guidance for industry, 21 CFR 94 Part 11; Electronic Records; Electronic Signatures, Electronic Copies of Electronic Records. 95 We had decided we wanted to minimize industry time spent reviewing and commenting on the 96 draft guidance when that draft guidance may no longer represent our approach under the CGMP 97 initiative. Then, in the Federal Register of February 25, 2003 (68 FR 8775), we announced the 98 withdrawal of the part 11 draft guidance documents on validation, glossary of terms, time 99 stamps,5 maintenance of electronic records, and CPG 7153.17. We received valuable public 100 comments on these draft guidances, and we plan to use that information to help with future 101 decision-making with respect to part 11. We do not intend to re-issue these draft guidance 102 documents or the CPG. 103 104 We are now re-examining part 11, and we anticipate initiating rulemaking to revise provisions of 105 that regulation. To avoid unnecessary resource expenditures to comply with part 11 106 requirements, we are issuing this guidance to describe how we intend to exercise enforcement 107 discretion with regard to certain part 11 requirements during the re-examination of part 11. As 108 mentioned previously, part 11 remains in effect during this re-examination period. 109 110 111 III.", "sources": [ "Part_11_Electronic_Records.pdf" ], "file": "Part_11_Electronic_Records.pdf", "type": "pdf" }, { "term": "DISCUSSION", "definition": "112 113 A. Overall Approach to Part 11 Requirements 114 115 5 Although we withdrew the draft guidance on time stamps, our current thinking has not changed in that when using time stamps for systems that span different time zones, we do not expect you to record the signer’s local time. When using time stamps, they should be implemented with a clear understanding of the time zone reference used. In such instances, system documentation should explain time zone references as well as zone acronyms or other naming conventions. Contains Nonbinding Recommendations 4 As described in more detail below, the approach outlined in this guidance is based on three main 116 elements: 117 118 • Part 11 will be interpreted narrowly; we are now clarifying that fewer records will be 119 considered subject to part 11. 120 • For those records that remain subject to part 11, we intend to exercise enforcement 121 discretion with regard to part 11 requirements for validation, audit trails, record retention, 122 and record copying in the manner described in this guidance and with regard to all part 11 123 requirements for systems that were operational before the effective date of part 11 (also 124 known as legacy systems). 125 • We will enforce all predicate rule requirements, including predicate rule record and 126 recordkeeping requirements. 127 It is important to note that FDA's exercise of enforcement discretion as described in this 128 guidance is limited to specified part 11 requirements (setting aside legacy systems, as to which 129 the extent of enforcement discretion, under certain circumstances, will be more broad). We 130 intend to enforce all other provisions of part 11 including, but not limited to, certain controls for 131 closed systems in § 11.10. For example, we intend to enforce provisions related to the following 132 controls and requirements: 133 134 • limiting system access to authorized individuals 135 • use of operational system checks 136 • use of authority checks 137 • use of device checks 138 • determination that persons who develop, maintain, or use electronic systems have the 139 education, training, and experience to perform their assigned tasks 140 • establishment of and adherence to written policies that hold individuals accountable for 141 actions initiated under their electronic signatures 142 • appropriate controls over systems documentation 143 • controls for open systems corresponding to controls for closed systems bulleted above (§ 144 11.30) 145 • requirements related to electronic signatures (e.g., §§ 11.50, 11.70, 11.100, 11.200, and 146 11.300) 147 148 We expect continued compliance with these provisions, and we will continue to enforce them. 149 Furthermore, persons must comply with applicable predicate rules, and records that are required 150 to be maintained or submitted must remain secure and reliable in accordance with the predicate 151 rules. 152 153 B. Details of Approach – Scope of Part 11 154 155 1. Narrow Interpretation of Scope 156 157 We understand that there is some confusion about the scope of part 11. Some have understood 158 the scope of part 11 to be very broad. We believe that some of those broad interpretations could 159 Contains Nonbinding Recommendations 5 lead to unnecessary controls and costs and could discourage innovation and technological 160 advances without providing added benefit to the public health. As a result, we want to clarify 161 that the Agency intends to interpret the scope of part 11 narrowly. 162 163 Under the narrow interpretation of the scope of part 11, with respect to records required to be 164 maintained under predicate rules or submitted to FDA, when persons choose to use records in 165 electronic format in place of paper format, part 11 would apply. On the other hand, when 166 persons use computers to generate paper printouts of electronic records, and those paper records 167 meet all the requirements of the applicable predicate rules and persons rely on the paper records 168 to perform their regulated activities, FDA would generally not consider persons to be \"using 169 electronic records in lieu of paper records\" under §§ 11.2(a) and 11.2(b). In these instances, the 170 use of computer systems in the generation of paper records would not trigger part 11. 171 172 2. Definition of Part 11 Records 173 174 Under this narrow interpretation, FDA considers part 11 to be applicable to the following records 175 or signatures in electronic format (part 11 records or signatures): 176 177 • Records that are required to be maintained under predicate rule requirements and that are 178 maintained in electronic format in place of paper format. On the other hand, records (and 179 any associated signatures) that are not required to be retained under predicate rules, but 180 that are nonetheless maintained in electronic format, are not part 11 records. 181 We recommend that you determine, based on the predicate rules, whether specific records 182 are part 11 records. We recommend that you document such decisions. 183 184 • Records that are required to be maintained under predicate rules, that are maintained in 185 electronic format in addition to paper format, and that are relied on to perform regulated 186 activities. 187 In some cases, actual business practices may dictate whether you are using electronic 188 records instead of paper records under § 11.2(a). For example, if a record is required to 189 be maintained under a predicate rule and you use a computer to generate a paper printout 190 of the electronic records, but you nonetheless rely on the electronic record to perform 191 regulated activities, the Agency may consider you to be using the electronic record 192 instead of the paper record. That is, the Agency may take your business practices into 193 account in determining whether part 11 applies. 194 Accordingly, we recommend that, for each record required to be maintained under 195 predicate rules, you determine in advance whether you plan to rely on the electronic 196 record or paper record to perform regulated activities. We recommend that you 197 document this decision (e.g., in a Standard Operating Procedure (SOP), or specification 198 document). 199 • Records submitted to FDA, under predicate rules (even if such records are not 200 specifically identified in Agency regulations) in electronic format (assuming the records 201 have been identified in docket number 92S-0251 as the types of submissions the Agency 202 accepts in electronic format). However, a record that is not itself submitted, but is used 203 Contains Nonbinding Recommendations 6 in generating a submission, is not a part 11 record unless it is otherwise required to be 204 maintained under a predicate rule and it is maintained in electronic format. 205 • Electronic signatures that are intended to be the equivalent of handwritten signatures, 206 initials, and other general signings required by predicate rules. Part 11 signatures include 207 electronic signatures that are used, for example, to document the fact that certain events 208 or actions occurred in accordance with the predicate rule (e.g. approved, reviewed, and 209 verified). 210 211 C. Approach to Specific Part 11 Requirements 212 213 1.", "sources": [ "Part_11_Electronic_Records.pdf" ], "file": "Part_11_Electronic_Records.pdf", "type": "pdf" }, { "term": "Validation", "definition": "214 215 The Agency intends to exercise enforcement discretion regarding specific part 11 requirements 216 for validation of computerized systems (§ 11.10(a) and corresponding requirements in § 11.30). 217 Although persons must still comply with all applicable predicate rule requirements for validation 218 (e.g., 21 CFR 820.70(i)), this guidance should not be read to impose any additional requirements 219 for validation. 220 221 We suggest that your decision to validate computerized systems, and the extent of the validation, 222 take into account the impact the systems have on your ability to meet predicate rule 223 requirements. You should also consider the impact those systems might have on the accuracy, 224 reliability, integrity, availability, and authenticity of required records and signatures. Even if 225 there is no predicate rule requirement to validate a system, in some instances it may still be 226 important to validate the system. 227 228 We recommend that you base your approach on a justified and documented risk assessment and 229 a determination of the potential of the system to affect product quality and safety, and record 230 integrity. For instance, validation would not be important for a word processor used only to 231 generate SOPs. 232 233 For further guidance on validation of computerized systems, see FDA’s guidance for industry 234 and FDA staff General Principles of Software Validation and also industry guidance such as the 235 GAMP 4 Guide (See References). 236 237 2.", "sources": [ "Part_11_Electronic_Records.pdf" ], "file": "Part_11_Electronic_Records.pdf", "type": "pdf" }, { "term": "Audit Trail", "definition": "238 239 The Agency intends to exercise enforcement discretion regarding specific part 11 requirements 240 related to computer-generated, time-stamped audit trails (§ 11.10 (e), (k)(2) and any 241 corresponding requirement in §11.30). Persons must still comply with all applicable predicate 242 rule requirements related to documentation of, for example, date (e.g., § 58.130(e)), time, or 243 sequencing of events, as well as any requirements for ensuring that changes to records do not 244 obscure previous entries. 245 246 Even if there are no predicate rule requirements to document, for example, date, time, or 247 sequence of events in a particular instance, it may nonetheless be important to have audit trails or 248 other physical, logical, or procedural security measures in place to ensure the trustworthiness and 249 Contains Nonbinding Recommendations 7 reliability of the records.6 We recommend that you base your decision on whether to apply audit 250 trails, or other appropriate measures, on the need to comply with predicate rule requirements, a 251 justified and documented risk assessment, and a determination of the potential effect on product 252 quality and safety and record integrity. We suggest that you apply appropriate controls based on 253 such an assessment. Audit trails can be particularly appropriate when users are expected to 254 create, modify, or delete regulated records during normal operation. 255 256 3.", "sources": [ "Part_11_Electronic_Records.pdf" ], "file": "Part_11_Electronic_Records.pdf", "type": "pdf" }, { "term": "Legacy Systems7", "definition": "257 258 The Agency intends to exercise enforcement discretion with respect to all part 11 requirements 259 for systems that otherwise were operational prior to August 20, 1997, the effective date of part 260 11, under the circumstances specified below. 261 262 This means that the Agency does not intend to take enforcement action to enforce compliance 263 with any part 11 requirements if all the following criteria are met for a specific system: 264 265 • The system was operational before the effective date. 266 • The system met all applicable predicate rule requirements before the effective date. 267 • The system currently meets all applicable predicate rule requirements. 268 • You have documented evidence and justification that the system is fit for its intended use 269 (including having an acceptable level of record security and integrity, if applicable). 270 271 If a system has been changed since August 20, 1997, and if the changes would prevent the 272 system from meeting predicate rule requirements, Part 11 controls should be applied to Part 11 273 records and signatures pursuant to the enforcement policy expressed in this guidance. 274 275 4.", "sources": [ "Part_11_Electronic_Records.pdf" ], "file": "Part_11_Electronic_Records.pdf", "type": "pdf" }, { "term": "Copies of Records", "definition": "276 277 The Agency intends to exercise enforcement discretion with regard to specific part 11 278 requirements for generating copies of records (§ 11.10 (b) and any corresponding requirement in 279 §11.30). You should provide an investigator with reasonable and useful access to records during 280 an inspection. All records held by you are subject to inspection in accordance with predicate 281 rules (e.g., §§ 211.180(c), (d), and 108.35(c)(3)(ii)). 282 283 We recommend that you supply copies of electronic records by: 284 285 • Producing copies of records held in common portable formats when records are 286 maintained in these formats 287 • Using established automated conversion or export methods, where available, to make 288 copies in a more common format (examples of such formats include, but are not limited 289 to, PDF, XML, or SGML) 290 6 Various guidance documents on information security are available (see References). 7 In this guidance document, we use the term legacy system to describe systems already in operation before the effective date of part 11. Contains Nonbinding Recommendations 8 In each case, we recommend that the copying process used produces copies that preserve the 291 content and meaning of the record. If you have the ability to search, sort, or trend part 11 292 records, copies given to the Agency should provide the same capability if it is reasonable and 293 technically feasible. You should allow inspection, review, and copying of records in a human 294 readable form at your site using your hardware and following your established procedures and 295 techniques for accessing records. 296 297 5.", "sources": [ "Part_11_Electronic_Records.pdf" ], "file": "Part_11_Electronic_Records.pdf", "type": "pdf" }, { "term": "Record Retention", "definition": "298 299 The Agency intends to exercise enforcement discretion with regard to the part 11 requirements 300 for the protection of records to enable their accurate and ready retrieval throughout the records 301 retention period (§ 11.10 (c) and any corresponding requirement in §11.30). Persons must still 302 comply with all applicable predicate rule requirements for record retention and availability (e.g., 303 §§ 211.180(c),(d), 108.25(g), and 108.35(h)). 304 305 We suggest that your decision on how to maintain records be based on predicate rule 306 requirements and that you base your decision on a justified and documented risk assessment and 307 a determination of the value of the records over time. 308 309 FDA does not intend to object if you decide to archive required records in electronic format to 310 nonelectronic media such as microfilm, microfiche, and paper, or to a standard electronic file 311 format (examples of such formats include, but are not limited to, PDF, XML, or SGML). 312 Persons must still comply with all predicate rule requirements, and the records themselves and 313 any copies of the required records should preserve their content and meaning. As long as 314 predicate rule requirements are fully satisfied and the content and meaning of the records are 315 preserved and archived, you can delete the electronic version of the records. In addition, paper 316 and electronic record and signature components can co-exist (i.e., a hybrid8 situation) as long as 317 predicate rule requirements are met and the content and meaning of those records are preserved. 318 8 Examples of hybrid situations include combinations of paper records (or other nonelectronic media) and electronic records, paper records and electronic signatures, or handwritten signatures executed to electronic records. Contains Nonbinding Recommendations 9 319 IV.", "sources": [ "Part_11_Electronic_Records.pdf" ], "file": "Part_11_Electronic_Records.pdf", "type": "pdf" }, { "term": "REFERENCES", "definition": "320 321 Food and Drug Administration References 322 323 1. Glossary of Computerized System and Software Development Terminology (Division of 324 Field Investigations, Office of Regional Operations, Office of Regulatory Affairs, FDA 325 1995) (http://www.fda.gov/ora/inspect_ref/igs/gloss.html) 326 327 2. General Principles of Software Validation; Final Guidance for Industry and FDA Staff 328 (FDA, Center for Devices and Radiological Health, Center for Biologics Evaluation and 329 Research, 2002) (http://www.fda.gov/cdrh/comp/guidance/938.html) 330 331 3. Guidance for Industry, FDA Reviewers, and Compliance on Off-The-Shelf Software Use 332 in Medical Devices (FDA, Center for Devices and Radiological Health, 1999) 333 (http://www.fda.gov/cdrh/ode/guidance/585.html) 334 335 4. Pharmaceutical CGMPs for the 21st Century: A Risk-Based Approach; A Science and 336 Risk-Based Approach to Product Quality Regulation Incorporating an Integrated Quality 337 Systems Approach (FDA 2002) (http://www.fda.gov/oc/guidance/gmp.html) 338 339 340", "sources": [ "Part_11_Electronic_Records.pdf" ], "file": "Part_11_Electronic_Records.pdf", "type": "pdf" }, { "term": "Industry References", "definition": "341 342 1. The Good Automated Manufacturing Practice (GAMP) Guide for Validation of 343 Automated Systems, GAMP 4 (ISPE/GAMP Forum, 2001) (http://www.ispe.org/gamp/) 344 345 2. ISO/IEC 17799:2000 (BS 7799:2000) Information technology – Code of practice for 346 information security management (ISO/IEC, 2000) 347 348 3. ISO 14971:2002 Medical Devices- Application of risk management to medical devices 349 (ISO, 2001) 350 351 352", "sources": [ "Part_11_Electronic_Records.pdf" ], "file": "Part_11_Electronic_Records.pdf", "type": "pdf" }, { "term": "CDISC SDTM Implementation Guide (Version 3.1.2)", "definition": "APPENDIX E: REVISION HISTORY As stated above, this document, when approved, together with the SDTM, represents the most recent version of the CDISC Submission Data Domain Models, previously known as Version 3.1. The V3.x Study Data Tabulation Model Implementation Guide for Human Clinical Trials was the first implementation-ready version of the CDISC Submission Data Domain Models. E1: CHANGES FROM CDISC SDS STANDARDS VERSION 2 MODELS TO VERSION 3 SDTM V3 represented a major change from the CDISC Version 2.0 (V2) domain models for two primary reasons. First, it included a general model for representing all types of tabulation data (the general model has now been published separately as the SDTM). Second, the general vertical record structures used in V3 were more normalized than the more horizontal V2 record structures. Standard horizontal listings of data, such as those described in the V2 horizontal representations of ECGs and Vitals by visit, will be produced by FDA standard review tools. V3 was initially released for comment in March 2003, and finalized as an approved HL7 informative document that addressed all prior comments on June 9, 2003. V3 was then tested in Summer 2003 by individuals from nine sponsor companies who participated in an FDA pilot project. The results of the pilot (which are discussed in Section 10.5) were shared with industry at an FDA public meeting held on October 2, 2003, and feedback from the pilot was a primary input to V3.x. Another key input was a list of comments that had to be deferred for the June 9, 2003 publication, but which were addressed in V3.1 and later versions. E2: CHANGES FROM CDISC SDTMIG V3 TO V3.1 • The general underlying conceptual model described in V3 as the General Study Data Information Model, was published separately as the SDTM, a separate document from this implementation guide. • Corrections and amendments applied in the SDTM were also applied throughout the domain models, assumptions, and examples provided in this document. • A new Trial Design component was incorporated with examples to provide a standardized representation of study timing and treatment plans, and a way of representing actual subject visits. • A more thorough solution was included for defining relationships between datasets, domains, and/or records. • The representation of all date/time variables was changed to ISO 8601 character format. The concept of a separate Date/Time Precision variable for each date/time variable was eliminated in this version, because that purpose is met by the ISO 8601 standard. • New domain variables were added to represent additional timing descriptions, flags, and descriptive attributes of an observation (e.g., --SCAT, --DOSRGM, --NRIND). • Some variables were removed from the domain models (e.g., --INTP, --DESC, --BLRESC, --BLRESN), either because they were deprecated in the prior version or were inconsistent with the intent of the model. • The core variable concept was expanded to categorize variables as required, expected, or permissible. • The Qualifier variable role was sub-categorized into five more granular subcategories (See Section 2.1) to provide more detail on the use of variables. • The SU, DS, and PE domains were significantly redesigned. CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Prepared by the", "definition": "CDISC Submission Data Standards Team", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Notes to Readers", "definition": "• This is the implementation guide for Human Clinical Trials corresponding to Version 1.2 of the CDISC Study Data Tabulation Model. • This Implementation Guide comprises version 3.1.2 of the CDISC Submission Data Standards and domain models.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Summary of Changes", "definition": "2004-07-14 3.1 Released version reflecting all changes and corrections identified during comment periods. 2005-08-26 3.1.1 Final Released version reflecting all changes and corrections identified during comment period. 2007-07-25 3.1.2 Draft for Public", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Comments", "definition": "co.xpt One record per comment per", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Draft", "definition": "CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 2", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "INTRODUCTION................................................................................................... 7", "definition": "1.1 PURPOSE.............................................................................................................................................................7 1.2 ORGANIZATION OF THIS DOCUMENT...................................................................................................................8 1.3 RELATIONSHIP TO PRIOR CDISC DOCUMENTS ...................................................................................................8 1.4 HOW TO READ THIS IMPLEMENTATION GUIDE ....................................................................................................9 1.5 SUBMITTING COMMENTS....................................................................................................................................9 2 FUNDAMENTALS OF THE SDTM...................................................................... 10 2.1 OBSERVATIONS AND VARIABLES .......................................................................................................................10 2.2 DATASETS AND DOMAINS .................................................................................................................................11 2.3 SPECIAL-PURPOSE DATASETS...........................................................................................................................11 2.4 THE GENERAL OBSERVATION CLASSES.............................................................................................................12 2.4.1 The Interventions Observation Class...................................................................................................13 2.4.2 The Events Observation Class .............................................................................................................14 2.4.3 The Findings Observation Class..........................................................................................................15 2.4.4 Identifier Variables for All Classes ......................................................................................................17 2.4.5 Timing Variables for All Classes..........................................................................................................18 2.5 THE SDTM STANDARD DOMAIN MODELS .......................................................................................................19 2.6 CREATING A NEW DOMAIN ...............................................................................................................................20 3 SUBMITTING DATA IN STANDARD FORMAT.................................................. 22 3.1 STANDARD METADATA FOR DATASET CONTENTS AND ATTRIBUTES..................................................................22 3.2 USING THE CDISC DOMAIN MODELS IN REGULATORY SUBMISSIONS - DATASET METADATA ..........................23 3.2.1 CDISC Submission Dataset Definition Metadata Example.................................................................23 3.2.1.1 Primary Keys .......................................................................................................................................25 3.2.2 CDISC Submission Value-Level Metadata..........................................................................................26 3.2.3 Conformance........................................................................................................................................26 4 ASSUMPTIONS FOR DOMAIN MODELS .......................................................... 27 4.1 GENERAL ASSUMPTIONS FOR ALL DOMAINS ....................................................................................................27 4.1.1 General Domain Assumptions .............................................................................................................27 4.1.1.1 Review Study Data Tabulation and Implementation Guide.................................................................27 4.1.1.2 Relationship to Analysis Datasets........................................................................................................27 4.1.1.3 Additional Timing Variables................................................................................................................27 4.1.1.4 Order of the Variables..........................................................................................................................27 4.1.1.5 CDISC Core Variables.........................................................................................................................27 4.1.1.6 Additional Guidance on Dataset Naming ............................................................................................28 4.1.1.7 Origin Metadata...................................................................................................................................29 4.1.1.8 Assigning Natural Keys in the Metadata .............................................................................................30 4.1.2 General Variable Assumptions.............................................................................................................32 4.1.2.1 Variable-Naming Conventions.............................................................................................................32 4.1.2.2 Two-Character Domain Identifier........................................................................................................32 4.1.2.3 Use of 'Subject' and USUBJID ............................................................................................................32 4.1.2.4 Case Use of Text in Submitted Data ....................................................................................................33 4.1.2.5 Convention for Missing Values............................................................................................................33 4.1.2.6 Grouping Variables and Categorization ...............................................................................................33 4.1.2.7 Submitting Free Text from the CRF ....................................................................................................35 4.1.2.8 Multiple Values for a Variable .............................................................................................................37 4.1.3 Coding and Controlled Terminology Assumptions..............................................................................39 4.1.3.1 Types of Controlled Terminology........................................................................................................39 CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 4", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 6", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 8", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 10", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 12", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Topic", "definition": "Short name for the Parameter described in TSPARM. The value in TSPARMCD cannot be longer than 8 characters, nor can it start with a number. TSPARMCD cannot contain characters other than letters, numbers, or underscores. Examples: DESIGN, MASK, COMPTRT", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Synonym of --TRT", "definition": "Standardized or dictionary-derived name of the topic variable, --TRT, or the modified topic variable (--MODIFY), if applicable. Equivalent to the generic drug name in WHO Drug, or a term in SNOMED, ICD9, or other published or sponsor- defined dictionaries. --CAT", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Grouping", "definition": "Used to define a further categorization level for a group of --CAT values. Example: DIFFERENTIAL. --POS", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Record", "definition": "Describes the severity or intensity of a particular finding. Examples: MILD, MODERATE, SEVERE. --LLOQ", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Variable Qualifier of", "definition": "--VAMT Units for the treatment vehicle. Examples: mL, puffs. --ADJ", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 14", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "of --TERM", "definition": "Dictionary or sponsor-defined derived text description of the topic variable, --TERM, or the modified topic variable (--MODIFY), if applicable. Equivalent to the Preferred Term (PT in MedDRA). --CAT", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "of", "definition": "--TESTCD LOINC Code for the topic variable such as a lab test. --SPEC Specimen Material Type", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Qualifier", "definition": "Value of TSPARM. Example: 'ASTHMA' when TSPARM value is 'Trial Indications'. TSVAL cannot be null – a value is required for the record to be valid. The first 200 characters of TSVAL will be in TSVAL, then next 200 in TSVAL1, and continuing as needed to TSVALn.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Qualifier of", "definition": "--STRESN Indicates the lower limit of quantitation for an assay. Units will be those used for --STRESU. CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "and", "definition": "--STRESN Standardized units used for --STRESC and --STRESN. Example: mmol/L. --BODSYS", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 16", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char", "definition": "Since QVAL can represent a mixture of collected (on a CRF), derived, or assigned items, QORIG is used to indicate the origin of this data. Examples include CRF, ASSIGNED, or DERIVED. See Section 4.1.1.7.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Num", "definition": "Identifier Sequence number given to ensure uniqueness within a dataset. Allows inclusion of multiple records for the same TSPARMCD, and can be used to join related records.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 18", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 20", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 22", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Standard Format", "definition": "3.1 STANDARD METADATA FOR DATASET CONTENTS AND", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ATTRIBUTES", "definition": "The SDTMIG provides standard descriptions of some of the most commonly used data domains, using the metadata attributes originally described in the CDISC Submission Metadata Model. The descriptive metadata attributes that should be included in a submission dataset definition file as applied in the domain models are: • The SDTMIG -standard variable name (standardized for all submissions, even though sponsors may be using other variable names internally in their operational database) • The SDTMIG -standard variable label • Expected data types (the SDTMIG uses character or numeric to conform to the data types consistent with SAS V5 transport file format, but Define.xml allows for more descriptive data types, such as integer or float) • The actual controlled terms and formats used by the sponsor (do not include the asterisk (*) included in the CDISC domain models to indicate when controlled terminology applies) • The origin or source of the data (e.g., CRF, derived - see definitions in Section 4.1.1.7) • The role of the variable in the dataset corresponding to the role in the SDTM if desired. Since these roles are predefined for all standard domains that follow the general observation classes, they do not need to be specified by sponsors in their Define data definition document for these domains.) • Any Comments provided by the sponsor that may be useful to the Reviewer in understanding the variable or the data in it. In addition to these metadata attributes, the CDISC domain models include three other shaded columns that are not sent to the FDA in order to assist sponsors in preparing their datasets — one column for notes relevant to the use of each variable, one to indicate how a variable is classified as a CDISC Core Variable (see Section 4.1.1.5), and one to provide references to relevant section of the SDTM or the SDTMIG. See the Define.xml specification for information about additional metadata attributes required in that standard. The domain models in Section 6 illustrate how to apply the SDTM when creating a specific domain dataset. In particular, these models illustrate the selection of a subset of the variables offered in one of the general observation classes along with applicable timing variables. The models also show how a standard variable from a general observation class should be adjusted to meet the specific content needs of a particular domain, including making the label more meaningful, specifying controlled terminology, and creating domain-specific notes and examples. Thus the domain models demonstrate not only how to apply the model for the most common domains, but also give insight on how to apply general model concepts to other domains not yet defined by CDISC. CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Demographics", "definition": "dm.xpt One record per subject", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Subject Elements", "definition": "se.xpt One record per actual Element", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Subject Visits", "definition": "sv.xpt One record per actual visit per", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Medications", "definition": "Interventions See Section 6.1.1.1, Assumption 1. Included as a value in the list of controlled terms, codelist Domain Abbreviation (C66734) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Exposure", "definition": "Interventions See Section 6.1.2.1, Assumption 1. Included as a value in the list of controlled terms, codelist Domain Abbreviation (C66734) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Substance Use", "definition": "Interventions See Section 6.1.3.1, Assumption 1 Included as a value in the list of controlled terms, codelist Domain Abbreviation (C66734) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Adverse Events", "definition": "ae.xpt One record per adverse event", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Disposition", "definition": "ds.xpt One record per disposition status or protocol milestone per", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Medical History", "definition": "mh.xpt One record per medical history", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Deviations", "definition": "dv.xpt One record per protocol", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DVSTDTC", "definition": "CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 24", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Clinical Events", "definition": "ce.xpt One record per event per", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "One record per ECG", "definition": "observation per time point per", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Results", "definition": "--VSORRESU, VSSTRESU Populated using a code value in the list of controlled terms, codelist VSRESU (C66770) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Tabulation", "definition": "STUDYID, RDOMAIN, USUBJID, IDVAR, IDVARVAL,", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Examination", "definition": "pe.xpt One record per body system or abnormality per visit per", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Questionnaires", "definition": "qs.xpt One record per question per questionnaire per time point per", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Characteristics", "definition": "sc.xpt One record per characteristic", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Vital Signs", "definition": "vs.xpt One record per vital sign measurement per time point per", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Specimen", "definition": "mb.xpt One record per microbiology specimen finding per time point", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Test", "definition": "ms.xpt One record per microbiology susceptibility test (or other organism-related finding) per", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Concentrations", "definition": "pc.xpt One record per time-point concentration or sample characteristic per analyte per", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PCTPTREF", "definition": "CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Parameters", "definition": "pp.xpt One record per PK parameter per time-concentration profile per modeling method per", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Clinical Findings", "definition": "cf.xpt One record per finding per subject per visit per time point", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Trial Arms", "definition": "Trial Design See SDTM 3.2. Included as a value in the list of controlled terms, codelist Domain Abbreviation (C66734) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Trial Elements", "definition": "Trial Design See SDTM 3.2. Included as a value in the list of controlled terms, codelist Domain Abbreviation (C66734) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Trial Visits", "definition": "Trial Design See SDTM 3.2. Included as a value in the list of controlled terms, codelist Domain Abbreviation (C66734) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Exclusion Criteria", "definition": "Trial Design See SDTM 3.4. Included as a value in the list of controlled terms, codelist Domain Abbreviation (C66734) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Trial Summary", "definition": "Trial Design See SDTM 3.5. Included as a value in the list of controlled terms, codelist Domain Abbreviation (C66734) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Related Records", "definition": "relrec.xpt One record per related record, group of records or datasets", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Qualifiers for", "definition": "[domain name] suppqual.xpt or supp--.xpt One record per IDVAR, IDVARVAL, and QNAM per", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "QNAM", "definition": "†Separate Supplemental Qualifier tables of the form supp--.xpt are recommended. See Section 8.4. 3.2.1.1", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PRIMARY KEYS", "definition": "Table 3.2.1 above shows examples of what a sponsor might submit as variables that comprise the primary key for SDTM datasets. Since the purpose of this column is to aid reviewers in understanding the structure of a dataset, sponsors should list all of the natural keys (see definition below) for the dataset. These keys should define uniqueness for records within a dataset, and may define a record sort order. The naming of these keys should be consistent with the description of the structure in the Structure column. For all the general-observation-class domains (and for some special-purpose domains), the --SEQ variable was created so that a unique record could be identified consistently across all of these domains via its use, along with STUDYID, USUBJID, DOMAIN. In most domains, --SEQ will be a surrogate key (see definition below) for a set of variables which comprise the natural key. In certain instances, a Supplemental Qualifier (SUPP--) variable might also contribute to the natural key of a record for a particular domain. See assumption 4.1.1.8 for how this should be represented, and for additional information on keys. A natural key is a piece of data (one or more columns of an entity) that uniquely identify that entity, and distinguish it from any other row in the table. A natural key is controlled from outside the database environment. The advantage of natural keys is that they exist already, and one does not need to introduce a new “unnatural” value to the data schema. One of the difficulties in choosing a natural key is that just about any natural key one can think of has the potential to change. Because they have business meaning, natural keys are effectively coupled to the business, and they may need to be reworked when business requirements change. An example of such a change in clinical trials CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 26", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Topic variable", "definition": "• Conforming to all business rules described in the CDISC Notes column and general and domain-specific assumptions. CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Models", "definition": "4.1 GENERAL ASSUMPTIONS FOR ALL DOMAINS 4.1.1 GENERAL DOMAIN ASSUMPTIONS 4.1.1.1 REVIEW STUDY DATA TABULATION AND IMPLEMENTATION GUIDE Review the Study Data Tabulation Model as well as this Implementation Guide before attempting to use any of the individual domain models. See the Case Report Tabulation Data Definition Specification (define.xml), available on the CDISC website, for information about an xml representation of the Define data definition document. 4.1.1.2 RELATIONSHIP TO ANALYSIS DATASETS Specific guidance on preparing analysis datasets can be found in the CDISC Analysis Dataset Model General Considerations document, available at http://www.cdisc.org/models/adam/V2.0/index.html . 4.1.1.3 ADDITIONAL TIMING VARIABLES Additional Timing variables (Section 2.4.5) can be added as needed to a standard domain model based on the three general observation classes except where discouraged in Assumption 4.1.4.8 and specific domain assumptions. Timing variables can be added to special-purpose domains only where specified in the SDTMIG domain model assumptions. Timing variables cannot be added to SUPPQUAL datasets or to RELREC (described in Section 8). 4.1.1.4 ORDER OF THE VARIABLES The order of variables in the Define data definition document should reflect the order of variables in the dataset. The current order of variables in the CDISC domain models has been chosen to facilitate the review of the models and application of the models. Sponsors may thus wish to reorder Timing and Qualifier variables in order to place more emphasis on the most important variables, but are encouraged to apply a consistent variable-ordering scheme for all domains in a submission wherever possible. 4.1.1.5", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CDISC CORE VARIABLES", "definition": "The concept of core variable is used both as a measure of compliance, and to provide general guidance to sponsors Three categories of variables are specified in the 'Core' column in the domain models: • A Required variable is any variable that is basic to the identification of a data record (i.e., essential key variables and a topic variable) or is necessary to make the record meaningful. Required variables must always be included in the dataset and cannot be null for any record. • An Expected variable is any variable necessary to make a record useful in the context of a specific domain. Columns for Expected variables must be present in each submitted dataset even if all values are null. Expected variables may contain some null values, but in most cases will not contain null values for every record; however, when no data has been collected for an expected variable, a null column should still be included in the dataset, and a comment should be included in the Define data definition document to state that data was not collected. • A Permissible variable should be used in a domain as appropriate when collected or derived. Except where restricted by specific domain assumptions, any SDTM Timing and Identifier variables, and any Qualifier variables from the same general observation class are permissible for use in a domain based on that general observation class. The Sponsor can decide whether a Permissible variable should be included as a column when all values for that variable are null. CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 28", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ORIGIN METADATA", "definition": "4.1.1.7.1 ORGIN METADATA FOR VARIABLES The Origin column of the Define data definition document is used to indicate where the data originated. Its purpose is to unambiguously communicate to the reviewer whether data was collected on a CRF (and thus should be traceable to an annotated CRF), derived (and thus traceable to some derivation algorithm), or assigned by some subjective process (and thus traceable to some external evaluator). The SDTMIG defines the following controlled terms for specifying Origin: CRF: The designation of ”CRF” (along with a reference) as an origin in the define document means that data was collected as part of a CRF and that there is an annotated CRF associated with the variable. Sponsors may specify additional details about the origin that may be helpful to the Reviewer (e.g., diary, electronic device) in the Comments section of the define document. An origin of “CRF” includes information that is preprinted on the CRF (e.g., “RESPIRATORY SYSTEM DISORDERS” for MHCAT). eDT: The designation of \"eDT\" as an origin in the Define document means that the data are received via an electronic Data Transfer (eDT) and usually does not have associated annotations. An origin of eDT refers to data collected via data streams such as laboratory, ECG, or IVRS. Sponsors may specify additional details about the origin that may be helpful to the Reviewer in the Comments section of the Define document. Derived: Derived data are not directly collected on the CRF but are calculated by an algorithm or reproducible rule, which often involves other data values. This algorithm is applied across all values and may reference other datasets (such as analysis datasets). The derivation is assumed to be performed by the Sponsor. This does not apply to derived lab test results performed directly by labs (or by devices). Examples illustrating the distinction between collected and derived values include the following: • A value derived by an eCRF system from other entered fields has an origin of \"Derived, \" since the sponsor controls the derivation. • A value derived from collected data by the sponsor, or a CRO working on their behalf, has an origin of \"Derived.\" This is true whether the derivation is done in an operational database or an analysis program. • A value derived by an investigator and written/entered on a CRF has an origin of \"CRF\" (along with a reference) rather than “derived”. • A value derived by a vendor (e.g., a central lab) according to their procedures is considered collected rather than derived, and would have an origin of “eDT”. Assigned: A value that is determined by individual judgment (by an evaluator other than the subject or investigator), rather than collected as part of the CRF or derived based on an algorithm. This may include third party attributions by an adjudicator. Coded terms that are supplied as part of a coding process (as in --DECOD) are considered to have an Origin of “Assigned”. Protocol: A value that is defined as part of the Trial Design preparation (see Section 7). An example would be VSPOS (Vital Signs Position), which may specified only in the protocol and not appear on a CRF. The term “Sponsor Defined” was used in earlier versions of the SDTMIG to advise the Sponsor to supply the appropriate Origin value in the metadata. The text “Sponsor Defined” was not intended to be used in the Define data definition document and is no longer used in version 3.1.2 and later. See the SDTM Metadata Implementation Guide for additional details on representing Origin metadata. CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 30", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rotation of the hand", "definition": "• Method of measurement (X-ray / MRI) •", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Machine Make", "definition": "CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Machine Model", "definition": "Trying to encapsulate all of this information within a unique value of a --TESTCD results in the creation of a potentially large number of test codes, with the eight-character values of --TESTCD becoming less intuitively meaningful. Additionally, multiple test codes are essentially representing the same test or measurement simply to accommodate attributes of a test within the --TESTCD value itself (e.g., to represent a body location at which a measurement was taken). Here is an example of what a compound --TESTCD might look like, it represents the measurement of erosion in the left metacarpal using an ultrasound technique to make the measurement:", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "LMCP1EA2", "definition": "This code breaks down as follows: LMCP1: represents the “Location” of the test - “Left Metacarpal 1” E: represents the “Test” – “Erosion” A: represents the evaluation machine “Make” – “Acme” 2: represents the evaluation machine “Model” – “u 2.1” A second approach for depicting a unique test would be to use a generic (or simple) test code that requires associated qualifier variables to fully express the test detail. Using this approach in the above example, a generic --TESTCD value might be “EROSION” and the additional components of the compound test codes discussed above would be represented in a number of distinct qualifier variables. These may include domain variables (--LOC, --METHOD, etc.) and Supplemental Qualifier variables (QNAM.MAKE, QNAM.MODEL, etc.). Expressing the natural key becomes very important in this situation in order to communicate the variables that contribute to the uniqueness of a test. If a generic --TESTCD was used the following variables would be used to fully describe the test. The test is “EROSION”, the location is “Left MCP I”, the method of measurement is “Ultrasound”, the make of the ultrasound machine is “ACME” and the model of the ultrasound machine is “u 2.1”. This domain includes both domain variables and Supplemental Qualifier variables that contribute to the natural key of each row and to describe the uniqueness of the test. --TESTCD --TEST --LOC --METHOD QNAM.MAKE QNAM.MODEL", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ACME", "definition": "U 2.1 CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 32", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CT1234-114-007", "definition": "004 211 4.1.2.4 CASE USE OF TEXT IN SUBMITTED DATA It is recommended that text data be submitted in upper case text. Exceptions may include long text data (such as comment text); values of --TEST in Findings datasets (which may be more readable in mixed case if used as labels in transposed views); and certain controlled terminology (see Section 4.1.4.3) that are already in mixed case. The Sponsor’s Define data definition document may indicate as a general note or assumption whether case sensitivity applies to text data for any or all variables in the dataset. 4.1.2.5 CONVENTION FOR MISSING VALUES Missing values for individual data items should be represented by nulls. This is a change from previous versions of the SDTMIG, which allowed sponsors to define their conventions for missing values. Conventions for representing observations not done using the SDTM --STAT and --REASND variables are addressed in Section 4.1.5.1.1 and the individual domain models. 4.1.2.6 GROUPING VARIABLES AND CATEGORIZATION Grouping variables are Identifiers and Qualifiers that group records in the SDTM domains/datasets such as the --CAT and --SCAT variables assigned by sponsors to categorize data. For example, a lab record with LBTEST = 'SODIUM' might have LBCAT = 'CHEMISTRY' and LBSCAT = 'ELECTROLYTES'. Values for --CAT and --SCAT should not be redundant with the domain or dictionary classification provided by --DECOD and --BODSYS. 1. Hierarchy of Grouping Variables", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "USUBJID", "definition": "Unique Subject Identifier Char Identifier Unique subject identifier within the submission.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "For the subject", "definition": "All records with the same USUBJID value are a group of records that describe that subject. b. Across subjects (records with different USUBJID values) 1. All records with the same STUDYID value are a group of records that describe that study 2. All records with the same DOMAIN value are a group of records that describe that domain 3. --CAT (Category) and --SCAT (Sub-category) values further subset groups within the domain. Generally, --CAT/--SCAT values have meaning within a particular domain. However, it is possible to use the same values for --CAT/--SCAT in related domains (e.g., MH and AE). When values are CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 34", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Non-medical reason", "definition": "_______________ The free text description should be stored in the SUPPEX dataset. • EXADJ=NONMEDICAL REASON • SUPPEX QNAM=EXADJOTH, QLABEL=Other Reason For Dose Adjustment, QVAL=PATIENT MISUNDERSTOOD INSTRUCTIONS ƒ Note that QNAM references the “parent” variable name with the addition of “OTH, ” one of the standard variable naming fragments for “Other” (see Appendix D). Likewise, the label is a modification of the parent variable label. When the CRF includes a list of values for a qualifier field that includes \"Other\" and the \"Other\" is supplemented with a \"Specify\" free text field, then the manner in which the free text \"Specify\" value is submitted will vary based on the sponsor's coding practice and analysis requirements. For example, consider a CRF that collects the anatomical location of administration (EXLOC) of a study drug given as an injection:", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Left Thigh", "definition": "Other, Specify: _________________ CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 36", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "RIGHT ABDOMEN", "definition": "• Occasionally, the sponsor may wish to use controlled terminology for the specify field as well as the location field. Suppose “UPPER RIGHT ABDOMEN” is coded to “ABDOMEN.” If the sponsor wishes to submit both the controlled term as well as the original term, an additional record could be included in SUPPEX. In this case, the QNAM value might make use of the standard variable naming fragment for “Original.” ƒ EXLOC=OTHER ƒ SUPPEX QNAM=EXLOCOTH, QLABEL= Other Location Of Dose Administration, QVAL=ABDOMEN ƒ SUPPEX QNAM=EXLOCOR, QLABEL=Original Location Of Dose Administration, QVAL=UPPER RIGHT ABDOMEN 2) If the sponsor wishes to maintain controlled terminology for EXLOC but will expand the terminology based on values seen in the specify field, then the value of EXLOC will reflect the sponsor’s coding decision and SUPPEX could be used to store the verbatim text. • EXLOC=ABDOMEN • SUPPEX QNAM= EXLOCOTH, QVAL=UPPER RIGHT ABDOMEN ƒ Note that the sponsor might choose a different value for EXLOC (e.g., UPPER ABDOMEN, TORSO) depending on the sponsor's coding practice and analysis requirements. 3) If the sponsor does not require that controlled terminology be maintained and wishes for all responses to be stored in a single variable, then EXLOC will be used and SUPPEX is not required. In this case, the sponsor might have provided the pre-specified choices as a convenience to the investigator. • EXLOC= UPPER RIGHT ABDOMEN 4.1.2.7.2 “SPECIFY” VALUES FOR RESULT QUALIFIER VARIABLES: When the CRF includes a list of values for a result field that includes \"Other\" and the \"Other\" is supplemented with a \"Specify\" free text field, then the manner in which the free text \"Specify\" value is submitted will vary based on the sponsor's coding practice and analysis requirements. For example, consider a CRF where the sponsor requests the subject's eye color:", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Green", "definition": "Other, specify: ________ An investigator has selected \"OTHER\" and specified \"BLUEISH GRAY.\" As in the above discussion for non-result Qualifier values, the sponsor has several options for submission: 1) If the sponsor wishes to maintain controlled terminology in the standard result field and limit the terminology to the 5 pre-specified choices, then the free text is placed in --ORRES and the controlled terminology in --STRESC. • SCTEST=Eye Color, SCORRES=BLUEISH GRAY, SCSTRESC=OTHER 2) If the sponsor wishes to maintain controlled terminology in the standard result field, but will expand the terminology based on values seen in the specify field, then the free text is placed in --ORRES and the value of --STRESC will reflect the sponsor's coding decision. • SCTEST=Eye Color, SCORRES=BLUEISH GRAY, SCSTRESC=GRAY 3) If the sponsor does not require that controlled terminology be maintained, the verbatim value will be copied to --STRESC. • SCTEST=Eye Color, SCORRES=BLUEISH GRAY, SCSTRESC=BLUEISH GRAY CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Naproxen", "definition": "Other: ______ If ibuprofen and diclofenac were reported, the CM dataset would include the following: • CMTRT=IBUPROFEN, CMPRESP=Y • CMTRT=DICLOFENAC, CMPRESP=N Events: “Other, Specify” for events may be handled similarly to interventions. --TERM should be populated with the description of the event found in the specified text and --PRESP could be used to distinguish between pre-specified and free text responses. Findings: “Other, Specify” for tests may be handled similarly to interventions. --TESTCD and --TEST should be populated with the code and description of the test found in the specified text. 4.1.2.8 MULTIPLE VALUES FOR A VARIABLE 4.1.2.8.1 MULTIPLE VALUES FOR AN INTERVENTION OR EVENT TOPIC VARIABLE If multiple values are reported for a topic variable (i.e., --TRT in an Interventions general-observation-class dataset or -- TERM in an Events general-observation-class dataset), it is assumed that the sponsor will split the values into multiple records or otherwise resolve the multiplicity as per the sponsor’s standard data management procedures. For example, if an adverse event term of “Headache and Nausea” or a concomitant medication of “Tylenol and Benadryl” is reported, sponsors will often split the original report into separate records and/or query the site for clarification. By the time of submission, the datasets should be in conformance with the record structures described in the SDTM IG. Note that the Disposition dataset (DS) should not have cases for splitting multiple topic values into separate records, since DS allows only one record per disposition status or protocol milestone per subject. See Section 6.2.2.1 for additional information. 4.1.2.8.2 MULTIPLE VALUES FOR A FINDING RESULT VARIABLE If multiple result values (--ORRES) are reported for a test in a Findings class dataset, multiple records should be submitted for that --TESTCD. Example: • EGTESTCD=RHYRATE, EGTEST=Rhythm and Rate, EGORRES=ATRIAL FIBRILLATION • EGTESTCD=RHYRATE, EGTEST=Rhythm and Rate, EGORRES=ATRIAL FLUTTER Note that in this case, the sponsor’s operational database may have a result-sequence variable as part of the natural key. Some sponsors may elect to keep this variable in a Supplemental Qualifier record, while others may decide to use --SPID or --SEQ to replace it. Dependent variables such as result Qualifiers should never be part of the natural key. CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 38", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AELOC1", "definition": "Location of the Reaction 1", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AELOC2", "definition": "Location of the Reaction 2", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AELOC3", "definition": "Location of the Reaction 3", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CHEST", "definition": "In some cases, values for QNAM and QLABEL more specific than those above may be needed. For example, a sponsor might conduct a study with two study drugs (e.g., open-label study of Abcicin + Xyzamin), and has the investigator assess causality and describe action taken for each drug for the rash:", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DOSE NOT CHANGED", "definition": "Note that in the latter case the label for standard variables AEREL and AEACN will have no indication that they pertain to Abcicin. This association must be clearly documented in the metadata and annotated CRF. CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "VARIABLES", "definition": "• For events such as AEs and Medical History, populate --DECOD with the dictionary’s preferred term and populate --BODSYS with the preferred body system name. If a dictionary is multi-axial, the value in --BODSYS should represent the system organ class (SOC) used for the sponsor’s analysis and summary tables, which may not necessarily be the primary SOC. • For concomitant medications, populate CMDECOD with the drug's generic name and populate CMCLAS with the drug class used for the sponsor’s analysis and summary tables. If coding to multiple classes, follow assumption 4.1.2.8.1 or omit CMCLAS. In either case, no other intermediate levels or relationships should be stored in the dataset (although they may be provided in a Supplemental Qualifiers dataset). By knowing the dictionary and version used, the reviewer will be able to obtain intermediate levels in a hierarchy (as in MedDRA), or a drug’s ATC codes (as in WHO Drug). The dictionary version should be listed in the Comments column of the Define data Definition document. The sponsor may be required to submit the dictionary if it is not already available to the reviewer. CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 40", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PESTRESC", "definition": "Character Result/Finding in Std. Format", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 42", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Precision", "definition": "ISO 8601 Date/Time 1 December 15, 2003 13:14:17 Complete date/time 2003-12-15T13:14:17 2 December 15, 2003 13:14", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Unknown seconds", "definition": "2003-12-15T13:14 3 December 15, 2003 13 Unknown minutes and seconds 2003-12-15T13 4 December 15, 2003", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Unknown day and time", "definition": "2003-12 6 2003 Unknown month , day, and time 2003 This date and date/time model also provides for imprecise or estimated dates, such as those commonly seen in Medical History. To represent these intervals while applying the ISO 8601 standard, it is recommended that the sponsor concatenate the date/time values (using the most complete representation of the date/time known) that describe the beginning and the end of the interval of uncertainty and separate them with a solidus as shown in the table below: Interval of Uncertainty ISO 8601 Date/Time 1 Between 10:00 and 10:30 on the Morning of December 15, 2003 2003-12-15T10:00/2003-12-15T10:30 2 After the first of this year (2003) until \"now\" (February 15, 2003, noon) 2003-01-01/2003-02-15T12:00 3 Between the first and the tenth of December, 2003 2003-12-01/2003-12-10 4 Sometime in the first half of 2003 2003-01-01/2003-06-30 Other uncertainty intervals may be represented by the omission of components of the date when these components are unknown or missing. As mentioned above, ISO 8601 represents missing intermediate components through the use of a hyphen where the missing component would normally be represented. This may be used in addition to \"appropriate right truncations\" for incomplete date/time representations. When components are omitted, the expected delimiters must still be kept in place and only a single hyphen is to be used to indicate an omitted component. Examples of this method of omitted component representation are shown in the table below: Date and Time as Originally", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Level of Uncertainty", "definition": "ISO 8601 Date/Time 1 December 15, 2003 13:15:17", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Complete date", "definition": "2003-12-15T13:15:17 2 December 15, 2003 ??:15 Unknown hour with known minutes 2003-12-15T-:15 3 December 15, 2003 13:??:17 Unknown minutes with known date, hours,", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "and seconds", "definition": "2003-12-15T13:-:17 4 The 15th of some month in 2003, time", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "not collected", "definition": "Unknown month and time with known year", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "and day", "definition": "2003---15 5 December 15, but can't remember the year, time not collected Unknown year with known month and day --12-15 6 7:15 of some unknown date Unknown date with known hour and", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "minute", "definition": "-----T07:15 CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PnW", "definition": "where: • [P] (duration designator): precedes the alphanumeric text string that represents the duration. NOTE: The use of the character P is based on the historical use of the term \"period\" for duration. • [n] represents a positive integer or zero • [W] is used as week designator, preceding a data Element that represents the number of calendar weeks within the calendar year (e.g., P6W represents 6 weeks of calendar time). The letter \"P\" must precede other values in the ISO 8601 representation of duration. The 'n' preceding each letter represents the number of Years, Months, Days, Hours, Minutes, Seconds, or the number of Weeks. As with the date/time format, 'T' is used to separate the date components from time components. Note that weeks cannot be mixed with any other date/time components such as days or months in duration expressions. As is the case with the date/time representation in --DTC, --STDTC or --ENDTC only the components of duration that are known or collected need to be represented. Also, as is the case with the date/time representation, if no time component is represented, the [T] time designator (in addition to the missing time) must be omitted in ISO 8601 representation. CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 44", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "P3D", "definition": "6 Months 17 Days 3 Hours", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "P6M17DT3H", "definition": "14 Days 7 Hours 57 Minutes", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "One-half hour", "definition": "PT0.5H 5 Days 12¼ Hours P5DT12.25H 4 ½ Weeks P4.5W Note that a leading zero is required with decimal values less than one. 4.1.4.3.2 INTERVAL WITH UNCERTAINTY When an interval of time is an amount of time (duration) following an event whose start date/time is recorded (with some level of precision, i.e. when one knows the start date/time and the duration following the start date/time), the correct ISO 8601 usage to represent this interval is as follows: YYYY-MM-DDThh:mm:ss/PnYnMnDTnHnMnS where the start date/time is represented before the solidus [/], the \"Pn…\", following the solidus, represents a “duration”, and the entire representation is known as an “interval”. NOTE: This is the recommended representation of elapsed time, given a start date/time and the duration elapsed. When an interval of time is an amount of time (duration) measured prior to an event whose start date/time is recorded (with some level of precision, i.e. where one knows the end date/time and the duration preceding that end date/time), the syntax is: PnYnMnDTnHnMnS/YYYY-MM-DDThh:mm:ss where the duration, \"Pn…\", is represented before the solidus [/], the end date/time is represented following the solidus, and the entire representation is known as an “interval”. 4.1.4.4 USE OF THE 'STUDY DAY' VARIABLES The permissible Study Day variables (--DY, --STDY, and --ENDY) describe the relative day of the observation starting with the reference date as Day 1. They are determined by comparing the date portion of the respective date/time variables (--DTC, --STDTC, and --ENDTC) to the date portion of the Subject Reference Start Date (RFSTDTC from the Demography domain). The Subject Reference Start Date (RFSTDTC) is designated as Study Day 1. The Study Day value is incremented by 1 for each date following RFSTDTC. Dates prior to RFSTDTC are decremented by 1, with the date preceding RFSTDTC designated as Study Day -1 (there is no Study Day 0). This algorithm for determining Study Day is consistent with how people typically describe sequential days relative to a fixed reference point, but creates problems if used for mathematical calculations because it does not allow for a Day 0. As such, Study Day is not CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Week 2 Unscheduled", "definition": "3.1 (Null) 17 4.1.4.6 REPRESENTING ADDITIONAL STUDY DAYS The SDTM allows for --DTC values to be represented as study days (--DY) relative to the RFSTDTC reference start date variable in the DM dataset, as described above in Section 4.1.4.4. The calculation of additional study days within subdivisions of time in a clinical trial may be based on one or more sponsor-defined reference dates not represented by RFSTDTC. In such cases, the Sponsor may define Supplemental Qualifier variables and the Define data definition document should reflect the reference dates used to calculate such study days. CDISC SDTM Implementation Guide (Version 3.1.2) 4.1.4.7 USE OF RELATIVE TIMING VARIABLES --STRF, --STTPT, --STRTPT, --ENRF, --ENTPT, AND --ENRTPT --STRF and --ENRF These variables --STRF and --ENRF were originally intended to represent timing of events and interventions relative to the study timeline when sponsors collected information such as 'PRIOR', 'ONGOING', or 'CONTINUING via check boxes on the CRF in lieu of collecting a date. However, it is acceptable to populate --STRF and --ENRF even if a date is collected. Some sponsors may automatically derive such information from collected dates for analysis or reporting purposes. --STRF is used to identify the start of an observation (Event, Intervention) or the time of collection (Finding) as being 'BEFORE', ‘BEFORE/DURING’, 'DURING', or 'AFTER' the sponsor-defined reference period. The sponsor- defined reference period is the continuous period of time defined by the discrete starting point (RFSTDTC) and the discrete ending point (RFENDTC) for each subject in Demographics. --ENRF is used to identify the end of the observation (Event, Intervention, or Finding) as being 'BEFORE', 'DURING', ‘DURING/AFTER' or 'AFTER' the sponsor-defined reference period. The sponsor-defined reference period is a continuous period of time defined by the discrete starting point (RFSTDTC) and the discrete ending point (RFENDTC) in Demographics. Both --STRF and --ENRF should be set to ‘U’ if the underlying value (e.g., ‘PRIOR', 'ONGOING', or 'CONTINUING’) was not collected for a subject, and is therefore unknown. Figure 4.1.4.7 below illustrates how to populate these variables in a CM domain for the standard reference period represented by RFSTDTC and RFENDTC (defined in the DM domain). ` Figure 4.1.4.7 Example of --STRF and --ENRF Variables for Concomitant Medications", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 46", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 48", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Collection Type", "definition": "--DTC --STDTC --ENDTC Single-Point Collection", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "4.1.4.9", "definition": "USE OF DATES AS RESULT VARIABLES Dates are generally used only as timing variables to describe the timing of an event, intervention, or collection activity, but there may be occasions when it may be preferable to model a date as a result (--ORRES) in a Findings dataset. Note that using a date as a result to a Findings question is unusual and atypical, and should be approached with caution, but this situation may occasionally occur when a) a group of questions (each of which has a date response) is asked and analyzed together; or b) the event(s) and intervention(s) in question are not medically significant (often the case when included in questionnaires). Consider the following cases: •", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Calculated due date", "definition": "• Date of last day on the job • Date of high school graduation CDISC SDTM Implementation Guide (Version 3.1.2) One approach to modeling these data would be to place the text of the question in --TEST and the response to the question, a date represented in ISO 8601 format, in --ORRES and --STRESC as long as these date results do not contain the dates of medically significant events or interventions. Again, use extreme caution when storing dates as the results of findings. Remember, in most cases, these dates should be timing variables associated with a record in an Intervention or Events dataset. 4.1.4.10 REPRESENTING TIME POINTS Time points can be represented using the time point variables, --TPT, --TPTNUM, --ELTM, and the time point anchors, --TPTREF (text description) and --RFTDTC (the date/time). Note that time-point data will usually have an associated --DTC value. The interrelationship of these variables is shown in Figure 4.1.1.10 below. Figure 4.1.1.10 Values for these variables for Vital Signs measurements taken at 30, 60, and 90 minutes after dosing would look like the following.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DOSE ADMINISTRATION", "definition": "2006-08-01T08:00 2006-08-01T09:32 Note that the actual elapsed time is not an SDTM variable, but can be derived by an algorithm representing VSDTC-VSRFTDTC. When time points are used, --TPTNUM is required. Time points may or may not have an associated --TPTREF. Sometimes, --TPTNUM may be used as a key for multiple values collected for the same test within a visit; as such, there is no dependence upon an anchor such as --TPTREF, but there will be a dependency upon the VISITNUM. In such cases, VISITNUM will be required to confer uniqueness to values of --TPTNUM. If the protocol describes the scheduling of a dose using a reference intervention or assessment, then --TPTREF should be populated, even if it does not contribute to uniqueness. The fact that time points are related to a reference time point, and what that reference time point is, are important for interpreting the data collected at the time point. CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 50", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PRE-DOSE", "definition": "1 1H 2 PERIOD 2, DAY 1 5", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PERIOD 1", "definition": "3 DAY 5, PM DOSE 4H 3", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PERIOD 2", "definition": "4 DAY 1, PM DOSE 4H 3 CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PM DOSE", "definition": "4H 3 Within the context that defines uniqueness for a time point, which may include domain, visit, and reference time point, there must be a one-to-relationship between values of --TPT and --TPTNUM. In other words, if domain, visit, and reference time point uniquely identify subject data, then if two subjects have records with the same values of DOMAIN, VISITNUM, --TPTREF, and --TPTNUM, then these records may not have different time point descriptions in --TPT. Within the context that defines uniqueness for a time point, there is likely to be a one-to-one relationship between most values of --TPT and --ELTM. However, since --ELTM can only be populated with ISO 8601 periods of time (as described in 4.1.4.4), --ELTM may not be populated for all time points. For example, --ELTM is likely to be null for time points described by text such as \"pre-dose\" or \"before breakfast.\" When --ELTM is populated, if two subjects have records with the same values of DOMAIN, VISITNUM, --TPTREF, and --TPTNUM, then these records may not have different values in --ELTM. When the protocol describes a time point with text such as \"4-6 hours after dose\" or \"12 hours +/- 2 hours after dose\" the sponsor may choose whether and how to populate --ELTM. For example, a time point described as \"4-6 hours after dose\" might be associated with an --ELTM value of P4H. A time point described as \"12 hours +/- 2 hours after dose\" might be associated with an --ELTM value of P12H. Conventions for populating --ELTM should be consistent (the examples just given would probably not both be used in the same trial). It would be good practice to indicate the range of intended timings by some convention in the values used to populate --TPT. Sponsors may, of course, use more stringent requirements for populating --TPTNUM, --TPT, and --ELTM. For instance, a sponsor could decide that all time points with a particular --ELTM value would have the same values of --TPTNUM and --TPT, across all visits, reference time points, and domains. CDISC SDTM Implementation Guide (Version 3.1.2) 4.1.5 OTHER ASSUMPTIONS 4.1.5.1 ORIGINAL AND STANDARDIZED RESULTS OF FINDINGS The --ORRES variable contains the result of the measurement or finding as originally received or collected. --ORRES is an expected variable and should always be populated, with two exceptions: • When --STAT = 'NOT DONE' • When a record is derived (e.g., to represent an average or sum of collected values), --ORRES should generally not be populated (see QS example 4.1.5.1.5 Row 7) Derived records are flagged with the --DRVFL variable. When the derived record comes from more than one visit, the sponsor must define the value for VISITNUM, addressing the correct temporal sequence. The derived flag should only be set if the derivation is done by the sponsor, not by the vendor. For example in ECG data, if QTc Intervals are derived in-house by the sponsor, then the derived flag is set to “Y”. If the QTc Intervals are received from a vendor the derived flag is not populated. When --ORRES is populated, --STRESC must also be populated, regardless of whether the data values are character or numeric.--STRESC is derived either by the conversion of values in --ORRES to values with standard units, or by the assignment of the value of --ORRES (as in the PE Domain, where --STRESC could contain a dictionary-derived term). A further step is necessary when --STRESC contains numeric values. These are converted to numeric type and written to --STRESN. Because --STRESC may contain a mixture of numeric and character values, --STRESN may contain null values, as shown in the flowchart below. --ORRES (all original values) --STRESC (derive or copy all results) --STRESN (numeric results only) When the original measurement or finding is a selection from a defined codelist, in general, the --ORRES and --STRESC variables contain results in decoded format, that is, the textual interpretation of whichever code was selected from the codelist. In some cases such as the code values in the codelist are statistically meaningful standardized values or scores, which are defined by sponsors or by valid methodologies such as SF36 questionnaires, the --ORRES variables will contain the decoded format, whereas, the --STRESC variables as well as the --STRESN variables will contain the standardized values or scores. Occasionally data that are intended to be numeric are collected with characters attached that cause the character-to- numeric conversion to fail. For example, numeric cell counts in the source data may be specified with a greater than (>) or less than (<) sign attached (e.g. >10, 000 or <1). In order for these values to be reported as numeric in --STRESN, and assuming this is desirable, the sponsor must apply a rule to derive a numeric value for these strings and document this in the value-level metadata. For example the rule may be to remove the character symbol or to compensate for the symbol in some other way, such as adding to values using > or subtracting from values using <. Examples are included in Section 4.1.5.1.2, Rows 11A-F.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 52", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 54", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Chemistry", "definition": "229 mg/dL 0 199 229 229 11", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "BLQ", "definition": "mg/L 0 0 mg/L 7", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Hematology", "definition": "5.9 10^9/L 4 11 5.9 5.9", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Not Done", "definition": "--STAT Populated using a code value in the list of controlled terms, codelist ND (C66789) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "QS2", "definition": "HEALTH PERCEPTIONS (0-100) 82 82", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "QSP11", "definition": "EXPECT HEALTH TO GET BETTER", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 56", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CRF", "definition": "Case report form (sometimes case record form)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "COMMITTEE", "definition": "4.1.5.5 CLINICAL SIGNIFICANCE FOR FINDINGS OBSERVATION CLASS DATA SDTM provides two ways to handle assessments of clinical significance. Each has its place; they are not interchangeable. One is used to handle assessments of the clinical significance of a particular result (single record), CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 58", "definition": "CDISC, © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "LBSEQ", "definition": "48 1 Additional examples may be found in the domain examples such as the examples for Disposition/Adverse Event found in Section 6.2.2.2, Example 4, and all of the Pharmocokinetics examples in Section 6.3.10.5. CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Value of", "definition": "--STAT Spontaneously reported event occurred Pre-specified event occurred", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DM", "definition": "--REAS Reason (include domain prefix) All general observation classes", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Exp", "definition": "* Indicates variable may be subject to controlled terminology A record in a SUPP-- dataset relates back to its parent record(s) via the key identified by the STUDYID, RDOMAIN, USUBJID and IDVAR/IDVARVAL variables. An exception is SUPP-- dataset records that are related to Demography (DM) records , such as the Intent To Treat (ITT) and Safety (SAFETY) subject-level population flags, where both IDVAR and IDVARVAL will be null because the key variables STUDYID, RDOMAIN, and USUBJID are sufficient to identify the unique parent record in DM (DM has one record per USUBJID). All records in the SUPP-- datasets must have a value for QVAL. Transposing source variables with missing/null values may generate SUPP-- records with null values for QVAL, causing the SUPP-- datasets to be extremely large. When this happens, the sponsor must delete the records where QVAL is null prior to submission. See Section 4.1.5.3 for information on representing information greater than 200 characters in length. CDISC SDTM Implementation Guide (Version 3.1.2) See Appendix C5 for controlled terminology for QNAM and QLABEL for some of the most common Supplemental Qualifiers. Additional QNAM values may be created as needed, following the guidelines provided in the CDISC Notes for QVAL. 8.4.2 SUBMITTING SUPPLEMENTAL QUALIFIERS IN SEPARATE DATASETS Beginning with the SDTMIG V3.1.1, the preferred approach is to submit Supplemental Qualifiers by domain rather than placing all of the supplemental information within one dataset. Therefore, it is recommended that sponsors who utilize the single SUPPQUAL approach begin to transition to individual SUPP-- datasets by domain. The single SUPPQUAL dataset option will be deprecated in the next (post V3.1.2) release. Following this convention for the Supplemental Qualifiers produces a one-to-one correspondence between a domain dataset and its Supplemental Qualifier dataset. In such cases, the set of Supplemental Qualifiers for each domain should be included in a separate dataset with the name SUPP-- where -- denotes the source domain which the Supplemental Qualifiers relate back to. For example, population flags and other demographic qualifiers would be placed in suppdm.xpt. Data may have been additionally split into multiple datasets (see Section 4.1.1.6.1, Splitting Domains). Sponsors must, however, choose only one approach for each study. Either individual SUPP-- datasets for each domain where needed should be submitted, or a single SUPPQUAL dataset for the entire study. In other words, separate SUPP-- datasets cannot be used with some domains and SUPPQUAL for the others. 8.4.3 SUPP-- EXAMPLES The examples below demonstrate how a set of SUPP-- datasets could be used to relate non-standard information to a parent domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Perm", "definition": "* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI code-list code value) 7.5.1 ASSUMPTIONS FOR TI DATASET 1. If inclusion/exclusion criteria were amended during the trial, then each complete set of criteria must be included in the TI domain. TIVERS is used to distinguish between the versions. 2. Protocol version numbers should be used to identify criteria versions, though there may be more versions of the protocol than versions of the inclusion/exclusion criteria. For example, a protocol might have versions 1, 2, 3, and 4, but if the inclusion/exclusion criteria in version 1 were unchanged through versions 2 and 3, and only changed in version 4, then there would be two sets of inclusion/exclusion criteria in TI, one for version 1, one for version 4. 3. Individual criteria do not have versions. If a criterion changes, it should be treated as a new criterion, with a new value for IETESTCD. If criteria have been numbered and values of IETESTCD are generally of the form INCL00n or EXCL00n, and new versions of a criterion have not been given new numbers, separate values of IETESTCD might be created by appending letters, e.g. INCL003A, INCL003B. 4. IETEST contains the text of the inclusion/exclusion criterion. However, since entry criteria are rules, the variable TIRL has been included in anticipation of the development of computer executable rules. 5. Assumption 5 for the IE Domain (Section 6.3.2.1) describes how to handle values of IETEST > 200 characters. CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 60", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Timing", "definition": "1. Planned study day of VISIT. 2. Due to its sequential nature, used for sorting.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DMDY", "definition": "Study Day of Collection Num", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 62", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ABC12301001", "definition": "001 2006-01-12 2006-03-10 01 JOHNSON, M 1948-12-13 57", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ABC12301002", "definition": "002 2006-01-15 2006-02-28 01 JOHNSON, M 1955-03-22 50", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ABC12301003", "definition": "003 2006-01-16 2006-03-19 01 JOHNSON, M 1938-01-19 68", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ABC12301004", "definition": "004 01 JOHNSON, M 1941-07-02 5", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ABC12302001", "definition": "001 2006-02-02 2006-03-31 02 GONZALEZ, E 1950-06-23 55", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ABC12302002", "definition": "002 2006-02-03 2006-04-05 02 GONZALEZ, E 1956-05-05 49", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "USA", "definition": "CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Check One", "definition": "American Indian or Alaska Native \u0000", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Asian", "definition": "\u0000 Black or African American \u0000 Native Hawaiian or Other Pacific", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "HISPANIC OR LATINO", "definition": "CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Check all that apply", "definition": "American Indian or Alaska Native \u0000", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "White", "definition": "\u0000 Other, Specify: _____________________ \u0000 Row 1 (DM), Row 1 (SUPPDM) Subject 001 checked “Other, Specify” and entered “Japanese” which was mapped to “Asian” by the sponsor. Row 2 (DM), Row 2 (SUPPDM) Subject 002 checked “Other, Specify” and entered “Swedish” which was mapped to “White” by the sponsor.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Race 2", "definition": "AMERICAN INDIAN OR ALASKA NATIVE", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 64", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Non-Japanese", "definition": "\u0000 Black or African American \u0000 Native Hawaiian or Other Pacific", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 66", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Run-In", "definition": "2006-05-21 2006-05-26 CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 68", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CO", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 70", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "INVESTIGATOR", "definition": "Note that the use of 10^9 as a unit is not a standard representation. CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 72", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SE", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Req", "definition": "• * indicates variable may be subject to controlled terminology CDISC SDTM Implementation Guide (Version 3.1.2) 8.2.2 RELREC DATASET EXAMPLES Example 1: This example shows how to use the RELREC dataset to relate records stored in separate domains for USUBJID 123456 who had two lab tests performed (Rows 5 and 6) and took two concomitant medications (Rows 2 and 3) as the result of an adverse event (Rows 1 and 4). This example represents a situation in which the adverse event is related to both the concomitant medications and the lab tests, but there is no relationship between the lab values and the concomitant medications", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Example 1", "definition": "In the two rows of suppdm.xpt, population flags are defined as supplemental information to a subject’s demographic data. IDVAR and IDVARVAL are null because the key variables STUDYID, RDOMAIN, and USUBJID are sufficient to identify a unique parent record in DM. suppdm.xpt: Supplemental Qualifiers for DM Row STUDYID RDOMAIN USUBJID IDVAR IDVARVAL QNAM", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "COUNTRY", "definition": "Populated using a code value in the list of controlled terms, codelist COUNTRY (C66786) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 74", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Example 2", "definition": "The two rows of suppae.xpt add qualifying information to adverse event data (RDOMAIN=AE). IDVAR defines the key variable used to link this information to the AE data (AESEQ). IDVARVAL specifies the value of the key variable within the parent AE record that the SUPPAE record applies to. The remaining columns specify the supplemental variables’ names (AESOTHC and AETRTEM), labels, values, , and who made the evaluation. suppae.xpt: Supplemental Qualifiers for AE Row STUDYID RDOMAIN USUBJID IDVAR IDVARVAL", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DBA", "definition": "2006-05-03 2006-05-31 2 DOUBLE-BLIND TREATMENT 5 456 3", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "UNPLAN", "definition": "2006-05-31 2006-06-13 Drug B dispensed in error DOUBLE-BLIND TREATMENT 6 456 4", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 76", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SV", "definition": "101 4.1 2006-02-07 2006-02-07 18 18", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SVSTDTC", "definition": "Start Date/Time of Visit Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SVENDTC", "definition": "End Date/Time of Visit Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Screen", "definition": "Example Trial 6: Arms and Epochs Screen Trt A R A Trt A R A Trt A R A Trt A R A Follow", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DAY 1", "definition": "1 2001-02-01T18:30 1 600 min 12", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Week 1", "definition": "1999-06-19 8 (cont) 5.00 9.00", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Week 2", "definition": "1999-07-21 11 (cont) 10^3/uL 4 11", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Follow-up", "definition": "Decision not to treat further 4 weeks", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 78", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Observation Classes", "definition": "6.1 INTERVENTIONS 6.1.1 CONCOMITANT MEDICATIONS — CM cm.xpt, Concomitant Medications — Interventions, Version 3.1.2, July 25, 2007. One record per recorded intervention occurrence or constant-dosing interval per subject, Tabulation", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char CM", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CMSPID", "definition": "Sponsor-Defined Identifier Char Identifier Sponsor-defined reference number. Examples: a number pre-printed on the CRF as an explicit line identifier or record identifier defined in the sponsor’s operational database. Example: line number on a concomitant medication page.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CMTRT", "definition": "Reported Name of Drug, Med, or Therapy", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 80", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CMCAT", "definition": "Category for Medication Char *", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CMROUTE", "definition": "Route of Administration Char (ROUTE)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 82", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "References", "definition": "MBORRESU Original Units Char *", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Time Point", "definition": "Char BEFORE, AFTER, COINCIDENT, ONGOING, U", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CMSTTPT", "definition": "Start Reference Time Point Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CMENTPT", "definition": "End Reference Time Point Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 84", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "VPA", "definition": "Example 3: Pre-specified concomitant medications using CMPRESP, CMOCCUR, CMSTAT, and CMREASND Sponsors often are interested in whether subjects are exposed to specific concomitant medications, and collect this information using a checklist. The example below is for a study that has a particular interest in the antidepressant medications that subjects use. For the study’s purposes, the absence is just as important as the presence of a medication. This can be clearly shown by using CMOCCUR. In this example, CMPRESP shows that the subjects were specifically asked if they use any of three antidepressants (Zoloft, Prozac, or Paxil). The value of CMOCCUR indicates the response to the pre-specified medication question. CMSTAT indicates whether the response was missing for a pre-specified medication and CMREASND shows the reason for missing response. The medication details (e.g., dose, frequency) are not of interest.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 86", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char EX", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "EXSPID", "definition": "Sponsor-Defined Identifier Char Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an explicit line identifier or defined in the sponsor’s operational database. Example: Line number on a CRF Page.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "EXTRT", "definition": "Name of Actual Treatment Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "EXCAT", "definition": "Category for Treatment Char *", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "EXSCAT", "definition": "Subcategory for Treatment Char *", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "EXROUTE", "definition": "Route of Administration Char (ROUTE)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 88", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "EXTPT", "definition": "Planned Time Point Name Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "EX Definition", "definition": "a. The Exposure domain model records the details of a subject’s exposure to protocol-specified study treatment. Study treatment may be any intervention that is prospectively defined as a test material within a study, and is typically but not always supplied to the subject. Examples include but are not limited to placebo, active comparators, and investigational products. Treatments that are not protocol-specified should be recorded in the Concomitant Medication (CM) domain. b. This domain should contain one record per constant dosing interval per subject. \"Constant dosing interval\" is sponsor-defined, and may include any period of time that can be described in terms of a known treatment given at a consistent dose. E.g., for a study with once-a-week administration of a standard dose for 6 weeks, exposure may be represented with a single record per subject, spanning the entire treatment phase. Or if the sponsor monitors each treatment administration and deviations in treatment or dose occur, there could be up to six records (one for each weekly administration). 2. Categorization and Grouping a. EXCAT and EXSCAT may be used when appropriate to categorize treatments into categories and subcategories. For example, if a study contains several active comparator medications, EXCAT may be set to 'ACTIVE COMPARATOR.' Such categorization will not be useful in most studies, so these variables are permissible but not expected. 3. Exposure Treatment Description a. EXTRT captures the name of the investigational treatment and it is the topic variable. It is a required variable and must have a value. EXTRT should only include the treatment name and should not include dosage, formulation or other qualifying information. For example, “ASPIRIN 100MG TABLET” is not a valid value for EXTRT. This example should be expressed as EXTRT= “ASPIRIN”, EXDOSE= “100”, EXDOSU= “MG”, and EXDOSFRM= “TABLET”. CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Timing Variables", "definition": "a. Relative timing assessments \"Prior\" or “Ongoing” are common in the collection of Clinical Event information. CESTRF or CEENRF may be used when this timing assessment is relative to the study reference period for the subject represented in the Demographics dataset (RFENDTC). CESTRTPT with CESTTPT or CEENRTPT with CEENTPT may be used when \"Prior\" or \"Ongoing\" are relative to specific dates other than the start and end of the study reference period. See Section 4.1.4.7. b. Additional Timing variables may be used when appropriate. 5. Additional Events Qualifiers The following qualifiers would generally not be used in CE: --SER, --ACN, --ACNOTH, --REL, --RELNST, --OUT, --SCAN, --SCONG, --SDISAB, --SDTH, --SHOSP, --SLIFE, --SOD, --SMIE CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "EXDOSFRM", "definition": "EXDOSFRQ EXDOSTOT EXROUTE EXSTDTC EXENDTC EXSTDY EXENDY 1 12345", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ORAL", "definition": "2004-07-10T07:30 2004-07-10T07:30 10 10 12 (cont) 800", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 90", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "TABLET", "definition": "2002-07-02 2002-08-01 Increased due to suboptimal efficacy CDISC SDTM Implementation Guide (Version 3.1.2) Exposure Example 4: This is an example of a titration Exposure dataset for a study that gradually increases dosage while simultaneously evaluating efficacy and toleration of the treatment regimen. The schedule specifies that Drug A be administered twice daily starting with 100 mg for 3 days, then increase to 200 mg daily for 3 days, then increase further in 100-mg increments every three days until signs of intolerance are noted or no improvement in efficacy is observed. Row STUDYID DOMAIN USUBJID EXSEQ EXGRPID EXTRT EXDOSE EXDOSU", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 92", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char SU", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SUSPID", "definition": "Sponsor-Defined Identifier Char Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an explicit line identifier or defined in the sponsor’s operational database. Example: Line number on a Tobacco & Alcohol use CRF page.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 94", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SUROUTE", "definition": "Route of Administration Char (ROUTE)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SUDUR", "definition": "Duration of Substance Use Char ISO 8601", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SUSTTPT", "definition": "Start Reference Time Point Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SUENTPT", "definition": "End Reference Time Point Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 96", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ONGOING", "definition": "2 (cont) 2006-01-01 2006-01-01 3 (cont) 2006-03-15", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "BEFORE", "definition": "2003 4 (cont) 2006-03-15 2006-03-15 5 (cont) 2006-03-15 2006-03-15 6 (cont) Subject left office before CRF was completed 7 (cont) Subject left office before CRF was completed CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 98", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char AE", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AESPID", "definition": "Sponsor-Defined Identifier Char Identifier Sponsor-defined identifier. It may be pre-printed on the CRF as an explicit line identifier or defined in the sponsor’s operational database. Example: Line number on an Adverse Events page.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AELOC", "definition": "Location of the Reaction Char *", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AEPATT", "definition": "Pattern of Adverse Event Char *", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AEOUT", "definition": "Outcome of Adverse Event Char (OUT)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 100", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AESOD", "definition": "Occurred with Overdose Char (NY)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AETOXGR", "definition": "Populated using a code value in the list of controlled terms, codelist TOXGR (C66784) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AEDUR", "definition": "Duration of Adverse Event Char ISO 8601", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Period", "definition": "--ROUTE Please see Section 4.1.4.7 “Use of RELATIVE Timing Variables --STRF, --STTPT, --STRTPT, --ENRF, --ENTPT, AND --ENRTPT” for specific regarding controlled terminology for these variables. Populated using a code value in the list of controlled terms, codelist STENRF (C66728) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AEENTPT", "definition": "End Reference Time Point Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AE Definition", "definition": "The Adverse Events dataset includes clinical data describing \"any untoward medical occurrence in a patient or clinical investigation subject administered a pharmaceutical product and which does not necessarily have to have a causal relationship with this treatment\" (ICH E2A). In consultation with regulatory authorities, sponsors may extend or limit the scope of adverse event collection (e.g., collecting pre-treatment events related to trial conduct, not collecting events that are assessed as efficacy endpoints). The events included in the AE dataset should be consistent with the protocol requirements. Adverse events may be captured either as free text or via a pre-specified list of terms. 2. Adverse Event Description and Coding a. AETERM captures the verbatim term collected for the event. It is the topic variable for the AE dataset. AETERM is a required variable and must have a value. b. AEMODIFY is a permissible variable and should be included if the sponsor’s procedure permits modification of a verbatim term for coding. The modified term is listed in AEMODIFY. The variable should be populated as per the sponsor’s procedures. c. AEDECOD is the preferred term derived by the sponsor from the coding dictionary. It is a required variable and must have a value. It is expected that the reported term (AETERM) will be coded using a standard dictionary such as MedDRA. The sponsor is expected to provide the dictionary name and version used to map the terms in the metadata (the information will be displayed in the Comments column in the Define document). If more than one version of the dictionary is used in a study, the version used for each record should be specified using standard Supplemental Qualifier name code AEDICTVS in the SUPPAE dataset as described in Appendix C5 and Section 8.4. d. AEBODSYS is the system organ class from the coding dictionary associated with the adverse event by the sponsor. This value may differ from the primary system organ class designated in the coding dictionary's standard hierarchy. It is expected that this variable will be populated. e. Sponsors may include the values of additional levels from the coding dictionary's hierarchy (i.e., High-Level Group Term, High-Level Term, Lower- Level Term) in the SUPPAE dataset as described in Appendix C5 (standard Supplemental Qualifier name codes) and Section 8.4. 3. Additional Categorization and Grouping a. AECAT and AESCAT should not be redundant with the domain code or dictionary classification provided by AEDECOD and AEBODSYS (i.e., they should provide a different means of defining or classifying AE records). AECAT and AESCAT are intended for categorizations that are defined in advance. For example, a sponsor may have a separate CRF page for AEs of special interest and then another page for all other AEs. AECAT and AESCAT should not be used for after-the-fact categorizations such as clinically significant. In cases where a category of AEs of special interest resembles a part of the dictionary hierarchy (e.g., \"CARDIAC EVENTS\"), the categorization represented by AECAT and AESCAT may differ from the categorization derived from the coding dictionary. b. AEGRPID may be used to link (or associate) different records together to form a block of related records at the subject level within the AE domain. It should not be used in place of AECAT or AESCAT, which are used to group data across subjects. CDISC SDTM Implementation Guide (Version 3.1.2) 4. Pre-specified terms; presence or absence of events a. Adverse events are generally collected in two different ways, either by recording free text or using a pre-specified list of terms. In the latter case, the solicitation of information on specific adverse events may affect the frequency at which they are reported; therefore, the fact that a specific adverse event was solicited may be of interest to reviewers. An AEPRESP value of “Y” is used to indicate that the event in AETERM was pre-specified on the CRF. b. If an adverse event was reported using free text, the value of AEPRESP should be null. AEPRESP is a permissible field and may be omitted from the dataset if all adverse events were collected as free text. c. If it is important to know which adverse events from a pre-specified list were not reported as well as those that did occur, these data should be submitted in a Findings class dataset such as Clinical Findings (CF, Section 6.3.11). A record should be included in that Findings dataset for each pre- specified adverse-event term. Records for adverse events that actually occurred should also exist in the AE dataset with AEPRESP set to 'Y.' d. If a study collects both pre-specified adverse events as well as free-text events, the value of AEPRESP should be “Y” for all pre-specified events and null for events reported as free-text. e. When adverse events are collected with the recording of free text, a record may be entered into the sponsor’s data management system to indicate 'no adverse events' for a specific subject. For these subjects, do not include a record in the AE submission dataset to indicate that there were no events. Records should be included in the submission AE dataset only for adverse events that have actually occurred. 5.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 102", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AE Structure", "definition": "The structure of the AE domain is one record per adverse event per subject. It is the sponsor's responsibility to define an event. This definition may vary based on the sponsor's requirements for characterizing and reporting product safety and is usually described in the protocol. For example, the sponsor may submit one record that covers an adverse event from start to finish. Alternatively, if there is a need to evaluate AEs at a more granular level, a sponsor may submit a new record when severity, causality, or seriousness changes or worsens. By submitting these individual records, the sponsor indicates that each is considered to represent a different event. The submission dataset structure may differ from the structure at the time of collection. For example, a sponsor might collect data at each visit in order to meet operational needs, but submit records that summarize the event and contain the highest level of severity, causality, seriousness, etc. Examples of dataset structure: 1. One record per adverse event per subject for each unique event. Multiple adverse event records reported by the investigator are submitted as summary records “collapsed” to the highest level of severity, causality, seriousness, and the final outcome. 2. One record per adverse event per subject. Changes over time in severity, causality, or seriousness are submitted as separate events. Alternatively, these changes may be submitted in a separate dataset based on the CF model (see Section 6.3.11). 3. Other approaches may also be reasonable as long as they meet the sponsor's safety evaluation requirements and each submitted record represents a unique event. The domain-level metadata (See Section 3.2.2) should clarify the structure of the dataset. 8. Additional Events Qualifiers The following qualifiers would not be used in AE: --OCCUR, --STAT, and--REASND. They are the only Qualifiers from the SDTM Events Class not in the AE domain. They are not permitted because the AE domain contains only records for adverse events that actually occurred. See Assumption 4c above for information on how to deal with negative responses or missing responses to probing questions for pre-specified adverse events. CDISC SDTM Implementation Guide (Version 3.1.2) 6.2.1.2 EXAMPLES FOR ADVERSE EVENTS DOMAIN MODEL", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 1 and 2", "definition": "Show the following: -an example of modifying the reported term for coding purposes. The modified value is in AEMODIFY. -an example of the overall seriousness question AESER answered with an “N” and corresponding seriousness category variables (e.g., AESDTH, AESHOSP) left blank.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Row 3", "definition": "Shows a date/time in ISO 8601 representation where both the date and time were collected. Rows 4-5 Show where EGGRPID is used to group related results.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AE", "definition": "--CLSIG Clinically Significant (include domain prefix)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AEREL", "definition": "1 (cont) Nervous system disorders", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "NOT APPLICABLE", "definition": "DEFINITELY NOT RELATED 2 (cont) Musculoskeletal and connective tissue disorders", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AEENRF", "definition": "1 (cont) RECOVERED/RESOLVED -1 -1 2 (cont) RECOVERED/RESOLVED 1 1 3 (cont) RECOVERING/RESOLVING", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 104", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 1and 2", "definition": "Show that nausea and vomiting were pre-specified on a CRF, as indicated by AEPRESP = “Y”. The subject did not experience diarrhea, so no record for that term exists in the AE dataset.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 3", "definition": "Shows an example of an AE (headache) that is not pre-specified on a CRF as indicated by a blank for AEPRESP", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "RELATED", "definition": "RECOVERED/RESOLVED 2005-10-13T13:00 2005-10-13T19:00 3 3 3 (cont) DOSE NOT CHANGED", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "POSSIBLY RELATED", "definition": "RECOVERED/RESOLVED 2005-10-21 2005-10-21 11 11", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Example 3", "definition": "This is an example of how the language used for a questionnaire might be represented. The parent domain (RDOMAIN) is QS, and IDVAR is QSCAT. QNAM holds the name of the Supplemental Qualifier variable being defined (QSLANG). The language recorded in QVAL applies to all of the subject’s records where IDVAR (QSCAT) equals the value specified in IDVARVAL . In this case, IDVARVAL has values for two questionnaires (SF36 and ADAS) for two separate subjects. QVAL identifies the questionnaire language version (French or German) for each subject. CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "disorders", "definition": "2005-10-29 19 CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Example 4", "definition": "The following example illustrates how data that may have been represented in an operational database as a single domain can be expressed using an Events general-observation-class dataset and a Supplemental Qualifiers dataset. Original Operational (non-SDTM) Dataset:", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Row 1", "definition": "Shows extent of growth of Organism 1 found at Visit 1 in specimen 1 ( MBGRPID=1, Row 3 in MB example above). Rows 2, 3 Show results of susceptibility testing on Organism 1 found at Visit 1 in specimen 1 (MBGRPID=1, Row 3 in MB example above).", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 2-6", "definition": "Show the data in original units of measure in EGORRES, EGSTRESC, and EGSTRESN. See Section 4.1.5.1 for additional examples for the population of Result Qualifiers.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 106", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char DS", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DSSPID", "definition": "Sponsor-Defined Identifier Char Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an explicit line identifier or defined in the sponsor’s operational database. Example: Line number on a Disposition page.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 108", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DS Definition", "definition": "a. The Disposition dataset provides an accounting for all subjects who entered the study and may include protocol milestones, such as randomization, as well as the subject's completion status or reason for discontinuation for the entire study or each phase or segment of the study, including screening and post-treatment follow up. Sponsors may choose which disposition events and milestones to submit for a study. See ICH E3, Section 10.1 for information about disposition events. 2. DS Description and Coding a. DSTERM and DSDECOD are required. DSDECOD values are drawn from sponsor-defined controlled terminology. The controlled terminology will depend on the value of DSCAT. When DSCAT=\"DISPOSITION EVENT\", DSTERM contains either \"COMPLETE\" or, if the subject did not complete, specific verbatim information about the disposition event. b. When DSTERM = \"COMPLETE\", DSDECOD = \"COMPLETE\". When DSTERM contains verbatim text, DSDECOD will contain a standard term from a controlled terminology list. For example, DSTERM = \"Subject moved\" might map to \"LOST TO FOLLOW-UP\" in the sponsor's controlled terminology. c. A sponsor may collect one disposition event for the trial as a whole, or they may collect disposition for each Epoch of the trial. When disposition is collected for each Epoch, the variable EPOCH should be included in the DS dataset. When EPOCH is populated for disposition events (records with DSCAT = DISPOSITION EVENT), EPOCH is the name of the Epoch whose disposition is described in the record. This is a subtly different meaning from that of EPOCH when it is used in other Events general-observation-class domains, where EPOCH, as a timing variable, is the name of the Epoch during which --STDTC occurs. The values of EPOCH are drawn from the Trial Arms domain, Section 7.2. d. When DSCAT=\"PROTOCOL MILESTONE\", DSTERM and DSDECOD will contain the same value drawn from the sponsor's controlled terminology. Examples of controlled terms include \"INFORMED CONSENT OBTAINED\" and \"RANDOMIZED.\" EPOCH should not be populated when DSCAT = 'PROTOCOL MILESTONE'. e. Events that are not disposition or milestone related are classified as an “OTHER EVENT.” “TREATMENT UNBLINDED” is a common example of “OTHER EVENT.” CDISC SDTM Implementation Guide (Version 3.1.2) 3.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 110", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DS", "definition": "456102 1 SUBJECT TAKING STUDY MED", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DISPOSITION EVENT", "definition": "2003-10-15 Disposition Example 3: Rows 1, 2 Subject completed the treatment and follow-up phase Rows 3, 5 Subject did not complete the treatment phase but did complete the follow-up phase.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Row 4", "definition": "Shows extent of growth of Organism 2 found at Visit 1 in specimen 1 (MBGRPID=2, Row 4 in MB example above). Rows 5, 6 Show results of susceptibility testing on Organism 2 found at Visit 1 in specimen 1 (MBGRPID=2, Row 4 in MB example above).", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 112", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PHASE", "definition": "2003-09-29 2003-09-29 Adverse Event (AE) Dataset:", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AESMIE", "definition": "1 (cont) DEFINITELY NOT RELATED", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "RELREC Dataset", "definition": "Rows 1-5 identify the records that participate in the relationship with RELID=1. Rows 1-4 identify the four CF records, and Row 5 identifies the AE record. Rows 6-8 identify the records that participate in the relationship with RELID=2. Rows 6 and 7 identify the two CF records, and Row 8 identifies the AE record.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AESEQ", "definition": "2 2 CDISC SDTM Implementation Guide (Version 3.1.2) 7 Trial Design Datasets 7.1", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 114", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char MH", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "MHSPID", "definition": "Sponsor-Defined Identifier Char Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an explicit line identifier or defined in the sponsor’s operational database. Example: Line number on a Medical History page.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "MHENTPT", "definition": "End Reference Time Point Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "MH Definition", "definition": "a. The Medical History dataset generally includes the subject's prior and concomitant conditions at the start of the trial. Note that treatments for prior and concomitant medications should be submitted in an appropriate dataset from the Interventions class (e.g., CM). Examples of subject medical history information could include general medical history and gynecological history. 2. Medical History Description and Coding a. MHTERM captures the verbatim term collected for the condition or event. It is the topic variable for the MH dataset. MHTERM is a required variable and must have a value. b. MHMODIFY is a permissible variable and should be included if the sponsor’s procedure permits modification of a verbatim term for coding. The modified term is listed in MHMODIFY. The variable should be populated as per the sponsor’s procedures; null values are permitted. c. If the sponsor codes the reported term (MHTERM) using a standard dictionary, then MHDECOD will be populated with the preferred term derived from the dictionary. The sponsor is expected to provide the dictionary name and version used to map the terms in the metadata (the information will be displayed in the Comments column in the Define document). If more than one version of the dictionary is used in a study, the version used for each record should be specified using standard Supplemental Qualifier name code MHDICTVS in the SUPPMH dataset as described in Section 8.4 and Appendix C5.. d. MHBODSYS is the system organ class from the coding dictionary associated with the adverse event by the sponsor. This value may differ from the primary system organ class designated in the coding dictionary's standard hierarchy. e. Sponsors may include the values of additional levels from the coding dictionary's hierarchy (e.g., High Level Group Term, High Level Term, Lower Level Term) in the SUPPMH dataset as described in Section 8.4. See Appendix C5 for standard Supplemental Qualifier name codes. f. If a CRF collects medical history by pre-specified body systems and the sponsor also codes reported terms using a standard dictionary, then MHDECOD and MHBODSYS are populated using the standard dictionary. MHCAT and MHSCAT should be used for the pre-specified body systems. 3. Additional Categorization and Grouping a. MHCAT and MHSCAT may be populated with the sponsor's pre-defined categorization of medical history events, which are often pre-specified on the CRF. Note that even if the sponsor uses the body system terminology from the standard dictionary, MHBODSYS and MHCAT may differ, since MHBODSYS is derived from the coding system, while MHCAT is effectively assigned when the investigator records a condition under the pre-specified category. i. This categorization should not group all records (within the MH Domain) into one generic group such as 'Medical History' or 'General Medical History' because this is redundant information with the domain code. If no smaller categorization can be applied, then it is not necessary to include or populate this variable. ii. Examples of MHCAT could include 'General Medical History, (see above assumption since if ‘General Medical History’ is an MHCAT value then there should be other MHCAT values), ' 'Allergy Medical History, ' and 'Reproductive Medical History.' b. MHGRPID may be used to link (or associate) different records together to form a block of related records at the subject level within the MH domain. It should not be used in place of MHCAT or MHSCAT, which are used to group data across subjects. For example, if a group of syndromes reported for a subject were related to a particular disease then the MHGRPID variable could be populated with the appropriate text.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 116", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Value of MHSTAT", "definition": "Spontaneously reported event occurred Pre-specified event occurred", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 1-3", "definition": "MHENRF has been populated based on the response to the \"Ongoing at Study Start\" question on the General Medical History CRF. If \"Yes\" is specified, MHENRF=\"DURING/AFTER;\" if \"No\" is checked, MHENRF=\"PRIOR.\" See Section 4.1.4.7 for further guidance on using --STRF and --ENRF.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Row 5", "definition": "Shows the organism assigned as ORG02 is still present in Specimen 2 at Visit 2.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Example 1 data", "definition": "Row STUDYID DOMAIN USUBJID MHSEQ", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "MH", "definition": "101003 4 TRANSIENT ISCHEMIC ATTACK", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 118", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 1-10", "definition": "MHPRESP values of 'Y' indicate that each condition was pre-specified on the CRF. The presence or absence of a condition is documented with MHOCCUR. The data are collected as part of the Screening visit. Rows 1-3, 7, 9 The absence of a condition is documented with MHOCCUR. Rows 4-6, 8 The presence of a condition is documented with MHOCCUR.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Row 10", "definition": "The subject had cholesterol measured. The normal range for this test is <200 mg/dL. The value <200 may not be used in the normal range variables LBORNRHI or LBSTNRHI; however, a sponsor may decide, for example, to enter '0' into LBORNRLO and '199' in LBORNRHI. The sponsor must define the appropriate value for all of the normal range variables. Row 1, 6 The SUPPLB dataset example shows clinical significance assigned by the investigator for test results where LBNRIND (reference range indicator) is populated. Lab Example 1 LB dataset", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 5-9", "definition": "MHCAT indicates that these terms were reported on the RISK FACTORS page. These terms are not coded.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 5- 8", "definition": "MHPRESP values of 'Y' indicate that each risk factor was pre-specified on the CRF. MHOCCUR is populated with Y or N corresponding to the CRF response to the questions for the 4 pre-specified risk factors.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Row 9", "definition": "Shows the proper use of the LBSTAT variable to indicate \"NOT DONE\", where a reason was collected when a test was not done.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 120", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char DV", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DVSPID", "definition": "Sponsor-Defined Identifier Char Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an explicit line identifier or defined in the sponsor’s operational database. Example: Line number on a CRF page.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 1 and 3", "definition": "Show examples of a TREATMENT DEVIATION type of protocol deviation. This type of deviation is categorized as MAJOR (DVCAT) for this study.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Row 2", "definition": "Shows the proper use of the STAT variable to indicate \"NOT DONE\", and when PEREASND is used to indicate why a body system (PETEST) was not examined.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 4 and 5", "definition": "Shows an example of VISIT OUTSIDE WINDOW, which is considered to be a MINOR (DVCAT) deviation for this study. Row STUDYID DOMAIN USUBJID DVSEQ", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DV", "definition": "123103 4 VISIT 3 MORE THAN 3 DAYS OUT OF", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 122", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char CE", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CESPID", "definition": "Sponsor-Defined Identifier Char Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an explicit line identifier or defined in the sponsor’s operational database. Example: Line number on a CRF page.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CECAT", "definition": "Category for Clinical Event Char *", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CEOCCUR", "definition": "Clinical Event Occurrence Char (NY)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 124", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CESTTPT", "definition": "Start Reference Time Point Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CEENTPT", "definition": "End Reference Time Point Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Value of CESTAT", "definition": "Spontaneously reported event occurred Pre-specified event occurred", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Conjunctivitis", "definition": "Example 1 Data: Rows 1-3 Show records for clinical events for which start dates were recorded. Since conjunctivitis was not observed, no start date was recorded and there is no CE record.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 126", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Diarrhea", "definition": "Row 1 shows the record for a Nausea probing question answered \"Yes\". Since Nausea is a pre-specified adverse event, the AE domain will contain a record for this event. Row 2 shows the record for a Vomiting probing question answered \"No\". Row 3 shows the record for a Diarrhea probing question with an answer of \"NOT DONE\".", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SEVERE", "definition": "2005-10-09 2005-10-11 CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 128", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char EG", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "EGSPID", "definition": "Sponsor-Defined Identifier Char Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an explicit line identifier or defined in the sponsor's operational database. Example: Line number from the ECG page.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "EGPOS", "definition": "ECG Position of Subject Char *", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "EGXFN", "definition": "ECG External File Name Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 130", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "EGTPT", "definition": "Planned Time Point Name Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 1-6", "definition": "Show how ECG measurements are represented.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 2-10", "definition": "Show how EGCAT could be used to group the intervals and the findings.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 5-6", "definition": "Show QTCB and QTCF. The data shows a “Y” in the EGDRVFL column since these results are derived by the sponsor in this example.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 7-10", "definition": "Show how ECG findings are represented.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Row 11", "definition": "Shows derived records. Notice how QSORRES is blank for derived records because there is no corresponding text value for the numeric value shown (see Section 4.1.1.7.1). In this study, QSTPTNUM is an arbitrary number, sponsor defined to aid in sorting.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Row 12", "definition": "The TEST \"Interpretation\" (i.e., the interpretation of the ECG strip as a whole), is \"ABNORMAL \". CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Screening 1", "definition": "2003-04-15T11:58 -36 ECG Example 1 SUPPEG dataset", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 132", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SCREEN I", "definition": "-2 2003-11-26 -2 2 (cont)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SCREEN II", "definition": "-1 2003-11-27 -1 3 (cont)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DAY 10", "definition": "10 2003-12-07T09:02 10 4 (cont)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DAY 15", "definition": "15 2003-12-12 15 CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 134", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char IE", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "IESPID", "definition": "Sponsor-Defined Identifier Char Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an explicit line identifier or defined in the sponsor’s operational database. Example: Inclusion or Exclusion criteria number from CRF.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "INCL03", "definition": "Acceptable mammogram from local radiologist?", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "WEEK -8", "definition": "-56 1999-01-12 -56 4 (cont) 1", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 136", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char LB", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "LBSCAT", "definition": "Subcategory for Lab Test Char *", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 138", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "LBTOXGR", "definition": "Standard Toxicity Grade Num *", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 140", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 2-4", "definition": "Show two records for Alkaline Phosphatase done at the same visit, one day apart. Row 4 shows how to create a derived record (average of the records 2 and 3) and flagged derived (LBDRVFL = “Y”) and as the record to use as baseline (LBBLFL = “Y”).", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 6 and 7", "definition": "Show a suggested use of the LBSCAT variable. It could be used to further classify types of tests within a laboratory panel (i.e., “DIFFERENTIAL”).", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DIFFERENTIAL", "definition": "5.1 10^9/L 2 8 5.1 5.1 8", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Urinalysis", "definition": "7.5 5.0 9.0 7.5 9", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "LBDTC", "definition": "1 (cont) g/dL 3.5 5", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 1", "definition": "Shows an example of a pre-dose urine collection interval (from 4 hours prior to dosing until 15 minutes prior to dosing) with a negative value for LBELTM that reflects the end of the interval in reference to the fixed reference LBTPTREF, the date of which is recorded in LBRFTDTC.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 2 and 3", "definition": "Show an example of post-dose urine collection intervals with values for LBELTM that reflect the end of the intervals in reference to the fixed reference LBTPTREF, the date of which is recorded in LBRFTDTC.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "VISITNUM", "definition": "Row 1 (cont) 0.38 0.38 mmol/L 0.1 0.8", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "INITIAL DOSING", "definition": "2 Row 3 (cont) 0.5 0.5 mmol/L 0.1 0.8", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "LBRFTDTC", "definition": "Row 1 (cont) 1999-06-19T04:00 1999-06-19T07:45", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Dosing", "definition": "1999-06-19T08:00 LB Example 3: This is an example of pregnancy test records, one with a result and one with no result because the test was not performed due to the subject being male.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 142", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char PE", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PESPID", "definition": "Sponsor-Defined Identifier Char Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an explicit line identifier or defined in the sponsor’s operational database. Example: Line number on a CRF.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PECAT", "definition": "Category for Examination Char *", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 144", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "VISITDY", "definition": "Planned Study Day of Visit Num", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PEDTC", "definition": "Date/Time of Examination Char ISO 8601", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PEDY", "definition": "Study Day of Examination Num", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 4-6", "definition": "Show how PESPID is used to show the sponsor-defined identifier, which in this case is a sequence number used for identifying abnormalities within a body system.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rows 4-7", "definition": "Show how PESTRESC is populated if abnormality is dictionary coded. Rows 8, 13 Show how the PECAT variable can be used to indicate a different type of physical examination. In this case, the ophthalmologic examination may have been collected in a separate dataset in the operational database.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "BASELINE", "definition": "1 1999-06-19 1 12 (cont)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "VISIT 1", "definition": "35 1999-07-21 33 CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 146", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char QS", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "QSSCAT", "definition": "Subcategory for Question Char *", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "QSORRES", "definition": "Finding in Original Units Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 148", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 150", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "GH11C", "definition": "Expect health to get worse", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "RP4A", "definition": "Phys. Health-cut down time spent", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "RP4C", "definition": "Phys. Health-limit kind of work", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SCREENING", "definition": "-14 2001-03-20 -10 1 11 (cont) 9", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 152", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char SC", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SCSPID", "definition": "Sponsor-Defined Identifier Char Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an explicit line identifier or defined in the sponsor’s operational database. Perm SDTM 2.2.4 SCTESTCD Subject Characteristic", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 154", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SCDY", "definition": "Study Day of Examination Num", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CQH", "definition": "1999-06-14 CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 156", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Identifier", "definition": "Sponsor-defined reference number. Perhaps pre-printed on the CRF as an explicit line identifier or defined in the sponsor’s operational database. Example: Line number on a CRF. Perm SDTMIG 2.4.4", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "VSSPID", "definition": "Sponsor-Defined Identifier Char Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an explicit line identifier or defined in the sponsor’s operational database.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "VSCAT", "definition": "Category for Vital Signs Char *", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "VSSCAT", "definition": "Subcategory for Vital Signs Char *", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 158", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "VSDY", "definition": "Study Day of Vital Signs", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "VSTPT", "definition": "Planned Time Point Name Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Row 14", "definition": "Shows a value collected in one unit, but converted to selected standard unit.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Row 15", "definition": "Shows the proper use of the STAT variable to indicate \"NOT DONE\" where a reason was collected when a test was not done.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "VSPOS", "definition": "VSORRES VSORRESU VSSTRESC VSSTRESN VSSTRESU 1", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "BASELINE BASELINE 1", "definition": "1 1 1999-06-19T08:45 1 9 (cont)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "BASELINE BASELINE 2", "definition": "2 1 1999-06-19T09:00 1 10 (cont)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 160", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char DA", "definition": "Identifier Two-character abbreviation for the domain most relevant to the observation.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DACAT", "definition": "Category of Assessment Char *", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DA Example 1", "definition": "Example 1 below shows drug accounting for a study with two study meds and one rescue med, all of which are measured in tablets. The sponsor has chosen to add EPOCH from the list of timing variables and to use DASPID and DAREFID for code numbers that appear on the label.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DISPAMT", "definition": "Amount Dispensed Rescue Medication 6", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "TABLETS", "definition": "Study Med Period 1 2004-07-15", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DA Example 2", "definition": "Example 2 is for a study where drug containers, rather than their contents, are being accounted for and the sponsor did not track returns. In this case, the purpose of the accountability tracking is to verify that the containers dispensed were consistent with the randomization. The sponsor has chosen to use DASPID to record the identifying number of the container dispensed or returned.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 162", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char MB", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "MBSPID", "definition": "Sponsor-Defined Identifier Char Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an explicit line identifier or defined in the sponsor's operational database. Example: Organism identifier. For organism identification, MBSPID would remain the same each time the same organism is identified in a new specimen.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 164", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "MBTPT", "definition": "Planned Time Point Name Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "The use of GRPID to relate MS to MB greatly simplifies RELREC because only two records are needed in RELREC to describe the relationship of", "definition": "MB to the many related records in MS. With this method there is no need to create detailed relationships at the subject level. Other standard methods of relating records can also be used, but will produce a more complicated RELREC requiring detail of the relationships by subject. For example, if MBSEQ and MSSEQ are used, then RELREC would contain records for each specimen's MBSEQ, and all of the related MS findings by MSSEQ, for every subject.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 166", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char MS", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "MSSPID", "definition": "Sponsor Defined Identifier Char Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an explicit line identifier or defined in the sponsor's operational database.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 168", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "MSTPT", "definition": "Planned Time Point Name Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 170", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Row 6", "definition": "Shows no organisms have grown from this specimen at Visit 3. Therefore the organism recorded is \"NO GROWTH\".", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Row 1-6", "definition": "Show MBMETHOD being used for reporting the method of testing the sample, e.g. GRAM STAIN or CULTURE PLATE. Microbiology Example 1 MB dataset", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "GRAM STAIN", "definition": "1 2005-06-19T08:00 2 (cont) 2+ FEW", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CULTURE PLATE", "definition": "3 2005-07-06T08:00 If the method of the collection of the sputum is reported (e.g., EXPECTORATION or BIOPSY), this information would go into SUPPMB. suppmb.xpt", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "QUADRANT", "definition": ">=30 COLONIES IN 2ND QUADRANT 1 5 (cont) 0.125 mcg/mL 0.125 0.125 mcg/mL", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "E-TEST", "definition": "Example 5: RELREC to relate MB and MS Row 1, 2 - Show the one-to-many relationship between MB and MS. For any organism found in a microbiology specimen and recorded in MB, there may be multiple findings about that organism recorded in MS. The organism in MB can be linked to its findings in MS because MBGRPID=MSGRPID for any organism within a subject.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 172", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "MSMETHOD", "definition": "1 (cont) 0.25 mcg/mL 0.25 mcg/mL", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "BROTH DILUTION", "definition": "4 (cont) 0.5 mcg/mL 0.5 mcg/mL", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ZONE SIZE", "definition": "7 (cont) 0.25 mcg/mL 0.25 mcg/mL", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 174", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PC", "definition": "Identifier Two-character abbreviation for the domain most relevant to the observation.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 176", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 178", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "All Rows", "definition": "PCTPTREF is a text value of the description of a “zero” time. It should be meaningful. If there are multiple PK profiles being generated, the zero time for each will be different (e.g., a different dose such as \"first dose\", \"second dose\"). In this example it is required to make values of PCTPTNUM and PCTPT unique (See Section 4.1.4.10). Rows 1-4, 12: Sponsor has standardized values of “<0.01” to “0” for this study. Rows 5, 6, 19, 20, 25, 26, 29, and 30: Specimen properties (volume and pH) are submitted as separate rows. Rows 3-6, 17-20, 23-30: The elapsed times for urine samples are based upon the elapsed time (from the reference time point, PCTPTREF) for the end of the specimen collection period. Elapsed time values that are the same for urine and plasma samples have been assigned the same value for PCTPT. For the urine samples, the value in PCEVLINT describes the planned evaluation (or collection) interval relative to the time point. The actual evaluation interval can be determined by determining PCENDTC - PCDTC. Row STUDYID DOMAIN USUBJID PCSEQ PCGRPID PCREFID PCTESTCD", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PCCAT", "definition": "PCSPEC PCORRES PCORRESU PCSTRESCPCSTRESNPCSTRESU 1", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ANALYTE PLASMA", "definition": "<0.1 ng/mL 0 0 ng/mL (Additional variables in dataset example on following page- > ) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "URINE", "definition": "2.4 ng/mL 2.4 2.4 ng/mL 29", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DAY 11", "definition": "11 2001-02-11T14:03 2001-02-11T20:10 11 12H 3 Day 11 Dose 2001-02-11T08:00", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PREDOSE", "definition": "0 Day 11 Dose 2001-02-11T08:00 -PT15M 15 (cont) 0.10 3", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PT6H", "definition": "-PT6H 21 (cont) 0.10 3", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DAY 2", "definition": "2 2001-02-02T08:00 2 24H 3 Day 1 Dose 2001-02-01T08:00", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PT12H", "definition": "-PT6H 27 (cont) 2.00 4", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DAY 12", "definition": "12 2001-02-12T08:00 12 24H 4 Day 11 Dose 2001-02-11T08:00", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PT24H", "definition": "-P12H 31 (cont) 0.10 4", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 180", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DRUG A PARENT", "definition": "Half-life of 2nd exp phase 4.50", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DRUG A METABOLITE", "definition": "THALF_2 Half-life of 2nd exp phase 2.25", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Clearance", "definition": "0.88 L/h (Additional variables in dataset example on following page- > ) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DAY 8", "definition": "2 2001-02-08T18:30 8 600 min 12", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 182", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "RELATING DATASETS", "definition": "If all time-point concentrations in PC are used to calculate all parameters for all subjects, then the relationship between the two datasets can documented as shown in the table below. Note that incorporating the name of the analyte and the day of the collection into the value of --GRPID (or some equivalent method for assigning different values of --GRPID for all the combinations of analytes and reference time points) is necessary when there is more than one reference time point (PCRFTDTC and PPRFTDTC, which are the same for related records), and more than one analyte (PCTESTCD, copied into PPCAT to indicate the analyte with which the parameters are associated), since these variables are part of the natural key (see Section 3.2.1.1) for both datasets. In this case, --GRPID is a surrogate key (Section 3.2.1.1) used for the relationship.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "RELATING RECORDS", "definition": "Four possible examples of different types of relationships between PC and PP records for one drug (DRUG X in this case) are described. For all of these, the actual PC and PP data are the same. The only variables whose values change across the examples are the sponsor-defined PCGRPID and PPGRPID. As in the case for relating datasets above (Section 6.3.10.5.1), --GRPID values must take into account all the combinations of analytes and reference time points, since both are part of the natural key (see Section 3.2.1.1) for both datasets. To conserve space, the PC and PP domains appear only once, but with four --GRPID columns, one for each of the examples. Note that a submission dataset would contain only one --GRPID column with a set of values such as those shown in one of the four columns in the PC and PP datasets, or values defined by the sponsor. The example specifics are as follows: Example 1: All PK time-point-concentration values in the PC dataset are used to calculate all the PK parameters in the PP dataset for both Days 1 and 8 for one subject. Pharmacokinetic Concentrations (PC) Dataset For All Examples Pharmacokinetic Parameters (PP) Dataset For All Examples RELREC Example 1. All PC records used to calculate all PK parameters • Method A (Many to many, using PCGRPID and PPGRPID) • Method B (One to many, using PCSEQ and PPGRPID) • Method C (Many to one, using PCGRPID and PPSEQ) • Method D (One to one, using PCSEQ and PPSEQ) Example 2: Two PC values were excluded from the calculation of all PK parameters for the Day 1 data. Day 8 values are related as per Example 1. RELREC Example 2: Only some records in PC used to calculate all PK parameters • Method A (Many to many, using PCGRPID and PPGRPID) • Method B (One to many, using PCSEQ and PPGRPID) • Method C (Many to one, using PCGRPID and PPSEQ) • Method D (One to one, using PCSEQ and PPSEQ) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 184", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ABC-123-0001", "definition": "12 DY1_DRGX DY1_DRGX DY1_DRGX_A DY1DRGX_D 123-0001-12", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DRUG X", "definition": "First dose of study drug, where drug is DRUG X 2 weeks after start of Element", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PCELTM", "definition": "1 (cont) 9 9 ug/mL 1.00 1", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Day 1 Dose", "definition": "2001-02-01T08:30 PT10H 13 (cont) 10.0 10.0 ug/mL 1.00 2", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Day 8 Dose", "definition": "2001-02-08T08:30 PT8H20M 24 (cont) 11.0 11.0 ug/mL 1.00 2", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 186", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "5 (cont)", "definition": "T1/2, 2 Half-life of 2nd exp phase", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "12 (cont)", "definition": "T1/2, 2 Half-life of 2nd exp phase", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DY8DRGX", "definition": "2 * RELID 1 indicates records with PCSEQ values of 1-12 are related to records with PPGRPID = DY1DRGX. * RELID 2 indicates records with PCSEQ values of 13-24 are related to records with PPGRPID = DY8DRGX. Method C (Many to one, using PCGRPID and PPSEQ)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PPSEQ", "definition": "7 4 The Day 8 relationships are the same as those shown in Example 1. * RELID 1 indicates records with PCSEQ values of 1-4 and 6-12 are related to the record with a PPSEQ value of 1. * RELID 2 indicates records with PCSEQ values of 1-5 and 7-12 are related to the record with a PPSEQ value of 2. * RELID 3 indicates records with PCSEQ values of 1-10 are related to the record with a PPSEQ value of 3. * RELID 4 indicates records with PCSEQ values of 1-12 are related to the records with PPSEQ values of 4-7.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 188", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DY1DRGX", "definition": "1 The Day 8 relationships are the same as those shown in Example 1. * RELID 1 indicates records with PCSEQ values of 1-7 and 10-12 are related to records with PPGRPID = DY1DRGX. * RELID 2 indicates records with PCSEQ values of 13-24 are related to records with PPGRPID = DY8DRGX. Method C (Many to one, using PCGRPID and PPSEQ)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 190", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PPGRPID", "definition": "DY1DRGX_HALF 2 The Day 8 relationships are the same as those shown in Example 1. * RELID 1 indicates records with PCSEQ values of 1-12 are related to records with PPGRPID = DY1DRGX_A * RELID 2 indicates records with PCSEQ values of 1-7 and 10-12 are related to records with PPGRPID = DY1DRGX_HALF. Method C (Many to one, using PCGRPID and PPSEQ)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 192", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PCGRPID", "definition": "DY1DRGX_D 4 The Day 8 relationships are the same as those shown in Example 1. * Same RELID of \"1\" Indicates that Tmax used records with PCGRPID values DY1DRGX_A, DY1DRGX_C, and DY1DRGX_D. * Same RELID of \"2\" Indicates that Cmax used records with PCGRPID values DY1DRGX_A, DY1DRGX_B, and DY1DRGX_D. * Same RELID of \"3\" Indicates that AUC used PCGRPID values DY1DRGX_A, DY1DRGX_B, and DY1DRGX_C. * Same RELID of \"4\" Indicates that all other parameters (PPGRPID = OTHER) used all PC time points: PCGRPID values DY1DRGX_A, DY1DRGX_B, DY1DRGX_C, and DY1DRGX_D. Note that in the above RELREC table, the single records in rows 1, 3, 5, 7, and 9, represented by their --GRPIDs (TMAX, DY1DRGX_C, CMAX, DY1DRGX_B, AUC) could have been referenced by their SEQ values, since both identify the records sufficiently. At least two other hybrid approaches would have been acceptable as well: using PPSEQ and PCGRPIDs whenever possible, and using PPGRPID and PCSEQ values whenever possible. Method D below uses only SEQ values. CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 194", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Conclusions", "definition": "Relating the datasets (6.3.10.5.1, and as described in Section 8.3) is the simplest method; however; all time-point concentrations in PC must be used to calculate all parameters for all subjects. If datasets cannot be related, then individual subject records must be related (Section 8.2). In either case, the values of PCGRPID and PPGRPID must take into account multiple analytes and multiple reference time points, if they exist. Method A, is clearly the most efficient in terms of having the least number of RELREC records, but it does require the assignment of --GRPID values (which are optional) in both the PC and PP datasets. Method D, in contrast, does not require the assignment of --GRPID values, instead relying on the required --SEQ values in both datasets to relate the records. Although Method D results in the largest number of RELREC records compared to the other methods, it may be the easiest to implement consistently across the range of complexities shown in the examples. Two additional methods, Methods B and C, are also shown for Examples 1-3. They represent hybrid approaches, using --GRPID values on only one dataset (PP and PC, respectively) and --SEQ values for the other. These methods are best suited for sponsors who want to minimize the number of RELREC records while not having to assign --GRPID values in both domains. Methods B and C would not be ideal, however, if one expected complex scenarios such as that shown in Example 4. Please note that an attempt has been made to approximate real PK data; however, the example values are not intended to reflect data used for actual analysis. When certain time-point concentrations have been omitted from PP calculations in Examples 2-4, the actual parameter values in the PP dataset have not been recalculated from those in Example1 to reflect those omissions. 6.3.10.7 Suggestions for Implementing RELREC in the Submission of PK Data Determine which of the scenarios best reflects how PP data are related to PC data. Questions that should be considered: 1. Do all parameters for each PK profile use all concentrations for all subjects? If so, create a PPGRPID value for all PP records and a PCGRPID value for all PC records for each profile for each subject, analyte, and reference time point. Decide whether to relate datasets (Section 6.3.10.5.1) or records (Section 6.3.10.5.2, Example 1). If choosing the latter, create records in RELREC for each PCGRPID value and each PPGRPID value (Method A). Use RELID to show which PCGRPID and PPGRPID records are related. Consider RELREC Methods B, C, and D as applicable. 2. Do all parameters use the same concentrations, although maybe not all of them (Example 2)? If so, create a single PPGRPID value for all PP records, and two PCGRPID values for the PC records: a PCGRPID value for ones that were used and a PCGRPID value for those that were not used. Create records in RELREC for each PCGRPID value and each PPGRPID value (Method A). Use RELID to show which PCGRPID and PPGRPID records are related. Consider RELREC Methods B, C, and D as applicable. 3. Do any parameters use the same concentrations, but not as consistently as what is shown in Examples 1 and 2? If so, refer to Example 3. Assign a GRPID value to the PP records that use the same concentrations. More than one PPGRPID value may be necessary. Assign as many PCGRPID values in the PC domain as needed to group these records. Create records in RELREC for each PCGRPID value and each PPGRPID value (Method A). Use RELID to show which PCGRPID and PPGRPID records are related. Consider RELREC Methods B, C, and D as applicable. 4. If none of the above applies, or the data become difficult to group, then start with Example 4, and decide which RELREC method would be easiest to implement and represent. CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 196", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CFOBJ", "definition": "CFORRES CFORRESU CFSTRESC CFSTRESCU VISIT", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "LEFT ARM", "definition": "Perm SDTMIG 2.4.3, SDTMIG 4.1.1.9", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 198", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "VESICLES", "definition": ". 5. Records in CF may or may not relate to records in an Events domain, such as AE or CE. If there are related event records, then RELREC may be used to document the relationships. a. If the clinical event or condition being evaluated does not represent a reportable adverse event, and if no data about the event as a whole (e.g., start date, end date) is collected, then there is no requirement that there be an event record for the clinical event or condition. b. If the clinical event or condition being assessed represents a reportable adverse event, as defined/agreed in the particular study, then it must be recorded in the AE domain, since all adverse events must be recorded in the AE domain. 6. Although no examples for clinical findings related to interventions have been provided, data on findings about interventions are expected to arise and may be stored in CF. 7. The following qualifiers should generally not be used in CF: --BODSYS, --MODIFY, --DTHREL, --SEV. CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Date of Collection", "definition": "Did you have the following?", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 200", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SYMPTOMS", "definition": "INVESTIGATOR GERD SYMPTOM MEASUREMENT (IF SYMPTOM OCCURRED) OCCURRED? Yes/No VOLUME (mL) Total for all episodes", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "NAUSEA", "definition": "1 1 1 2006-02-02 10", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "XYZ-701-002", "definition": "13 NUMEPISD Number of episodes DIARRHEA 1 1 2 2006-02-03 14", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "NOT DONE 2006-02-03", "definition": "CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "VOMIT", "definition": ">10 >10 1 2006-02-02 4", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 202", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Visit", "definition": "1 2 3 4 5 6", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AE Domain (For clarity, only selected variables are shown.)", "definition": "Row 1 shows the record for a verbatim term of \"Morning queasiness\", for which the maximum severity over the course of the event was \"Moderate.\" Row 2 shows ????", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CF domain", "definition": "Rows 1-4 show severity data collected at the four visits that occurred between the start and end of the AE 'Morning queasiness'. CFOBJ = NAUSEA, which is the value of AEDECOD in the associated AE record. Rows 5-6 show severity data collected at the two visits that occurred between the start and end of the AE 'Watery stools'. CFOBJ = DIARRHOEA, which is the value of AEDECOD in the associated AE record.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "INTRODUCTION", "definition": "7.1.1 PURPOSE OF TRIAL DESIGN MODEL ICH E3, Guidance for Industry, Structure and Content of Clinical Study Reports, Section 9.1, calls for a brief, clear description of the overall plan and design of the study, and supplies examples of charts and diagrams for this purpose in Annex IIIa and Annex IIIb. Each Annex corresponds to an example trial, and each shows a diagram describing the study design and a table showing the schedule of assessments. The Trial Design Model in the SDTM provides a standardized way to describe those aspects of the planned conduct of a clinical trial shown in the study design diagrams of these examples. The standard Trial Design Datasets will allow reviewers to: • clearly and quickly grasp the design of a clinical trial • compare the designs of different trials • search a data warehouse for clinical trials with certain features • compare planned and actual treatments and Visits for subjects in a clinical trial. Modeling a clinical trial in this standardized way requires the explicit statement of certain decision rules that may not be addressed or may be vague or ambiguous in the usual prose protocol document. Prospective modeling of the design of a clinical trial should lead to a clearer, better protocol. Retrospective modeling of the design of a clinical trial should ensure a clear description of how the trial protocol was interpreted by the sponsor. 7.1.2 DEFINITIONS OF TRIAL DESIGN CONCEPTS A clinical trial is a scientific experiment involving human subjects, which is intended to address certain scientific questions (the objectives of the trial). [See CDISC glossary for more complete definitions of clinical trial and objective.] Trial Design: The design of a clinical trial is a plan for what will be done to subjects, and what data will be collected about them, in the course of the trial, to address the trial's objectives. Epoch: As part of the design of a trial, the planned period of subjects' participation in the trial is divided into Epochs. Each Epoch is a period of time that serves a purpose in the trial as a whole. Typically, the purpose of an Epoch will be expose subjects to a treatment, or to prepare for such a treatment period (e.g., determine subject eligibility, wash out previous treatments) or to gather data on subjects after a treatment has ended. Arm: An Arm is a planned path through the trial. This path covers the entire time of the trial. The group of subjects assigned to an Arm is also often colloquially called an Arm. The group of subjects assigned to an Arm is also often called a treatment group, and in this sense, an Arm is equivalent to a treatment group. Study Cell: Since the trial as a whole is divided into Epochs, each planned path through the trial (i.e., each Arm) is dived into pieces, one for each Epoch. Each of these pieces is called a Study Cell. Thus, there is a study cell for each combination of Arm and Epoch. Each Study Cell represents an implementation of the purpose of its associated Epoch. For Epochs whose purpose is to expose subjects to treatment, the Study Cells associated with that Epoch expose subjects to particular treatments. For example, a 3-Arm parallel trial might have a Treatment Epoch whose purpose is to expose subjects to one of three study treatments, placebo, investigational product, or active control. There would be three Study Cells associated with the Treatment Epoch, one for each Arm. Each of these Study Cells exposes the subject to one of the three study treatments.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 204", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 206", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Domain", "definition": "A collection of observations with a topic-specific commonality about a subject", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rule", "definition": "Rule that expresses the criterion in computer-executable form.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 208", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Randomization", "definition": "Evaluation for disease progression Evaluation/consent for surgery", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "In the next diagram, the Epochs of the trial have been identified. The Epochs are represented by the rectangles", "definition": "\"behind\" the blocks representing the Elements. In this simple trial, which has three Elements in each Arm, there are three Epochs. There is not always a one-to-one relationship between the Elements in the Arms of the trial and the Epochs of the trial, but such a relationship is common, particularly for blinded trials.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 210", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Blind", "definition": "CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Drug B", "definition": "First dose of study drug, where drug is Drug B 2 weeks after start of Element", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 212", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "P-5-10", "definition": "CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 214", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Placebo-5mg-10mg", "definition": "Second Treatment Epoch 4 5 5 mg 5", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Third Treatment Epoch", "definition": "6 10 10 mg 14", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "First Treatment Epoch", "definition": "2 5 5 mg 17", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "TA", "definition": "CRS/No P/S CRS/No P/S Contin/", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 216", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rescue", "definition": "See Section 7.2.3.1 for additional discussion of when a decision point in a trial design should be considered to give rise to a new Arm. CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Screen Study Drug", "definition": "Blind/Rescue Blind/Open A Study Drug Open Drug A", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 218", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Treatment A", "definition": "5 days after start of", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Treatment B", "definition": "Assigned to Rescue on basis of", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 220", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "In the diagram below, the trial is considered to have one Epoch for each cycle, with each Epoch consisting of two", "definition": "Elements, a treatment Element and a rest Element. The choice between these alternatives is a somewhat subjective one. In oncology trials, there is a strong tradition of discussing \"cycles\" and the second diagram is more consistent with this tradition. However, if there were important analyses which distinguished between assessments or events in the Treatment and Rest Elements, then the first assignment of Epochs might better support those analyses.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 222", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "The logistics of dosing mean that few oncology trials are blinded, but the diagrams we have drawn might represent a", "definition": "blinded trial. If this trial is blinded, then the diagram below, based on the one-Epoch-per-cycle alternative, shows the trial from the viewpoint of blinded participants.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 224", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rest", "definition": "Last dose of previous treatment cycle + 24 hrs At least 16 days after start of Element and WBC recovered 5", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 226", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "R A Follow", "definition": "Trt A = Treatment A R A = Rest A B = Treatment B", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 228", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "The Trial Design Matrix below corresponds to the last diagram for this trial, with one Epoch per cycle.", "definition": "Trial Design Matrix for Example Trial 5, with one Epoch per Cycle", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rest A", "definition": "If disease progression, go to Follow-up Epoch 8", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rest B", "definition": "If disease progression, go to Follow-up Epoch 18", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 230", "definition": "CDISC. © 2007. All rights reserved July 1025, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "The Trial Design Matrix for this trial assumes that Epochs are assigned as in the diagram above. Note that in this", "definition": "trial, there are eight Elements in the treatment Epoch for Arm A, and six Elements in the treatment Epoch for Arm B. Trial Design Matrix for Example Trial 6", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 232", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 234", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "No surgery", "definition": "However, it is also possible to see this study as a two-Arm trial, but with major \"skip forward\" arrows for some subjects, as illustrated in the following diagram. 3-5w", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Arm 2", "definition": "CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Progression", "definition": "Example Trial 7, RTOG 93-09: Five Arms and Four Epochs", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 236", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Induction", "definition": "Chemo + RT 3-5 w", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 238", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CBST", "definition": "Addl Chemo + Rad Boost 4", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Induction CR", "definition": "No Progression and eligible and consented to surgery 10", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "FU", "definition": "Off Treatment Follow-up CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "up", "definition": "The Trial Arms dataset for the trial is also simpler for the two-Arm version of the trial. Note that this version has rules in the TRANS column that appeared in the BRANCH column in the five-Arm version of the Trial Arms dataset. In the five-Arm view of the trial, these decision points are considered to be branches between Arms, while in the two-Arm view of the trial, these are considered to represent variations within an Arm. Trial Arms dataset for Example Trial 7, as a tow-Arm trial", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 240", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DEFINING EPOCHS", "definition": "The series of examples in Section 7.2.2 provides a variety of scenarios and guidance about how to assign Epoch in those scenarios. In general, assigning Epochs for blinded trials is easier than for unblinded trials. The blinded view of the trial will generally make the possible choices clear. For unblinded trials, the comparisons that will be made between Arms can guide the definition of Epochs. For trials that include many variant paths within an Arm, comparisons of Arms will mean that subjects on a variety of paths will be included in the comparison, and this is likely to lead to definition of broader Epochs. 7.2.3.4", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "RULE VARIABLES", "definition": "The Branch and Transition columns shown in the example tables are variables with a Role of 'Rule.' The values of a Rule variable describe conditions under which something is planned to happen. At the moment, values of Rule variables are text. At some point in the future, it is expected that these will become executable code. Other Rule variables are present in the Trial Elements and Trial Visits datasets. CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 242", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "TAETORD", "definition": "Orders the Elements within an Arm ETCD, ELEMENT Name an Element within the Arm", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "TABRANCH", "definition": "Indicate a branch in the trial design at the end of the Element. A branch may be a randomization or another method of assigning subjects to Arms", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "TATRANS", "definition": "Indicates how to decide where a subject should go at the end of the Element. The default rule is, 'go to the next Element in sequence.'", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Epoch", "definition": "1 hour after start of Treatment", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "TE", "definition": "10 10 mg First dose of a treatment Epoch, where dose is 10 mg drug 2 weeks after start of Element", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "TEENRL", "definition": "Rule for End of Element Char", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 244", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Informed Consent", "definition": "Screening assessments are complete, up to 2 weeks after start", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Eligibility confirmed", "definition": "2 weeks after start of Element", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Placebo", "definition": "1 2 3 4 5 CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "P14D", "definition": "Trial Elements Dataset for Example Trial 2", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "P21D", "definition": "The Trial Elements dataset for Example Trial 4 illustrates Element end rules for Elements that are not of fixed duration. The Screen Element in this study can be up to 2 weeks long, but may end earlier, so is not of fixed duration. The Rest Element has a variable length, depending on how quickly WBC recovers. Note that the start rules for the A and B Elements have been written to be suitable for a blinded study. Trial Elements Dataset for Example Trial 4", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Trt A", "definition": "First dose of treatment in Element, where drug is", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Trt B", "definition": "First dose of treatment Element, where drug is Treatment B 5 days after start of Element", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "P28D", "definition": "CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 246", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Step Question", "definition": "How step question is answered by information in the Trial Design datasets 1 Should the subject leave the current Element? Criteria for ending the current Element are in TEENRL in the TE dataset. 2 Which Element should the subject enter next? • If there is a branch point at this point in the trial, evaluate criteria described in TABRANCH (e.g., randomization results) in the TA dataset • otherwise, if TATRANS in the TA dataset is populated in this Arm at this point, follow those instructions • otherwise, move to the next Element in this Arm as specified by TAETORD in the TA dataset. 3 What does the subject do to enter the next Element? The action or event that marks the start of the next Element is specified in TESTRL in the TE dataset Note that the subject is not \"in limbo\" during this process. The subject remains in the current Element until Step 3, at which point the subject transitions to the new Element. There are no gaps between Elements. From this table, it is clear that executing a transition depends on information that is split between the Trial Elements and the Trial Arms datasets. It can be useful, in the process of working out the Trial Design datasets, to create a dataset that supplements the Trial Arms dataset with the TESTRL, TEENRL, and TEDUR variables, so that full information on the transitions is easily accessible. However, such a working dataset is not an SDTM dataset, and should not be submitted. The following table shows a fragment of such a table for Example Trial 4. Note that for all records that contain a particular Element, all the TE variable values are exactly the same. Also, note that when both TABRANCH and TATRANS are blank, the implicit decision in Step 2 is that the subject moves to the next Element in sequence for the Arm.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Screening assessments", "definition": "are complete, up to 2", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Last dose of previous", "definition": "treatment cycle + 24 hrs 16 days after start of", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "P5D", "definition": "Note that both the first and fourth rows of this dataset involve the same Element, Trt A, and so TESTRL is the same for both. The activity that marks a subject's entry into the fourth Element in Arm A is \"First dose of treatment Element, where drug is Treatment A.\" This is not the subject's very first dose of Treatment A, but it is their first dose in this Element, which is in the Second Treatment Epoch. 7.3.4 RECAP OF TRIAL ELEMENTS VARIABLES", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "TESTRL", "definition": "Describe the \"transition event\" that defines the start of the Element TEENRL, TEDUR Describe when the Element should end (TEDUR is used only if the Element is of fixed duration) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 248", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "TV", "definition": "5 2 weeks after start of Treatment", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Start of Screen Epoch", "definition": "1 hour after start of Visit 2", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "At Trial Exit", "definition": "Although the start and end rules in this example reference the starts and ends of Epochs, the start and end rules of some Visits for trials with Epochs that span multiple Elements will need to reference Elements rather than Epochs. When an Arm includes repetitions of the same Element, it may be necessary to use TAETORD as well as an Element name to specify when a Visit is to occur. 7.4.3 TRIAL VISITS ISSUES 7.4.3.1 IDENTIFYING TRIAL VISITS In general, a trial's Visits are defined in its protocol. The term 'Visit' reflects the fact that data in out-patient studies is usually collected during a physical Visit by the subject to a clinic. Sometimes a Trial Visit defined by the protocol may not correspond to a physical Visit. It may span multiple physical Visits, as when screening data may be collected over several clinic Visits but recorded under one Visit name (VISIT) and number (VISITNUM). A Trial Visit may also represent only a portion of an extended physical Visit, as when a trial of in-patients collects data under multiple Trial Visits for a single hospital admission. Diary data and other data collected outside a clinic may not fit the usual concept of a Trial Visit, but the planned times of collection of such data may be described as 'Visits' in the Trial Visits dataset if desired. 7.4.3.2", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "TRIAL VISIT RULES", "definition": "Visit start rules are different from Element start rules because they usually describe when a Visit should occur, while Element start rules describe the moment at which an Element is considered to start. There are usually gaps between Visits, periods of time that do not belong to any Visit, so it is usually not necessary to identify the moment when one Visit stops and another starts. However, some trials of hospitalized subjects may divide time into Visits in a manner more like that used for Elements, and a transition event may need to be defined in such cases. Visit start rules are usually expressed relative to the start or end of an Element or Epoch, e.g., '1-2 hours before end of First Wash-out' or '8 weeks after end of 2nd Treatment Epoch.' Note that the Visit may or may not occur during", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 250", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CONTINGENT VISITS", "definition": "Section 5.3.1, which describes the Subject Visits dataset, describes how records for unplanned Visits are incorporated. It is sometimes difficult to decide exactly what constitutes an \"unplanned Visit\" versus a \"contingent Visit, \" a Visit that is contingent on a \"trigger\" event, i.e., a Visit that the protocol says should take place under certain circumstances. Also, for certain contingent assessments, it can be difficult to decide whether performing that assessment constitutes a Visit. Contingent Visits can be included in the Trial Visits table, with start rules that describe the circumstances under which they will take place. Since values of VISITNUM must be assigned to all records in the Trial Visits dataset, a contingent Visit must be assigned a value of VISITNUM, but that value may not be a \"chronological\" value, due to the uncertain timing of the Visit. 7.4.4 RECAP OF TRIAL VISITS VARIABLES", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Identifiers", "definition": "VISIT, VISITNUM, VISITDY Name and order the Visits ARM, ARMCD Blank if Visit schedule does not depend on Arm. Name of the ARM if Visit schedule does depend on Arm.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "TVSTRL", "definition": "Rule describing when the Visit should start. Usually expressed relative to the start or end of an Epoch or Element.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "TVENRL", "definition": "Rule describing when the Visit should end. Usually expressed relative to the start of an Epoch or Element. CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 252", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Char TS", "definition": "Identifier Two-character abbreviation for the domain.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "OBJPRIM", "definition": "Trial Primary Objective TO INVESTIGATE THE SAFETY AND EFFICACY OF TWO DOSES 14", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Trial Title", "definition": "No controlled terminology. Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "TRT", "definition": "Reported Name Of Test Product Investigational New Drug 15", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 254", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "and Safety of New", "definition": "Drug (up to 16 mg/day) in the", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SAFETY", "definition": "CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ADDON", "definition": "Test Product Is Added On To", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DOSE", "definition": "Dose Per Administration 200 9", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "OBJSEC", "definition": "Trial Secondary Objective COMPARE SAFETY PROFILES", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PLANSUB", "definition": "Planned Number Of Subjects 210 16", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 256", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "and Data", "definition": ".The fixed structures of the SDTM general observation classes may restrict the ability of sponsors to represent all the data they wish to submit. Collected data that may not entirely fit includes relationships between records within a domain, records in separate domains, and sponsor-defined “variables”. As a result, the SDTM has methods to represent five distinct types of relationships, all of which are described in more detail in subsequent sections. These include the following: • Section 8.1 describes a relationship between a group of records for a given subject within the same domain. • Section 8.2 describes a relationship between independent records (usually in separate datasets) for a subject, such as a concomitant medication taken to treat an adverse event. • Section 8.3 describes a relationship between two (or more) datasets where records of one (or more) dataset(s) are related to record(s) in another dataset (or datasets). • Section 8.4 describes a method for representing the dependent relationship where data that cannot be represented by a standard variable within a general-observation-class domain record (or records) can be related back to that record. • Section 8.5 describes a dependent relationship between a comment in the Comments domain (see also Section 5.2) and a parent record (or records) in other datasets, such as a comment recorded in association with an adverse event. • Section 8.6 discusses the concept of related datasets and whether to place additional data in a separate domain or a Supplemental Qualifier special-purpose dataset, and the concept of modeling findings data that refers to data in another general-observation-class dataset. All relationships make use of the standard domain identifiers, STUDYID, DOMAIN, and USUBJID. In addition, the variables IDVAR and IDVARVAL are used for identifying the record-level merge/join keys. These keys are used to tie information together by linking records. The specific set of identifiers necessary to properly identify each type of relationship is described in detail in the following sections. Examples of variables that could be used in IDVAR include the following variables: • The Sequence Number (--SEQ) variable uniquely identifies a record for a given USUBJID within a domain. The variable --SEQ is required in all domains except DM. For example, if subject 1234-2003 has 25 adverse event records in the adverse event (AE) domain, AESEQ values for this subject should be the numbers 1 to 25. Then numbers in --SEQ numbers may not always be represented sequentially in cases where the sponsor assigns the numbers early in the process and subsequently deletes some records, or uses blocks of numbers to sequence data coming from different sources. • The Reference Identifier (--REFID) variable can be used to capture a sponsor-defined or external identifier, such as an identifier provided in an electronic data transfer. Some examples are lab-specimen identifiers and ECG identifiers. --REFID is permissible in all domains, but never required. Values for --REFID are sponsor -defined and can be any alphanumeric strings the sponsor chooses, consistent with their internal practices. • The Grouping Identifier (--GRPID) variable, used to link related records for a subject within a dataset, is explained below in Section 8.1.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 258", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "THE --GRPID VARIABLE", "definition": "The optional grouping identifier variable --GRPID is permissible in all domains that are based on the general observation classes. It is used to identify relationships between records within a USUBJID within a single domain. An example would be Intervention records for a combination therapy where the treatments in the combination varies from subject to subject. In such a case, the relationship is defined by assigning the same unique character value to the --GRPID variable. The values used for --GRPID can be any values the sponsor chooses; however, if the sponsor uses values with some embedded meaning (rather than arbitrary numbers), those values should be consistent across the submission to avoid confusion. It is important to note that --GRPID has no inherent meaning across subjects or across domains. Using --GRPID in the general-observation-class datasets can reduce the number of records in the RELREC, SUPP--, and CO datasets when those datasets are submitted to describe relationships/associations for records or values to a ‘group’ of general-observation-class records. 8.1.1 --GRPID EXAMPLE The following table illustrates how to use --GRPID in the Concomitant Medications (CM) domain to identify a combination therapy. In this example, both subjects 1234 and 5678 have reported two combination therapies, each consisting of three separate medications. Each component of a combination is given the same value for CMGRPID. Note that for USUBJID 1234, the medications for CMGRPID = ‘COMBO THPY 1’ (Rows 1-3) are different from the medications for CMGRPID = ‘COMBO THPY 2 (Rows 4-6). Likewise, for USUBJID 5678, the medications for CMGRPID = ‘COMBO THPY 1’ (Rows 7-9) are different from the medications for CMGRPID = ‘COMBO THPY 2’ (Rows 10-12). Additionally, the medications for Subject 1234 CMGRPID = ‘COMBO THPY 1’ and CMGRPID = ‘COMBO THPY 2’ (Rows 1-6) are different from the medications for Subject 5678 CMGRPID = ‘COMBO THPY 1’ and CMGRPID = ‘COMBO THPY 2’ (Rows 7-12). This example illustrates how CMGRPID groups information only within a subject within a domain. Row STUDYID DOMAIN USUBJID CMSEQ CMGRPID", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "mg", "definition": "2004-03-21 2004-03-22 CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 260", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Study Identifier Char", "definition": "Unique identifier for a study", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "In the sponsor's operational database, these datasets may have existed as either separate datasets that were merged", "definition": "for analysis, or one dataset that may have included observations from more than one general observation class (e.g., Events and Findings). The value in IDVAR must be the name of the key used to merge/join the two datasets. In the above example, the --SPID variable is used as the key to identify the related observations. The values for the --SPID variable in the two datasets are sponsor defined. Although other variables may also serve as a single merge key when the corresponding values for IDVAR are equal, --SPID or --REFID are typically used for this purpose. The variable RELTYPE identifies the type of relationship between the datasets. The allowable values are ONE and MANY. This information defines how a merge/join would be written, and what would be the result of the merge/join. The possible combinations are: 1. ONE and ONE. This combination indicates that there is NO hierarchical relationship between the datasets and the records in the datasets. Only One record from each dataset will potentially have the same value of the IDVAR within USUBJID. 2. ONE and MANY. This combination indicates that there IS a hierarchical (parent/child) relationship between the datasets. One record within USUBJID in the dataset identified by RELTYPE=ONE will potentially have the same value of the IDVAR with many (one or more) records in the dataset identified by RELTYPE=MANY. 3. MANY and MANY. This combination is unusual and challenging to manage in a merge/join, and may represent a relationship that was never intended to convey a usable merge/join (such as in described for PC and PP in Section 6.3.10.5). Since IDVAR identifies the keys that can be used to merge/join records between the datasets, the root values (i.e., SPID in the above example) for IDVAR must be the same for both records with the same RELID. --SEQ cannot be used because --SEQ only has meaning within a subject within a dataset, not across datasets.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 262", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 264", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Abbreviation", "definition": "Char * Domain Abbreviation of the Parent record(s).", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Variable", "definition": "Char * Identifying variable in the dataset that identifies the related record(s). Examples: --SEQ, --GRPID.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Variable Name", "definition": "Char * The short name of the Qualifier variable, which is used as a column name in a domain view with data from the parent domain. The value in QNAM cannot be longer than 8 characters, nor can it start with a number (e.g., '1TEST'). QNAM cannot contain characters other than letters, numbers, or underscores. This will often be the column name in the sponsor’s operational dataset.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Evaluator", "definition": "Char * Used only for results that are subjective (e.g., assigned by a person or a group). Should be null for records that contain objectively collected or derived data. Some examples include ADJUDICATION COMMITTEE, STATISTICIAN, DATABASE ADMINISTRATOR, CLINICAL COORDINATOR, etc.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SPUNFL", "definition": "Any Time in Spec. Unit?", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "RLCNDF", "definition": "Visit Related to Study Med Cond.?", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SUPPQUAL Variables", "definition": "HO Events General Observation Class Custom Dataset with SUPPHO Supplemental Qualifiers dataset: The shading in the two datasets below is used to differentiate the three hospitalization records for which data are shown. Note that for Rows 1-7 in the SUPPHO dataset, RDOMAIN= HO, USUBJID = 0001, IDVAR = HOSEQ, and IDVARVAL = 1. These three values (along with STUDYID and USUBJID) allow these seven SUPPHO records to be linked to the HO dataset record in Row 1 which has value in HOSEQ = 1 for Subject 0001. Likewise, SUPPHO dataset rows 8-14 are linked to the HO dataset record where HOSEQ = 2 for the same subject, and SUPPHO dataset rows 15-21 are linked to the HO dataset record where HOSEQ =1 for Subject 0002. ho.xpt: Hospitalization (modeled as a custom Events general-observation-class domain) Row STUDYID DOMAIN USUBJID HOSEQ", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "HO", "definition": "0002 1 Hospitalization 2004-01-21 2004-01-22 P1D", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 266", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "HOSEQ", "definition": "1 RLCNDF Visit Related to Study Med Cond.?", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Comments Examples", "definition": "The table below shows the following: • A comment unrelated to any specific domain or record, because it was collected on a separate comments page/screen (Row 1) • A comment related to a specific domain (PE in this example), but not to any specific record because it was collected on the bottom of the PE page without any indication of specific records it applies to. COREF is populated with the text ‘VISIT 7’ to show this comment came from the VISIT 7 PE page (Row 2) • Comments related to parent records:", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "A comment related to multiple VS records with VSGRPID=’VS2’", "definition": "• Three options are available for representing a comment unrelated to any specific general observation class record(s) because it was collected on a separate comments page/screen, but the page was associated with a specific visit:", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 268", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "A comment related to a Subject Visit record in SV (Row 6). The RDOMAIN variable is populated", "definition": "with SV (the Subject Visits domain) and the variables IDVAR and IDVARVAL are populated with the key variable name and value of the parent Subject-Visit record.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "COREF is populated to indicate that the comment reference is “VISIT 4”. RDOMAIN, IDVAR,", "definition": "and IDVARVAL are not populated.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "VISIT 4", "definition": "PRINCIPAL INVESTIGATOR PRINCIPAL INVESTIGATOR 4 8.6 RELATING FINDINGS OBSERVATIONS TO EVENTS OR INTERVENTIONS USING --OBJ The Clinical Findings domain introduces the --OBJ variable. A record in CF may have a parent record in an event or intervention domain, although a parent record in another domain is not required. When there is a parent-child relationship between an event or intervention record and a CF record, --OBJ establishes that relationship. See the following for further information: • Section 2.4.3 for a definition of the --OBJ variable • Section 6.3.11 for the Clinical Findings domain definition and examples illustrating the use of the --OBJ variable. CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 270", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "FINDINGS ABOUT EVENTS", "definition": "This section discusses events, findings, and findings about events. The relationship between interventions, findings, and findings about interventions is similar. The names of the new Clinical Events (Section 6.2.5) and Clinical Findings (Section 6.3.11) domains are similar because both are intended to hold data about \"clinical\" events or conditions. The Clinical Findings domain was specially created to store findings about events. This section discusses events and findings generally, but it is particularly useful for understanding the distinction between the CE and CF domains. There may be several sources of confusion about whether a particular piece of data belongs in an event record or a findings record. One generally thinks of an event as something that happens spontaneously, and has a beginning and end; however, one should consider the following: • Events of interest in a particular trial may be pre-specified, rather than collected as free text. • Some events may be so long lasting in that they are perceived as \"conditions\" rather than \"events\", and their beginning and end dates are not of interest. • Some variables or data items one generally expects to see in an Events record may not be present. For example, a post-marketing study might collect the occurrence of certain adverse events, but no dates. • Properties of an Event may be measured or assessed, and these are then treated as findings about events, rather than as events. • Some assessments of events (e.g., severity, relationship to study treatment) have been built into the SDTM Events model as qualifiers, rather than being treated as findings about events. • Sponsors may choose how they define an Event. For example, adverse event data may be submitted using one record that summarizes an event from beginning to end, or using one record for each change in severity. The structure of the data being considered, although not definitive, will often help determine whether the data represent an Event or a Finding. The structural questions below may assist sponsors in deciding where data should be placed in SDTM.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Question", "definition": "Interpretation of Answers What date/times are collected? • If the dates collected are start and/or end dates, then data are probably about an event or Intervention. • If the dates collected are dates of assessments, then data probably represents a Finding. • If dates of collection are different from other dates collected, it suggests that data are historical, or that it is about an Event or Intervention that happened independently of the study schedule for data collection. Is verbatim text collected, and then coded? • 'Yes' answer suggests that this is Events or Interventions general- observation-class data. However, Findings general-observation-class data from an examination that identifies abnormalities may also be coded. Note that for Events and Interventions general-observation-class data, the topic variable is coded, while for Findings general-observation-class data, it is the result that is coded. • A 'No' answer is inconclusive. It does not rule out Events or Interventions general-observation-class data, particularly if Events or Interventions are pre-specified; it also does not rule out Findings general observation class data. If this is data about an event, does it apply to the event as a whole? • ‘Yes” answer suggests this is traditional Events general-observation-class data, and should have a record in an Events domain. • 'No' answer suggests that there are multiple time-based findings about an event, and that this data should be treated as Clinical Findings data. The Events general observation class is intended for observations about a clinical event as a whole. Such observations typically include what the condition was, captured in --TERM (the topic variable), and when it happened (captured in its start and/or end dates). Other qualifier values collected (severity, seriousness, etc.) apply to the totality of the event. Note that sponsors may choose how they define the \"event as a whole.\" Data that does not describe the event as a whole should not be stored in the record for that event or in a --SUPP record tied to that event. If there are multiple assessments of an event, then each should be stored in a separate CF record. When data related to an event does not fit into one of the existing Event general observation class qualifiers, the first question to consider is whether the data represents information about the event itself, or whether it represents data about something (a Finding or Intervention) that is associated with the event. • If the data consist of a finding or intervention that is associated with the event, it is likely that it can be stored in a relevant Findings or Intervention general observation class dataset, with the connection to the Event record being captured using RELREC. For example, if a subject had a fever of 102 that was treated with aspirin, the fever would be stored in an adverse event record, the temperature could be stored in a vital signs record, and the aspirin could be stored in a concomitant medication record, and RELREC might be used to link those records. • If the data item contains information about the event, then consider storing it as a Supplemental Qualifier. However, a number of circumstances may rule out the use of a Supplemental Qualifier: • The data are measurements that need units, normal ranges, etc. • The data are about the non-occurrence or non-evaluation of a pre-specified Adverse Event, data that may not be stored in the AE domain, since each record in the AE domain must represent an reportable event that occurred. If a supplemental qualifier is not appropriate, the data may be stored in Clinical Findings. Section 6.3.11 provides additional information and examples. CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 272", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Appendices", "definition": "APPENDIX A: CDISC SDS TEAM *", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Company", "definition": "Fred Wood, Team Leader Octagon Research Solutions, Inc. Wayne Kubick, Past Team Leader", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "FDA Observer", "definition": "* Individuals having met membership criteria as of publication date. CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ATC code", "definition": "Anatomic Therapeutic Chemical code from WHO Drug", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AUC", "definition": "Area under the curve (PK and PD)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "BID", "definition": "Twice a Day (Latin: bis in die)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CDISC", "definition": "Clinical Data Interchange Standards Consortium", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CMAX", "definition": "Concentration maximum; used in pharmacokinetics and bioequivalence testing to indicate maximum plasma concentration for a drug", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "CTCAE", "definition": "Common Terminology Criteria for Adverse Events", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Dataset", "definition": "A collection of structured data in a single file", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Health Level 7", "definition": "HPLC/MS High Performance Liquid Chromatography/Mass Spectrometer", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ICD9", "definition": "International Classification of Diseases, 9th revision. See also MedDRA.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ICH", "definition": "International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ICH E2A", "definition": "ICH guidelines on Clinical Safety Data Management : Definitions and Standards for Expedited", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ICH E3", "definition": "ICH guidelines on Structure and Content of Clinical Study Reports", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ICH E9", "definition": "ICH guidelines on Statistical Principles for Clinical Trials", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ISO 3166", "definition": "ISO country codes. The SDTM uses the three-character format.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ISO 8601", "definition": "ISO character representation of dates, date/times, intervals, and durations of time", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "LOINC", "definition": "Logical Observation, Identifiers, Names, and Codes", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "MedDRA", "definition": "Medical Dictionary for Regulatory Activities (new global standard medical terminology designed to supersede other terminologies (such as COSTART and ICD9) used in the medical product", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PD", "definition": "Pharmacodynamics. The effect of the drug on physiological measures in the subject.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "PK", "definition": "Pharmacokinetics. The study of the absorption, distribution, metabolism and excretion of a drug.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "QD", "definition": "Every Day (Latin: quaque die)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SDS", "definition": "Submission Data Standards. Also the name of the Team that created the SDTM and SDTMIG", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SDTMIG", "definition": "Submission Data Standards SDTM Implementation Guide for Human Clinical Trials (this document)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SEND", "definition": "Standard for Exchange of Non-Clinical Data", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SF-36", "definition": "A multi-purpose, short-form health survey with 36 questions", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SNOMED", "definition": "Systematized Nomenclature of Medicine (a dictionary)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SOC", "definition": "CDISC System Organ Class --BODSYS Populated using a code value in the list of controlled terms, codelist SOC (C66783) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "TID", "definition": "Three Times Daily (Latin: ter in die)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "TMAX", "definition": "The time after dosing when Cmax occurs (PK) or when the maximum effect is observed (PD)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "UUID", "definition": "Universally Unique Identifier V3.x Version 3.1 of the SDSIG and all subsequent versions of the SDTMIG", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "WHODRUG", "definition": "World Health Organization Drug Dictionary", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 274", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Treatment", "definition": "--CAN Populated using a code value in the list of controlled terms, codelist ACN (C66767) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Age Unit", "definition": "Populated using a code value from the list of controlled terms, codelist AGEU (C66781) at http://www.cdisc.org/standards/terminology/index.html Units are associated with", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AESEV", "definition": "Populated using a code value in the list of controlled terms, codelist AESEV (C66769) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ETHNIC", "definition": "Will be changed to Subject Ethnic Group in the future", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Form", "definition": "--DOSFRM Populated using a code value in the list of controlled terms, codelist FRM (C66726) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Size", "definition": "Controlled Terms for when VSTESTCD = FRMSIZE (Frame Size) Populated using a code value in the list of controlled terms, codelist SIZE (C66733) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "IECAT", "definition": "Populated using a code value in the list of controlled terms, codelist IECAT (C66797) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "LBTESTCD", "definition": "Populated using a code value in the list of controlled terms, codelist LBTESTCD (C65047) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "LBTEST", "definition": "Populated using a code value in the list of controlled terms, codelist LBTEST (C65154) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "See also LBTESTCD", "definition": "CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 276", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Non-Completion", "definition": "DSDECOD when DSCAT = “DISPOSITION EVENT” Populated using a code value in the list of controlled terms, codelist NCOMPLT (C66727) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "No Yes Response", "definition": "--PRESP, --OCCUR, --SCAN, --SCONG, --SDISAB, --SDTH, --SHOSP, --SLIFE, --SOD, --SMIE, --CONTRT, --BLFL, --FAST, --DRVFL, ADDON (Trial Summary Parameter) Populated using a code value in the list of controlled terms, codelist NY (C66742) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Outcome of Event", "definition": "--OUT Populated using a code value in the list of controlled terms, codelist OUT (C66768) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ROUTE", "definition": "Route of Administration --ROUTE Populated using a code value in the list of controlled terms, codelist ROUTE (C66728) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "SEX", "definition": "Populated using a code value in the list of controlled terms, codelist SEX (C66731) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "VSTESTCD", "definition": "Populated using a code value in the list of controlled terms, codelist VSTESTCD (C66741) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "VSTEST", "definition": "Populated using a code value in the list of controlled terms, codelist VSTEST (C67153) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "See also VSTESTCD", "definition": "CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Applicable", "definition": "Added as a “restricted prefix” and variable naming prefix - see Appendix D. Do not use as a Domain Code. Included as a value in the list of controlled terms, codelist Domain Abbreviation (C66734) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Events", "definition": "Evidence of disease or condition, including objective signs and subjective symptoms that are typically observed by a physician or described to the investigator by the subject.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Findings", "definition": "Semi-Quantitative measurement of vertebral fractures CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Purpose", "definition": "See SDTM 3.3. Included as a value in the list of controlled terms, codelist Domain Abbreviation (C66734) at http://www.cdisc.org/standards/terminology/index.html CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 278", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Meal Data", "definition": "Interventions Information regarding the subject's meal consumption, such as fluid intake, amounts, form (solid or liquid state), frequency, etc., typically used for PK analysis. Included as a value in the list of controlled terms, codelist Domain Abbreviation (C66734) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "will not be used with", "definition": "SDTM standard domains, where - may be any valid letter or number. CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 280", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Autopsy", "definition": "TBD (To Be Determined) Additional data about deaths, specific to findings from autopsies.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Under Discussion", "definition": "Included as a value in the list of controlled terms, codelist Domain Abbreviation (C66734) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Under Development", "definition": "Included as a value in the list of controlled terms, codelist Domain Abbreviation (C66734) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 282", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Treatments", "definition": "Populated using a code value from the list of controlled terms, codelist No Yes Response (C66742) at http://www.cdisc.org/standards/terminology/index.html Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "DO NOT USE -", "definition": "Information on dictionaries and dictionary versions be", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "included in the SDTM", "definition": "metadata, since the define.xml specification has explicit mechanisms for handling references to dictionaries and dictionary versions. Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "at", "definition": "http://www.cdisc.org/standards/terminology/index.html CDISC SDTM Implementation Guide (Version 3.1.2) APPENDIX C4: DRUG ACCOUNTABILITY TEST CODES The following table contains the test codes suggested by CDISC for use in DRUG Accountability domains.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "of Subjects", "definition": "No controlled terminology. Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Age Group", "definition": "Populated using a code value from the list of controlled terms, codelist AGESPAN (C66780) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "A record for each", "definition": "applicable category should be included. Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "AGEMAX", "definition": "Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Name", "definition": "No controlled terminology. In the future, may be added to list of controlled terms on CDISC website.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Administration", "definition": "(ROUTE) Includes many more controlled terms than for those listed in CDISC Notes for – ROUTE. Includes more specific routes than INHALATION listed in CDISC notes for SUROUTE on page 50.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "to submit dosing", "definition": "parameters, as the TE and TA datasets are better suited to describing such information. Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Frequency", "definition": "Use controlled terminology for interventions (DOSFRQ). Dose frequency associated with a test product or comparative treatment. In the future, may be added to list of controlled terms on CDISC website", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Dose Units", "definition": "Use controlled terminology for interventions (DOSU). Units used with value(s) in DOSE. In the future, may be added to list of controlled terms on CDISC website", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Indication", "definition": "No controlled terminology. In the future, may be added to list of controlled terms on CDISC website", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Trial Length", "definition": "No controlled terminology. Defined as the planned", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "length of time for a", "definition": "subject's participation. It should be recorded using", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "the IS8601 format for", "definition": "durations, see Section 4.1.4.3. Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 284", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Objective", "definition": "No controlled terminology Should be described in", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "terms of the desired", "definition": "statement in labeling. In the future, may be added to list of controlled terms on CDISC website", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Subjects", "definition": "No controlled terminology. Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Randomized", "definition": "Populated using a code value from the list of controlled terms, codelist NY (C66742) at http://www.cdisc.org/standards/terminology/index.html Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Participants", "definition": "Populated using a code value from the list of controlled terms, codelist SEXPOP (C66732) at http://www.cdisc.org/standards/terminology/index.html Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Organization", "definition": "No controlled terminology. In the future, may be added to list of controlled terms on CDISC website", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Rules", "definition": "If the trial has study stop rules (STOPRULE is not equal to \"NONE\"), contains a description of the stop rules. Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Schema", "definition": "Populated using a code value from the list of controlled terms, codelist TPLIND (C66735) at http://www.cdisc.org/standards/terminology/index.html Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Control Type", "definition": "Populated using a code value from the list of controlled terms, codelist TCNTRL (C66785) at http://www.cdisc.org/standards/terminology/index.html Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Group", "definition": "Populated using a code value from the list of controlled terms, codelist TDIGRP (C66787) at http://www.cdisc.org/standards/terminology/index.html If trial does not enroll healthy subjects (TDIGRP is not equal to \"HEALTHY SUBJECTS\"), contains the diagnosis of subjects to be enrolled. Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Type", "definition": "Populated using a code value from the list of controlled terms, codelist TINDTP (C66736) at http://www.cdisc.org/standards/terminology/index.html", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "TINDTP provides a", "definition": "classification system for the indication provided as text in INDIC.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "the adverse effect of", "definition": "another treatment. Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Classification", "definition": "Populated using a code value from the list of controlled terms, codelist TPHASE (C66737) at http://www.cdisc.org/standards/terminology/index.html The controlled terminology for phase includes several formats as synonyms. Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "of Test Product", "definition": "No controlled terminology. In the future, may be added to list of controlled terms on CDISC website", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Trial Type", "definition": "Populated using a code value from the list of controlled terms, codelist TTYPE (C66739) at http://www.cdisc.org/standards/terminology/index.html Included as a value in the list of controlled terms, codelist Trial Summary Parameter Test Code (C66738)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Returned Amount", "definition": "APPENDIX C5: SUPPLEMENTAL QUALIFIERS NAME CODES The following table contains an initial set of standard name codes for use in the Supplemental Qualifiers (SUPP--) special-purpose datasets. There are no specific conventions for naming QNAM and some sponsors may choose to include the 2- character domain in the QNAM variable name. Note that the 2-character domain code is not required in QNAM since it is present in the variable RDOMAIN in the SUPP-- datasets.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Dictionary Version", "definition": "AE, MH, PE and other domains that use dictionary-", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "High Level Group Term", "definition": "AE, MH, PE, and any other domain coded to MedDRA --HLT", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "High Level Term", "definition": "AE, MH, PE, and any other domain coded to MedDRA --LLT", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Lower Level Term", "definition": "AE, MH, PE, and any other domain coded to MedDRA", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "ITT", "definition": "Intent to Treat Population Flag", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 286", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "RL", "definition": "CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 288", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "General", "definition": "Changed abbreviation from SDSIG to SDTMIG . Updated references to SDTM v1.1. Introduced abbreviation of V3.x to refer to V3.1 IG and subsequent versions.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Change", "definition": "Controlled Terminology - Type of Trial (TTYPE) 1) TSPARMCD was changed from TYPE on page 170 to TTYPE. 2) TSVAL CONFIRMATORY and EXPLORATORY were removed based on review comments. 3) TSVAL PHARMACODYNAMICS changed to PHARMACODYNAMIC 4) TSVAL PHARMACOGENOMICS changed to PHARMACOGENOMIC TSVAL PHARMACOKINETICS changed to PHARMACOKINETIC", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Add", "definition": "10.3.1 Added SI Infection Site Measurements to domain codes.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Document", "definition": "New Pharmacokinetics Parameters domain model and example", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Correct", "definition": "10.4 Corrected list of fragments (Domain codes are no longer included in this fragment list, but appear only in 10.3.1). E4: CHANGES FROM CDISC SDTMIG V3.1.1 TO V3.1.2 DRAFT Note: This is limited to Appendix 10 changes as of current printing.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Sections 5", "definition": "& 6 Changed the header for all domain models to use lowercase xpt filenames for consistency with eCTD specifications.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Section 6", "definition": "Changed domain models for CM, SU, AE, MH to address possible use of new SUPP-- variable --PRESP, reconsider use of --OCCUR, and emphasize importance of including data that represents actual interventions or events.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 290", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 292", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "move", "definition": "6.1.1.0 Added new assumption 5 to discuss record structure. Removed old assumption 5 (which was redundant with Notes and Section 4.1.4.7.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Replace", "definition": "9.5.1 Replaced Trial Design examples with new examples to better communicate core concepts; added new TS example.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Remove", "definition": "10.3.1 Removed reserved codes for DR, DY, NE, PF, RF, SZ, VX as these may no longer be necessary as separate domains. Changed code for Biopsy from BX to BR.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 294", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Delete", "definition": "10.3.2 Removed “FINDING” from list of controlled terms for EG.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Addition", "definition": "10.3.1 – Reserved Domain Codes – AD,", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "FH", "definition": "Added Analysis Dataset and Family History", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Domain Codes - AU", "definition": "Class for Autopsy changed to TBD ( To Be Determined). SDS team has not yet determined the class or possibly classes for this domain. Findings obtained during an autopsy would fall into another domain such as organ measurements) or lab tests such as analysis of stomach contents. Facts about the autopsy may need to be captured such as date of autopsy, autopsy start and end dates, whether it is a complete or partial autopsy etc.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Codes", "definition": "Controlled terminology will be published on the CDISC website. CDISC SDTM Implementation Guide (Version 3.1.2)", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 296", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Changes", "definition": "Removed Controlled Terminology - Units for Vital Signs Results (VSRESU) Controlled terminology is published on the CDISC website. Examples were in CDISC notes for VSORRESU on page 126.More units were added and some were modified: 1) INCHES changed to IN 2) FEET is not included 3) POUNDS changed to LB BEATS PER MINUTE changed to BEATS/MINUTE Removed Section 10.3.3 Vital", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Signs Test Codes", "definition": "(VSTESTCD, VSTEST) Controlled terminology is published on the CDISC website and some changes made from10.3.3 Vital Signs Test Codes Page 167 1) VSTEST “Frame Size” changed to “Body Frame Size” 2) VSTEST “Body Fat” changed to “Adipose Tissue”", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Study Treatment", "definition": "Examples were in CDISC notes for AEACN on page 54. DRUG INTERRUPTED was not included as an example.", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "for Non-Completion", "definition": "(NCOMPLT) Examples were in CDISC notes for DSDECOD disposition events on page 60. Added “RECOVERY”, Changed “WITHDRAWAL OF CONSENT” to “WITHDRAWAL BY SUBJECT”", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Minor Changes", "definition": "CDISC SDTM Implementation Guide (Version 3.1.2) APPENDIX F: REPRESENTATIONS AND WARRANTIES, LIMITATIONS OF LIABILITY, AND DISCLAIMERS CDISC Patent Disclaimers. It is possible that implementation of and compliance with this standard may require use of subject matter covered by patent rights. By publication of this standard, no position is taken with respect to the existence or validity of any claim or of any patent rights in connection therewith. CDISC, including the CDISC Board of Directors, shall not be responsible for identifying patent claims for which a license may be required in order to implement this standard or for conducting inquiries into the legal validity or scope of those patents or patent claims that are brought to its attention. Representations and Warranties. Each Participant in the development of this standard shall be deemed to represent, warrant, and covenant, at the time of a Contribution by such Participant (or by its Representative), that to the best of its knowledge and ability: (a) it holds or has the right to grant all relevant licenses to any of its Contributions in all jurisdictions or territories in which it holds relevant intellectual property rights; (b) there are no limits to the Participant’s ability to make the grants, acknowledgments, and agreements herein; and (c) the Contribution does not subject any Contribution, Draft Standard, Final Standard, or implementations thereof, in whole or in part, to licensing obligations with additional restrictions or requirements inconsistent with those set forth in this Policy, or that would require any such Contribution, Final Standard, or implementation, in whole or in part, to be either: (i) disclosed or distributed in source code form; (ii) licensed for the purpose of making derivative works (other than as set forth in Section 4.2 of the CDISC Intellectual Property Policy (“the Policy”)); or (iii) distributed at no charge, except as set forth in Sections 3, 5.1, and 4.2 of the Policy. If a Participant has knowledge that a Contribution made by any Participant or any other party may subject any Contribution, Draft Standard, Final Standard, or implementation, in whole or in part, to one or more of the licensing obligations listed in Section 9.3, such Participant shall give prompt notice of the same to the CDISC President who shall promptly notify all Participants. No Other Warranties/Disclaimers. ALL PARTICIPANTS ACKNOWLEDGE THAT, EXCEPT AS PROVIDED UNDER SECTION 9.3 OF THE CDISC INTELLECTUAL PROPERTY POLICY, ALL DRAFT STANDARDS AND FINAL STANDARDS, AND ALL CONTRIBUTIONS TO FINAL STANDARDS AND DRAFT STANDARDS, ARE PROVIDED “AS IS” WITH NO WARRANTIES WHATSOEVER, WHETHER EXPRESS, IMPLIED, STATUTORY, OR OTHERWISE, AND THE PARTICIPANTS, REPRESENTATIVES, THE CDISC PRESIDENT, THE CDISC BOARD OF DIRECTORS, AND CDISC EXPRESSLY DISCLAIM ANY WARRANTY OF MERCHANTABILITY, NONINFRINGEMENT, FITNESS FOR ANY PARTICULAR OR INTENDED PURPOSE, OR ANY OTHER WARRANTY OTHERWISE ARISING OUT OF ANY PROPOSAL, FINAL STANDARDS OR DRAFT STANDARDS, OR CONTRIBUTION. Limitation of Liability. IN NO EVENT WILL CDISC OR ANY OF ITS CONSTITUENT PARTS (INCLUDING, BUT NOT LIMITED TO, THE CDISC BOARD OF DIRECTORS, THE CDISC PRESIDENT, CDISC STAFF, AND CDISC MEMBERS) BE LIABLE TO ANY OTHER PERSON OR ENTITY FOR ANY LOSS OF PROFITS, LOSS OF USE, DIRECT, INDIRECT, INCIDENTAL, CONSEQUENTIAL, OR SPECIAL DAMAGES, WHETHER UNDER CONTRACT, TORT, WARRANTY, OR OTHERWISE, ARISING IN ANY WAY OUT OF THIS POLICY OR ANY RELATED AGREEMENT, WHETHER OR NOT SUCH PARTY HAD ADVANCE NOTICE OF THE POSSIBILITY OF SUCH DAMAGES. Note: The CDISC Intellectual Property Policy can be found at http://www.cdisc.org/about/bylaws_pdfs/CDISCIPPolicy-FINAL.pdf . CDISC, © 2007. All rights reserved", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "Page 298", "definition": "CDISC. © 2007. All rights reserved July 25, 2007", "sources": [ "SDTM.pdf" ], "file": "SDTM.pdf", "type": "pdf" }, { "term": "T h i r d E d i t i o n", "definition": "S u s a n n e P r o k s c h a", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "MANAGEMENT", "definition": "T h i r d E d i t i o n S u s a n n e P r o k s c h a CRC Press is an imprint of the Taylor & Francis Group, an informa business Boca Raton London New York", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "CRC Press", "definition": "Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2012 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Version Date: 2011916 International Standard Book Number-13: 978-1-4398-4831-9 (eBook - PDF) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information stor- age or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copy- right.com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that pro- vides licenses and registration for a variety of users. For organizations that have been granted a pho- tocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Preface......................................................................................................................xv", "definition": "Introduction............................................................................................................xvii Section I  Study Startup", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "The Data Management Plan..................................................................3", "definition": "History of Data Management Plans......................................................3 What Goes into a DMP?.......................................................................4 Signing Off on the DMP.......................................................................5 Revising the DMP.................................................................................5 DMPs and the Study Files....................................................................6 Using DMPs with CROs.......................................................................6 Quality Assurance and DMPs..............................................................7 SOPs for DMPs and Study Files...........................................................7 Using Data Management Plans.............................................................8", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "CRF Design Considerations..................................................................9", "definition": "Primary Goals of CRF Design.............................................................9 Collecting Required Data: Visits, Procedures, Fields....................10 Protocol Compliance......................................................................12 Collecting Analyzable Data...........................................................13 Secondary Goal: Reducing Queries.................................................... 14 Avoiding Duplicate Data................................................................ 14 Eliminating Missing or Ambiguous Responses.............................15 CRFs with Data Processing Impact.................................................... 16 Log Forms...................................................................................... 17 Questionnaires............................................................................... 18 Diagrams and Analog Scales.........................................................19 Early Termination Visits................................................................20 Revisions to the CRF..........................................................................20 Quality Assurance for CRFs...............................................................21 SOPs on CRF Design..........................................................................22 Reuse and Refine CRF Modules.........................................................22", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Database Design Considerations.........................................................23", "definition": "Making Design Decisions...................................................................23 Basic Clinical Database Concepts......................................................24 Field Data Types.............................................................................24", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Numeric Fields...............................................................................25", "definition": "Dates..........................................................................................26 Texts..........................................................................................28 Coded Data.....................................................................................28 Identifier Fields..............................................................................30 Calculated or Derived Values........................................................ 31 Tall-Skinny versus Short-Fat Tables...................................................32 Using Standards..................................................................................34 After Deciding on a Design................................................................35 Quality Assurance for Database Design.............................................35 SOPs for Database Design..................................................................35 Responsibilities in Database Design...................................................36", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Edit Checks.........................................................................................37", "definition": "Choosing Edit Checks.........................................................................37 Missing Values...............................................................................38 Simple Range Checks....................................................................38 Logical Inconsistencies..................................................................38 Cross-Form or Cross-Page Checks................................................39 Protocol Violations.............................................................................39 Specifying Edit Checks......................................................................40 Quality Assurance of Edit Checks......................................................40 SOPs for Edit Checks..........................................................................40 The Connection to Queries.................................................................42", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Preparing to Receive Data..................................................................43", "definition": "Overview of Creating Study Databases..............................................43 Validating Study Databases................................................................44 A Study Validation Plan.....................................................................45 Database Specifications......................................................................45 Paper Studies..................................................................................45 EDC Studies...................................................................................46 How Building Impacts Specifications............................................46 Testing Study Databases.....................................................................46 Testing Environment......................................................................47 Testing Paper Studies.....................................................................47 Testing EDC Studies......................................................................48 Final Steps in Testing.....................................................................48 Moving to Production.........................................................................48 Study Database Change Control.........................................................49 Quality Assurance for Building Studies............................................. 51 SOPs for Preparing for Data............................................................... 51 Study Creation Is Programming.........................................................52", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Receiving Data on Paper.....................................................................55", "definition": "Transcribing Data...............................................................................55 Double Entry..................................................................................55 OCR Plus Review...........................................................................56 Single Entry....................................................................................56 How Close a Match to the CRF?........................................................57 Dealing with Problem Data................................................................58 Illegible Fields................................................................................58 Notations in Margins.....................................................................58 Using Preentry Review..................................................................59 Changing Data after Entry..................................................................59 Quality Assurance and Quality Control for Entry..............................60 Audit Plan.......................................................................................60 Audit Process................................................................................. 61 Audit Report...................................................................................62 SOPs for Data Entry...........................................................................62 Entry Quality......................................................................................62", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Overseeing Data Collection................................................................65", "definition": "Monitoring EDC Data Collection.......................................................65 Monitoring Paper Data Collection......................................................65 Paper CRF Workflow.....................................................................66 Tracking Challenges..................................................................67 Repeating Pages........................................................................67 Pages with No Data...................................................................68 Duplicate Pages.........................................................................68 Studies without Page Numbers..................................................68 Missing Pages Reports...................................................................69 What Pages Do You Expect?.....................................................69 CROs and Tracking Pages..............................................................70 Principal Investigator Signatures........................................................71 Using Tracking for Quality Assurance and Quality Control..............71 SOPs for Overseeing Data Collection.................................................72 Tracking throughout the Process........................................................72", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Cleaning Data.....................................................................................73", "definition": "Identifying Discrepancies...................................................................73 Automatic Checks.......................................................................... 74 Manual Queries.............................................................................. 74 Clinical and Listing Review...................................................... 74", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Problems during Entry from Paper...........................................75", "definition": "Discrepancies Identified by External Programs........................75 The EDC Query Process.....................................................................75 Creating Manual Queries...............................................................76 Resolving an EDC Query...............................................................76 Getting PI Signatures.....................................................................76 The Paper Query Process....................................................................77 Resolving Discrepancies Internally...............................................77 Turning a Discrepancy into a Query..............................................79 Sending Queries to the Sites..........................................................80 Resolving Paper Queries................................................................80 Getting PI Signatures................................................................ 81 Applying the Resolution............................................................ 81 Tracking Queries.................................................................................82 Links to Quality Assurance and Quality Control...............................83 SOPs for Discrepancy Management...................................................84 Using Queries to Improve Efficiency..................................................84", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Managing Lab Data............................................................................87", "definition": "Storing Lab Data.................................................................................87 Advantages of the Tall-Skinny Format..........................................88 Disadvantages of the Tall-Skinny Format.....................................89 Identifying Lab Tests.....................................................................90 Storing Units....................................................................................... 91 Ranges and Normal Ranges................................................................ 91 Laboratory IDs...............................................................................92 Normal Range Storage...................................................................92 Using the Normal Ranges..............................................................93 Lab Result Trends...............................................................................93 Using Central Labs.............................................................................93 Using Specialty Labs..........................................................................94 Auditing the Lab............................................................................94 Monitoring the Data.......................................................................95 Quality Assurance around Lab Data..................................................95 SOPs for Processing Lab Data............................................................96 Why Lab Data Needs Special Attention.............................................96 Chapter 10\t Non-CRF Data....................................................................................97 Receiving Electronic Files from a Vendor..........................................97 Transferring Files...........................................................................97 Formatting the Data.......................................................................98 Loading Data..................................................................................98 Identifying File Contents...............................................................99 Cleaning Non-CRF Data..................................................................100", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "ix", "definition": "Quality Assurance for External Data............................................... 101 SOPs for Non-CRF Data................................................................... 101 When Non-CRF Data Is outside Data Management........................102 Chapter 11\t Collecting Adverse Event Data......................................................... 103 Collecting Adverse Events................................................................ 103 Adverse Event Forms...................................................................104 Special Considerations for Paper AE Forms...............................106 Storing and Cleaning AE Data.........................................................107 Coding Adverse Event Terms...........................................................108 Reconciling Serious Adverse Events................................................109 Methods for Reconciliation.......................................................... 110 Easing the Reconciliation Process............................................... 110 Quality Assurance and Quality Control........................................... 110 SOPs for AE Data............................................................................. 111 Impact of AEs on Data Management................................................ 111 Chapter 12\t Creating Reports and Transferring Data........................................... 113 Specifying the Contents.................................................................... 113 From Where?................................................................................ 113 Exactly What?.............................................................................. 114 When?........................................................................................... 114 Standard and Ad Hoc Reports.......................................................... 115 Data Transfers................................................................................... 116 Transfer Checklists...................................................................... 116 Transfer Metrics........................................................................... 117 Quality Control Review of Printed Reports and Presentations........ 118 SOPs for Reports and Transfers........................................................ 118 Putting in the Appropriate Effort...................................................... 118 Section III  Study Closeout Chapter 13\t Study Database Lock........................................................................123 Final Data..........................................................................................123 Final Queries.....................................................................................124 Final Quality Control........................................................................124 Database Audits...........................................................................124 Summary Review.........................................................................125 Reconciling..................................................................................126 Final Steps for EDC..........................................................................127 Using a Checklist to Lock a Study....................................................127 Setting Database Lock......................................................................129", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Time to Study Database Lock...........................................................129", "definition": "Quality Assurance around Lock.......................................................130 SOPs for Study Closeout...................................................................130 Reducing Time to Study Lock.......................................................... 131 Chapter 14\t After Database Lock......................................................................... 133 Complete Study Files........................................................................ 133 Assess Study Conduct....................................................................... 133 Site eCRF Copies..............................................................................134 Unlocking..........................................................................................134 Avoiding Unlocks.........................................................................134 Approval for Unlocking............................................................... 135 Unlocking for Paper Studies........................................................ 135 Unlocking for EDC Studies......................................................... 135 Quality Assurance............................................................................136 SOPs for Study Database Unlock.....................................................136 Avoid Unlocks...................................................................................136 Section IV  Necessary Infrastructure Chapter 15\t Standard Operating Procedures (SOPs)............................................ 139 What Is an SOP?............................................................................... 139 SOPs for Data Management.............................................................. 140 Creating Standard Procedures.......................................................... 141 Starting from Scratch................................................................... 141 Procedures for New CDM Systems............................................. 143 Complying with Standard Procedures.............................................. 143 Training on SOPs.........................................................................144 Designing for Compliance...........................................................144 Proving Compliance..................................................................... 145 How Data Management SOPs Are Different from Clinical SOPs.................................................................................... 146 SOPs on SOPs................................................................................... 146 SOP Work Never Ends...................................................................... 147 Chapter 16\t Training............................................................................................. 149 Who Gets Trained on What?............................................................ 149 Study-Specific Training....................................................................150 How to Train..................................................................................... 152 Training Records.............................................................................. 153 SOPs on Training..............................................................................154 Allotting Time for Training..............................................................154", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "xi", "definition": "Chapter 17\t Controlling Access and Security...................................................... 155 Account Management....................................................................... 155 Usernames....................................................................................156 Passwords.....................................................................................156 Account Timeouts........................................................................ 157 Access Control.................................................................................. 157 How to Grant Access................................................................... 158 Who Had Access?........................................................................ 158 SOPs and Guidelines for Accounts................................................... 159 Taking Security Seriously................................................................. 159 Chapter 18\t Working with CROs.......................................................................... 161 The CRO Myth................................................................................. 161 Auditing CROs.................................................................................. 162 Defining Responsibilities.................................................................. 163 Oversight and Interaction.................................................................. 163 Study Startup................................................................................ 163 Study Conduct..............................................................................164 Closing the Study......................................................................... 166 EDC Vendors as CROs..................................................................... 166 CROs as Functional Service Providers............................................. 167 SOPs for Working with CROs.......................................................... 167 Benefiting from CROs...................................................................... 167 Section V  CDM Systems Chapter 19\t Clinical Data Management Systems................................................. 171 CDM System Characteristics............................................................ 171 Where CDM Systems Come From................................................... 172 Choosing a CDM System.................................................................. 172 Using CDM Systems Successfully.................................................... 173 SOPs for CDM Systems.................................................................... 173 CDM Systems Are for More than Data Entry.................................. 174 Chapter 20\t EDC Systems.................................................................................... 175 What Makes EDC Systems Different?............................................. 175 Multiple Data Streams................................................................. 176 Coding.......................................................................................... 176 Where the Servers Are—Hosting................................................ 176 Study Setup.................................................................................. 177 The Need for Data Repositories................................................... 177", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Working with EDC Systems............................................................. 178", "definition": "Main Advantages of EDC................................................................. 179 Some Problems with EDC................................................................ 179 Will Data Management Groups Disappear?..................................... 180 SOPs for EDC................................................................................... 181 Making EDC Successful................................................................... 181 Chapter 21\t Choosing Vendor Products............................................................... 183 Defining Business Needs.................................................................. 183 Initial Data Gathering....................................................................... 184 Requests for Information.................................................................. 184 Evaluating Responses....................................................................... 185 Extended Demos and Pilots.............................................................. 185 Hands-On Demos......................................................................... 186 Pilots............................................................................................. 186 Additional Considerations................................................................ 187 What Is Missing?.............................................................................. 189 Preparing for Implementation........................................................... 189 Chapter 22\t Implementing New Systems............................................................. 191 Overview and Related Plans............................................................. 191 Essential Preparation........................................................................192 Integration and Extensions................................................................ 193 Migration of Legacy Data.................................................................194 Benefiting from Pilots.......................................................................194 Validation..........................................................................................196 Preparation for Production................................................................196 Successful Implementation...............................................................196 Chapter 23\t System Validation.............................................................................199 What Is Validation?...........................................................................199 Validation Plans or Protocols............................................................200 Introduction and Scope................................................................200 Assumptions and Risks................................................................201 Business Requirements and Functional Specification.................201 Installation...................................................................................201 Testing Overview.........................................................................202 Vendor Audit................................................................................202 Security Plan................................................................................203 SOPs and Guidelines....................................................................203 Completion Criteria......................................................................203 Maintaining Validation Plans......................................................203 Change Control and Revalidation.....................................................204", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "xiii", "definition": "What Systems to Validate.................................................................204 SOPs for Validation..........................................................................205 Requirements and Benefits...............................................................206 Chapter 24\t Test Procedures.................................................................................207 Traceability Matrix...........................................................................207 Test Script Contents..........................................................................208 Purchasing Test Scripts.....................................................................209 Training for Testers........................................................................... 210 Reviewing Results............................................................................. 210 Test Outcome.................................................................................... 211 Retaining the Test Materials............................................................. 212 Chapter 25\t Change Control................................................................................. 213 What Changes Should Be Controlled?............................................. 213 Changes to Software Systems...................................................... 213 Changes to Study Databases........................................................ 214 Documenting the Change................................................................. 214 Describe or Propose the Change.................................................. 215 Assess the Impact......................................................................... 215 Plan Testing.................................................................................. 216 Document the Outcome............................................................... 216 Releasing Changes............................................................................ 217 Problem Logs.................................................................................... 217 Considering Version Control............................................................ 218 The Value of Change Control........................................................... 218 Chapter 26\t Coding Dictionaries and Systems..................................................... 219 Common Coding Dictionaries.......................................................... 219 MedDRA......................................................................................220 WHO Drug...................................................................................220 Using Autocoders..............................................................................220 Collecting the Term...................................................................... 221 Storing the Results.......................................................................222 Failure to Code.............................................................................222 Special Considerations for AE Terms...............................................224 Dictionary Maintenance...................................................................225 Quality Assurance and Quality Control for Coding.........................226 SOPs for Coding and Dictionaries....................................................226 Effective Coding...............................................................................227 Chapter 27\t Migrating and Archiving Data..........................................................229 Simple Migrations within Systems...................................................229", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Why Migrate between Systems?.......................................................230", "definition": "Complex Migrations......................................................................... 231 Migration by Hand.......................................................................232 Migrating Audit Trails.................................................................232 Archiving Data..................................................................................232 Level of Archive Access..............................................................233 What to Archive...........................................................................233 Migration and Archive Plans............................................................234 Future Directions..............................................................................234 Appendix A: Data Management Plan Outline...................................................235 Appendix B: Clinical Data Management SOPs.................................................239 Appendix C: CRO-Sponsor Responsibility Matrix..........................................243 Appendix D: Implementation Plan Outline.......................................................247 Appendix E: Validation Plan Outline.................................................................249 Appendix F: CDISC and HIPAA........................................................................ 251 Bibliography..........................................................................................................253", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Preface", "definition": "conduct and, in all cases, have tried to keep the information practical rather than academic in the hopes that it can be applied by every reader to every CDM group.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Introduction", "definition": "example. (See Chapter 9, “Managing Lab Data,” and Chapter 10, “Non- CRF Data.”) 11.\tOnce the data is in a computer database (through entry into eCRFs or through transcription from paper CRFs), that data goes through vari- ous levels of checks and rechecks until it is considered “clean” enough to support analysis. (See Chapter 4, “Edit Checks,” and Chapter 8, “Cleaning Data.”) 12.\tWhen all the data from all of the patients has been collected and cleaned, the data goes through a final process that checks the completeness and qual- ity of the data. The dataset is then “locked” against changes. (See Chapter 13, “Study Database Lock.”) 13.\tWhile statistical programs may have been run over data prior to database lock to test the programs and review trends in data, it is not until after the study is locked that the final analysis can be done (with unblinded treatment information for blinded studies). 14.\tConclusions can now be reached and the final study report written. Refer also to the diagram in Figure I.1. The Importance of Clinical Data Management The references to particular chapters that appear in the preceding clinical trial flow give an indication of what role clinical data management plays in a clinical trial.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Testing in Humans", "definition": "Drug development begins in the laboratory or on a computer when a company identi- fies candidate compounds to address a particular disease cause, progression mecha- nism, or symptom. If a candidate looks promising, it is moved to preclinical testing; this will involve experiments in test tubes and in animals. The company will also begin to explore manufacturing techniques because a candidate that is not stable or cannot be produced in necessary amounts cannot move forward. At some point, the developer identifies one candidate that can be moved into human trials. An often- cited figure is that for every 10,000 possible candidates, only 5 would be considered safe and practical to consider testing in humans. When a company has such a candidate for testing in the United States, it files an Investigational New Drug (IND) Application with the Food and Drug Administration (FDA). If the FDA approves, testing in humans can proceed. At the point that the drug or treatment is introduced into humans, the experiments are called clinical trials. While there are many exceptions for serious conditions, testing typically begins in healthy volunteers and then, if the drug or treatment appears safe enough and (pos- sibly) effective, testing moves into the target population. Human testing is divided roughly into three phases that are used by both the industry and the FDA: •\t Phase I is the first in-human testing. Phase I trials are most commonly con- ducted in healthy volunteers. These are small, short studies that focus on safety and begin to identify appropriate dosing. The studies will also investi- gate further how the drugs act in a human metabolism (pharmacokinetics). •\t Phase II involves larger studies of somewhat longer duration in the target population. The participants are carefully chosen and the scheduling of examinations and dosing are carefully controlled. These are sometimes called proof-of-concept trials. The main goals are to show effectiveness of the treatment, gather further safety information, and determine an appro- priate dose.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "xix", "definition": "Just as we sometimes fill in paper forms and sometimes fill in forms online, so it is for clinical trials. Some trials are based on paper forms; others are based on elec- tronic CRFs (eCRFs) and are known as electronic data capture (EDC) trials. (See Chapter 2, “CRF Design Considerations.”)", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Getting to a Result", "definition": "The steps involved in getting from an experimental plan (the protocol) to a result that supports or opposes the hypothesis are very similar for each trial that is run: 1.\tFor each clinical trial there is a protocol and a statistical analysis plan, in addition to other regulatory documents, which are completed before the actual experiment begins. 2.\tWhen the protocol is final, a team uses it to create a CRF or eCRF. (See Chapter 2.) 3.\tThe CRF/eCRF is used to create a database to store and manage the data. (See Chapter 3, “Database Design Considerations,” and Chapter 5, “Preparing to Receive Data.”) 4.\tIn parallel, or after the protocol is final, clinical sites are recruited and they, in turn, obtain approval to participate in the trial and begin to identify and recruit patients to participate in the study as subjects of the experiment. 5.\tSubjects who are enrolled in the study begin the various visits and proce- dures identified in the protocol. Except in some Phase I studies, the enroll- ment and recruitment are ongoing until the target enrollment numbers are reached. Subjects do not all visit at the same time. 6.\tAt each visit, most of the procedures and results are first documented by site staff on source documentation such as the subject’s medical record—not on the CRF! This is true for both paper and EDC trials. 7.\tSite personnel transcribe each subject’s data to the CRF or eCRF after the visit. (See Chapter 6, “Receiving Data on Paper,” and Chapter 7, “Overseeing Data Collection.”) 8.\tSites are monitored according to good clinical practice (GCP) require- ments to protect the subjects and ensure the trial is being conducted in compliance with the protocol. The sites are also monitored to ensure accurate completion of the CRF or eCRFs. The monitor representing the sponsoring company visits the sites and compares some or all of the source information for all subjects against what was entered into the CRF or eCRF. This is called source document verification or sometimes source data verification and is an important step that has implications for the interpretation of the data. (See Chapter 12, “Creating Reports and Transferring Data.”) 9.\tFor paper studies, the paper CRFs are sent to a data entry center represent- ing the sponsor where all of the data is transcribed into a database. (See Chapter 6, “Receiving Data on Paper.”) 10.\tFor most trials, some data will be received through electronic files from external sources and not on the CRF. Central lab data is the most common", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "CRF or eCRF designed", "definition": "Study database built and released Data recorded on source documents Data transcribed to CRF or eCRF Source document verification/monitoring Data entry (paper studies only) Receipt of electronic non-CRF data", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Data cleaning", "definition": "Complete and accurate data Extraction for analysis and study report FIGURE I.1  Steps in a clinical trial.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "xxi", "definition": "Clinical data management is the work performed on data from a clinical trial from the preparation to collect that data through the time it is extracted for final analy- sis. (Data managers, however, do not analyze the data.) It will become clear in the chapters that CDM tasks are technical tasks linked closely to computer systems and software applications. Data managers focus on that data including the individual values and the relation- ship of those values to each other. Data management is responsible for delivering complete datasets that are of a quality (accurate, clean) to reliably support a conclu- sion. The importance of clinical data management hinges on the fact that if the data is not accurate, reliable, and analyzable, all the money invested in conducting the study has gone to waste.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Study Startup", "definition": "While the sponsor will occasionally provide a CRF design to the CRO, it is more common for the CRO to develop the CRF or electronic CRF (eCRF). The sponsor must plan to be involved in the development and review and must approve the final version. The final sign-off for the CRF should come from both sides: the sponsor’s team (or clinical data management [CDM] liaison at a minimum) to indicate the CRF meets its requirements and the CRO side to indicate that the CRF design can be successfully translated to a database in their system. Every CRO study should have a data management plan (DMP). This can come from the sponsor, but is more likely to be written by the CRO’s data management group using their own template. As discussed in Chapter 1, the DMP should cover all the data management tasks for a study. In the case of a CRO–sponsor relationship, the DMP should make clear whose procedures are to be followed for a particular task and to clearly identify points at which data or responsibility passes from one side to the other. The sponsor must review and approve the data management plan of a CRO and must be current on all the updates that take place during the course of the 164 Practical Guide to Clinical Data Management, Third Edition study. If the CRO’s DMP does not cover all the activities in which the sponsor has an interest, the sponsor should request that the information be added or that a separate document be prepared. Some procedures that have a direct impact on the data may appear in the data management plan or as separate documents. The sponsor liaison should review and approve documents and procedures related to the following: •\t Data entry conventions (paper studies) •\t Data management self-evident corrections (paper studies) •\t Study-specific discrepancy-handling instructions •\t Receiving and managing electronic data •\t Coding conventions including study-specific coding queries •\t SAE reconciliation workflow •\t Data quality plans (including all edit checks, manual reviews, and sum- mary reviews) The review of these documents and procedures, sending the feedback to the CRO, and then checking that comments have been incorporated appropriately is a time- intensive job. Sponsors should be sure to allot an appropriate amount of time during the study start-up phase to do all this. It is the sponsor’s responsibility to provide feedback and approval in a timely manner. For paper studies, data management groups (both on the sponsor and on the CRO side) vary as to whether they expect an annotated CRF or EDC eCRF database specifi- cations from the CRO to be reviewed by the sponsor. At first, this seems inappropriate; after all, the CRO should know what they are doing and they are using their own data management system. But as we saw in Chapter 3, “Database Design Considerations,” a database designed from a CRF can be implemented in more than one way. For EDC studies, sponsors should expect to be involved in a review of the eCRF in an online version at a minimum, and they may possibly be involved in extensive user acceptance testing of the EDC application. It is critical that these expectations around testing be clear at the start of the study and reflected in the statement of work or responsibil- ity matrix as testing can involve a significant investment of staff and time. However, both of these sorts of reviews—annotated CRF or online eCRF review—are worth the effort. Actual experience has shown sponsors that they can turn up misunderstandings about the data to be collected before it is too late and too far into the study. Time put in up front can prevent serious problems at transfer time or at lock. That brings up the question of transfers. The CRO and sponsor together must define not only how the data is to be formatted for transfer between them (that is, what the datasets or tables should look like) but also how often that data is to be transferred. This information goes into the DMP or into a separate transfer specification. As we will see next, a single transfer at the end of a study is rarely a good idea.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Management Plan", "definition": "Data management plans (DMPs) are created by clinical data management (CDM) to document how data management for a given study was carried out. Data man- agement plans are not required by any law or regulation but are so common across biopharmaceutical companies that they are considered an auditable document. After looking at standard operating procedures (SOPs) and training records, an auditor investigating clinical data management practices will typically ask for the data man- agement plan for a study being reviewed. This chapter discusses what is typically found in a DMP and how to use these documents efficiently. HISTORY OF DATA MANAGEMENT PLANS The history of how DMPs came into existence helps to illuminate how they are used today and explains their structure. From the time that data management groups were first formed, data managers set up studies, collected or entered data, cleaned those data, and processed the data until the study could be considered ready for analysis. For the most part, these groups did a good job and produced datasets with accurate data that reflected values provided by the investigator sites. Over time, the idea of “if you didn’t document it, it wasn’t done” became the rule, and groups made an effort to produce documents at key points along the way during conduct of a trial to record what was done by CDM and to provide evidence of good practice. These documents were (and still are) filed together in what is referred to simply as the data manage- ment study file. To ensure that study files across groups were consistent, companies eventually wrote standard operating procedures (SOPs) that outlined what the con- tents of each study file should be. However, even with good study files, some data management groups found they could not always find an answer quickly when an auditor asked a specific question about the conduct of a past study. In the early 1990s, some companies began to address this problem by creating a document whose purpose was to record all the most important information on how data management was carried out for a given study. They quickly found that creating this kind of document at the start of a study provided added value beyond its function as a reference for auditors, by forcing study planning before the work was carried out. These new documents were also more accurate when written at the start of a study rather than as a summary or report at the end of the study. These documents summarizing data management activities came to be called data management plans or data handling plans. By the mid to late 1990s, DMPs were in common use and data managers could attend seminars or courses on how to write 4 Practical Guide to Clinical Data Management, Third Edition and maintain them. In the early 2000s, DMPs came to be considered an auditable document. Besides serving as a tool to provide information during an audit, experi- ence has shown that DMPs have an even higher value to data management groups in that they aid in the transition of studies between data managers and in providing a clear history for long-term studies that go through a complicated life cycle. Today, DMPs are considered an industry standard. At the beginning of a study, the DMP provides a focus for identifying the data management work to be per- formed, who will perform that work, and what is to be produced as documentation of the work. During the study, the DMP is updated as key elements of the data manage- ment process change so that at the end of the study, the DMP provides an accurate record of how the study was carried out. WHAT GOES INTO A DMP? A DMP should touch on all the elements of the data management process for the study in question. The key activities for data management are found in Chapters 2 through 14 of this book and are summarized in Figure 1.1. For each of those elements or related groups of elements in the data management process, a DMP specifies: •\t What is the work to be performed? •\t Who is responsible for the work? •\t Which SOPs or guidelines will apply? •\t What documentation or output will be collected or produced? By including the final point, the documentation or output produced, in each sec- tion of the DMP, the document then also becomes an approximate table of contents Topics to Cover in a Data Management Plan CRF/eCRF Creation Database Design and Build Edit Check Specification Study Database Testing and Release Data or Paper Workflow", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Study Database Lock", "definition": "131 or the topic can be covered in its own SOP. A controlled unlock process is probably even more important than a controlled lock process. REDUCING TIME TO STUDY LOCK The best way to reduce the time needed to lock a study database is to avoid leaving tasks until the end. This not only helps ensure that the study is locked soon after the last query resolution is received, but it also improves the quality of the data by detecting problems soon after they have been introduced. Companies should con- sider the following approaches to data management tasks that can shorten time to study close. For paper studies: •\t Enter data as soon as possible after it is received. Data on paper does not move the process along. •\t Run cleaning procedures throughout the study as data is collected so that the queries go out early. Toward the end of the study, the outstanding que- ries should be only those pertaining to recently received data. •\t Identify missing CRF pages by knowing what is expected. Use tracking systems. •\t Begin to audit data against the CRF (for paper-based trials) early in the study to detect systematic problems. Continue to audit as the study proceeds to monitor quality. For EDC studies: •\t Keep on top of SDV status and principal investigator (PI) signatures •\t Track the query status throughout the trial even if it seems less important because the site has the queries. For all studies: •\t Track all non-CRF data received electronically to identify early missing samples or results. •\t Code adverse events and medications frequently. Issue coding discrepan- cies promptly. •\t For studies expecting large numbers of SAEs, reconcile periodically throughout the study. Because AE and SAE data often comes in late in the study (because AEs may be ongoing, for example), get listings from the safety system early so you know what to expect. For tasks that cannot be carried out earlier in the study, the best that can be done is to understand the amount of effort involved. At this critical point in a study, when other groups are depending on the outcome and delivery of data, data management must provide as good an estimate as possible for the amount of time required to carry out the tasks properly. 133 14 After Database Lock In the previous chapter we reviewed the activities required to lock a database. The study closeout activities do not end with lock; additional tasks must be performed to shut down a study. These tasks include archiving files, and for electronic data capture (EDC) systems, providing copies of the electronic case report forms (eCRFs) to the sites. In this chapter we also discuss what to do if a problem is detected that requires the database to be reopened for updates to the data.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "REVISING THE DMP", "definition": "It is very likely that during the course of an average Phase II or Phase III study, some critical data management process or a key computer application will change. Even though the DMP is a plan, that is, it is the way you expect to conduct the study, it must be revised whenever there is a significant change. The DMP must document how you expect to conduct the study from that point forward. Keeping the DMP current is harder to manage than one might expect. Companies constantly wrestle with finding the best or most practical way to record updates to 6 Practical Guide to Clinical Data Management, Third Edition the data management process. Sometimes the documentation produced by a task is sufficient. For example, additional edit checks can be added to the edit check specification (see more in Chapter 4), and if the DMP simply refers to that document, then no DMP update is required. Similarly, if a company has good change control documentation, it probably is not necessary to update the DMP when the study data- base is modified. If, however, that change control documentation is not sufficient (or nonexistent), the DMP could be updated as the place to record information regarding changes to the study database. In whatever way it is accomplished, after study lock, the DMP together with doc- umentation found in the study files should reflect all important changes to the data management process and computer systems that took place during the study. DMPs AND THE STUDY FILES The SOPs for data management activities and often the DMP specify what output documents are to be created during the course of the study. These are filed in what is known as the study file or data management study file. (This is not the same thing as the trial master file that is managed by clinical operations and contains key study documentation required by good clinical practice [GCP].) The study file may be a folder in a cabinet, a binder in a data manager’s office, or an electronic folder on a shared drive. The DMP and the documents found in the study file must be kept in synchro- nization. If the DMP states that the final database design will be filed in the study file, there must be a folder or tab in the study file for that document. Documents should be added to the study file as required by the DMP whenever they are ready. This makes the study “audit ready” at any time. Compiling the study file at or near study lock is almost a guarantee of finding entire documents or versions of docu- ments missing.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "USING DMPs WITH CROs", "definition": "When a sponsor uses a contract research organization (CRO) to conduct all or part of the data management activities for a study, either the sponsor’s or the CRO’s DMP can be used. Most companies will use the CRO’s DMP. In fact, many CROs have more comprehensive data management plans with well-defined procedures for its creation and maintenance than many sponsors do because CROs are audited frequently by their clients who will undoubtedly review DMPs and the associated study files. An experienced data manager from the sponsor company should expect to review in detail, and sign off on, a CRO’s DMP. The CRO should explain to the sponsor the process for revising the DMP during the course of the study. It is the sponsor’s responsibility to allocate resources to get the initial DMP and all revisions reviewed and signed in a reasonable period of time. Sponsors must consider how to create study files for CRO studies. Some documents normally produced during a study conducted by the sponsor in-house may be produced by the CRO as well but are then kept internally by the CRO rather than transferred to the sponsor. (See Chapter 18, “Working with CROs,” for additional discussion.) The Data Management Plan 7 QUALITY ASSURANCE AND DMPs Quality assurance (QA) is the prevention, detection, and correction of errors or problems. In biopharmaceutical firms, QA is closely tied to regulatory compliance because good practice must be closely tied to following regulations. Regulatory com- pliance and quality assurance are critical even in emerging companies too small to have a separate QA group. A key requirement of most quality methods is the creation of a plan; a key requirement of GCP is the documentation of what has happened dur- ing a study. The DMP helps fulfill both of these requirements by creating the plan and detailing what documents will record the conduct of the study. The DMP and the output documents it specifies can be used as the starting point when conducting internal QA audits of the data management process. As noted above, the DMP is also used by external auditors. SOPs FOR DMPs AND STUDY FILES Every data management group should have a process for documenting how a study was conducted. The process must be described formally in either an SOP or a depart- ment guideline. For most companies, the way to document a study will be to create a DMP-type document as described. A few companies, typically small ones with little variation across studies, may choose not to have a DMP but to have instead a detailed document on study files (similar to the technique used widely in the past). When using a DMP, the associated SOP must clearly define a point at which the DMP for a given study must be in place. For the DMP to be a plan, rather than a report at the end of the study, and to provide the value of thinking through a study before data comes in, a draft or an initial version typically needs to be in place before any substantial work is performed on data for the study. It is not unusual to use a point of first patient in or first data in as the trigger around which the DMP must be final. The SOP must also state the circumstances under which the DMP must be revised and what signatures are required. Along with an SOP for creating and maintaining a DMP, there should be a blank template document or an outline for the plan to assure consistency across studies. Each section in the template should have instructions on what kind of information and what level of detail is expected. An example of a completed DMP used in train- ing of new CDM staff is especially helpful. Because the DMP and study files are so closely linked, it is a risk to have two separate SOPs for these topics. Many companies have made the mistake of having the DMP requirements specified in one SOP (with a template) and a separate SOP describing contents and maintenance of the study files. Because the DMP is in con- stant use, the template is updated to change contents of the study files, but the SOP on study files is not. An auditor looking at the SOP on study files will find one list of contents or folders and a different list in the DMP template—and some unknowable final result in the actual study file. New data managers will also be confused because they will have a document required in the DMP and no obvious place to file it in the study files. Once again, this shows us that DMPs have two audiences: auditors and internal staff, and it must work for both. 8 Practical Guide to Clinical Data Management, Third Edition USING DATA MANAGEMENT PLANS To overcome the natural, strong reluctance to spend time planning or document- ing anything when there is “real” work to be done, the value of the effort must be recognized. To get more than minimal compliance from staff, that value has to be more than “because so-and-so tells us we have to” or “the FDA requires it.” A DMP actually does have benefits that can be recognized by every data manager. These benefits include: •\t The work to be done and responsibilities are clearly stated at the start of the study so that everyone knows what is expected. •\t The expected documents are listed at the start of the study so they can be produced during the course of, rather than after, the conduct of the study. •\t The document helps everyone fulfill regulatory requirements. •\t Data management tasks become more visible to other groups when the DMP is made available to the project team. •\t The DMP provides continuity of process and a history of a project. This is par- ticularly useful for long-term studies and growing data management groups. Forcing the planning work to take place at the beginning of the study may be hard, but it will save time at the close of the study when the time pressure likely will be even stronger. To avoid overwhelming staff with documentation requirements, managers of data management groups should encourage the use of templates and the use of previous plans as examples. The first few plans will require some work; after that, the burden should be considerably reduced as each new plan builds on the experience of the previous ones. The key is to keeping DMP requirements both focused and practical. 9 2 CRF Design", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Considerations", "definition": "Data from a clinical trial will be collected and stored in a computer system. The ini- tial data capture method may be paper, a computer entry screen at the investigator’s site, a laboratory instrument, central lab system, or perhaps a handheld computer used to collect diary information from the subject. It is typically the clinical data manager who will specify (design) the structures to capture that data, though it may not always be the data manager who actually builds or programs the system. All data managers should understand the kinds of fields and organization of fields that affect storage, analysis, and processing of the trial data, and they should be aware of some of the options to weigh in making a design decision for those fields. This chapter focuses on the design considerations that go into translating a case report form (CRF) or electronic data capture (EDC) requirement into a database for storage, cleaning, and later analysis of the data. The database concepts that form the basis of this chapter may be foreign and confusing to data managers who have not yet been introduced to them. This chapter aims to give an introduction to the main concepts common to clinical trial databases that will benefit all data managers with- out going into too much technical detail. Chapter 5, “Preparing to Receive Data,” discusses the steps involved in putting design decisions into action—setup, building, and release of a study. Chapters 19 and 20 provide more information on the clinical database systems that are used to support paper-based and EDC studies. MAKING DESIGN DECISIONS No matter what the underlying database or software application, the main design goal, or rather the requirement for all databases holding information from clinical trials, is to store the data accurately and in such a way that it can be analyzed. To come up with a practical database, a good database design balances various needs, preferences, and limitations, such as: •\t Clarity, ease, and speed of data entry •\t Efficient creation of analysis data sets for biostatisticians •\t Formats of data transfer files •\t Database design theory •\t Database application software requirements and limitations Balance is really the key word, and weight is typically given to designs that make frequent tasks or those requiring highly trained (expensive) staff the easiest. 24 Practical Guide to Clinical Data Management, Third Edition Often, there are several different ways of approaching a database structure design for a given study, all of which are perfectly valid and technically possible. One bio- pharmaceutical firm may choose structures that speed entry; another may choose structures that require less transformation to create analysis datasets. One company may choose to load data from electronic files in the format in which they are deliv- ered to avoid complex remapping; another may reformat that data to be consistent with best practices for database design. In addition to questions of balancing com- pany needs and preferences, all designs have to allow for the constraints imposed by the software applications that actually manage the clinical database. For paper-based studies, the design of the database follows the finalization of the CRF. In most (but not all) traditional database management systems used by clini- cal data management (CDM) groups, the definition of a data collection device like a CRF page influences, but does not completely determine, the design of the database. For EDC studies, the database is often defined by default when the entry forms are designed. That can make design of an EDC system more, not less, complex as the data manager must be aware of the CRF design considerations we saw in Chapter 2, and at the same time must take into account database design requirements and limi- tations. In some EDC systems the data manager defines not only the database storage characteristics but also the ways the collected data will be presented for review or transferred for analysis (clinical views). BASIC CLINICAL DATABASE CONCEPTS Some basic concepts apply particularly to clinical database design and pertain to all systems whether EDC or those that support data from paper trials. We will look at each of these in turn: •\t Field data types •\t Coded fields •\t Identifier fields •\t Groups/panels", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "reason for leaving", "definition": "In designing the CRF, we must always include these implied procedures as well as those found explicitly in the protocol. Once the list of procedures is in place, we have to list all the fields or questions for each procedure. To get those, we go back to the text of the protocol. Where CRF Design Considerations 11 the matrix may say “vital signs,” the procedures section of the protocol should say exactly which vital signs are to be collected. Ideally, the protocol will clearly say whether, for example, blood pressure and pulse rate are required when vital signs are collected or blood pressure, pulse rate, and respiration. CRF designers have to be aware that while vital signs may be collected at each visit, it is still possible that exactly which vital signs are collected may vary from visit to visit and, in fact, vital signs may be collected more than once in a single visit. The clinical teams who write protocols do not always provide the level of speci- ficity that data management requires, in which case the data manager must go back to the study team to completely identify the appropriate fields. Consulting with the clinical team is particularly common for specialized procedures whose results may be obvious to a clinical team experienced in the indication being treated but not necessarily to the data manager, as, for example, in our example of “assess signs/ symptoms of infection.” In addition to getting a complete list of fields associated with a procedure, the data manager will need to know how the result is reported (text, integer number, decimal number, etc.) and what the typical units are. Later in the study startup process (see Chapter 4), the data manager will again come back to these fields and ask the study team to identify what normal or expected ranges apply to the reported results and", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "FIGURE 2.1  This table is a subset of a protocol visit/procedure matrix showing only Days", "definition": "3 and 4 from a fictitious study of a treatment for skin infections. In this example, a physical exam is not performed on either Day 3 or Day 4, but presumably would be done at the start and possibly the end of the study. Several other procedures such as Blood Culture appear on both days. Several, such as ECG, appear only on Day 4, not on Day 3 (and of course they could also be performed on days outside of our subset). 12 Practical Guide to Clinical Data Management, Third Edition what kinds of logical consistency with other fields are assumed (e.g., systolic blood pressure is greater than diastolic blood pressure). This process of determining the structure of the CRF from the protocol should ensure that all the data required by the protocol is actually being collected in the CRF at all the required time points in order to satisfy one of the primary goals of CRF design. It is worth noting here that clinical teams should be discouraged from collecting data “just in case it proves interesting.” Every data point collected has a cost associated with it—the database field must be programmed, cleaning rules written, source documents verified, discrepancies followed up, and analysis pro- grams developed. The cost matters for companies both large and small. If it might be interesting, it should be included in the protocol and fully supported in collection and data cleaning. That being said, a few fields on the CRF collect data that is not analyzed; some fields are used to check compliance with the protocol and some are used to assist in cleaning the data. These kinds of fields are discussed more in the following sections.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Protocol Compliance", "definition": "A simple way to support compliance with the protocol is to include appropriate, short instructions and text along with the fields for critical procedures. For example, if a particular medication is strictly prohibited during the course of treatment, the page that collects information about concomitant medications may include, “If xyz is given during the course of the study, contact the medical monitor immediately.” In another example, in a section measuring a primary infection site, instructions may say, “If the wound measures less than n centimeters, check the ‘healed’ box.” Finally, to remind the sites to record certain events as adverse events, an instruction in an infusion sec- tion may say, “If the infusion is stopped due to an AE [adverse event], enter AE as the reason for stopping and also record the information on the AE page.” A more subtle way to support sites in complying with the protocol is to ensure that the order of the procedures, and the fields within a procedure on the CRF, match the requirements in the protocol. If the protocol says that a blood draw must be performed before dosing, fields associated with the blood draw (such as “Was blood drawn yes/no?”) should appear before fields associated with dosing). Those fields may also be clearly labeled as in “pre-dose blood draw.” In addition, the data man- ager should bring up in discussion with the clinical team the question of whether or not it is worth collecting the actual times of both the blood draw and the dosing. This would allow checks to be programmed that would identify cases where the required order was not followed. As noted above, every field that is collected costs money. If we add fields to record the times, we add expense. The team makes the decision based on resources and the importance of the question to protocol compliance. Is it very important to the study and analysis that the draw comes before dosing? If yes, then do we have programmer resources to add the additional fields and checks and/ or do we have time for the monitors to review the data during a visit? In the “Introduction,” we learned that the sites do not enter results directly onto the CRF (paper or electronic); they transcribe the data from source documents after CRF Design Considerations 13 the subject visit. What they put into those source documents is based on the require- ments of the protocol. The source documents are later compared to the CRF data during source document verification (monitoring). If this is true, why do we put aids to protocol compliance in the CRF at all? We add compliance aids to the CRF because site staff may only read the protocol once or twice but they use the CRF for each subject during the entire course of the study. The CRF acts as a reminder of the requirements of the protocol. As the site fills in the CRF prior to monitoring, the site staff may be made aware of an issue that they can then correct before the monitor (clinical research associate, or CRA) visits the site. Collecting Analyzable Data In order to analyze data, it must be the correct data type. If the statistician plans to use a particular statistical method for numerical data, then the results reported in the fields have to be numeric, not text, and they have to be reported using known units. Usually, the clinical team will know what kinds of results are typical for a given procedure, but if the indication is new to the team, they may not know for sure so plan on numeric results to a particular CRF question. It is not until the data arrives on paper, or sites call up to complain because they can’t enter text into an eCRF field, will the problem become known. While a major misunderstanding of expected results happens infrequently, a more frequent example of text in a numeric field in case of a result that is usually numeric but occasionally becomes text when the site enters a result of “trace” or “<10” or “10–15.” These are valid results, but they can- not be analyzed along with the other numeric data. Exactly how these text results for numeric fields are handled often depends on the clinical data management system database or EDC system being used. Regardless of how the results are stored, too many cases of text results will lead to difficulties during analysis. Even when the result data type is known to be numeric and reported as a number, problems can arise if the numbers reported by the sites are associated with differ- ent units. Novice CRF designers may provide a particular unit as text on the paper or electronic form only to get surprising variations when the sites report results in varying units. In consultation with the clinical team, the data manager should ensure that alternate units can be recorded if they can be reported. Most commonly, this is done by having a field to report units on the form or a list of appropriate units from which the site can select. Text can only be analyzed if it is part of a limited list of choices, such as mild/ moderate/severe, male/female, yes/no (often called coded fields or codelists), or if it falls into the few classes of text that can be matched to a thesaurus (see Chapter 26, “Coding Dictionaries and Systems”). Free text cannot be analyzed—it can only be read. In fact, most free-text comment fields, such as “If abnormal, specify” and “If other, specify,” must be read by a clinical team member to ensure that there are no adverse reactions or other safety issues implied in the text. Because little can be done with text in the way of analysis and because it usually requires manual review, free- text fields should be limited to where they provide value in cleaning or interpretation of data. 14 Practical Guide to Clinical Data Management, Third Edition SECONDARY GOAL: REDUCING QUERIES While the primary goals of CRF design must be met, there is a secondary goal that has an impact on whether or not the trial as a whole is considered successful. That secondary goal is to prospectively reduce the number of queries to the site that ask for clarification of the data responses. Discrepancies in data are time consuming to identify, track, and resolve, especially when they involve a query to a site for a paper study, but this is true even for EDC studies where the burden then falls more on the site. (See Chapter 8, “Cleaning Data” for more information on the query process.) Because queries are burdensome, working on a CRF design that reduces the need for sites to address queries that do not actually add to the quality of the data has a great deal of value. Asking the investigator to provide duplicate or repeat information in different locations is one such source of low-value queries. Other low-value queries will be generated if the CRF designer does not make adequate provision for blank or unavailable responses or permits ambiguous responses. Avoiding Duplicate Data CRFs frequently include questions that are intentionally used as cross-checks to other fields. This can be a very good policy when the data is actually different. For exam- ple, cleaning programs can check for logical consistency by comparing the sex of the subject to the subject’s pregnancy status. Or, the weight of a subject can be checked against a weight-dependent dosage administered by the site to confirm proper com- pliance with the protocol. The fields are different, but related. Problems arise when fields are actually duplicates of the same data because it will almost always happen that there will be discrepancies between the two in the course of a reasonably sized study. For example, a CRF should not ask for both the subject’s age and birth date. The computer application—either the EDC system or the data management system used in a paper study—can easily calculate the subject’s age from the birth date or just the age can be used. Asking the site for both is bound to generate discrepancies and confusion when the values do not agree. Similarly, CRFs should avoid asking a site to repeat values, such as “time of treatment start,” in multiple fields, even on the same page, or request that values from one page be copied to another without good justification. The copied values will have to be checked against each other and mismatches will, without a doubt, occur through human error. See Figure 2.2 for an example of inappropriate duplication. Asking the site to calculate values and enter them in the CRF is a form of indi- rect duplication of values. If the CRF asks the site to measure blood pressure three times and then to calculate and report the mean, the value of the mean may be inappropriately duplicating the information in the three measurements. As with the example of age and birth date above, a computer can easily calculate the mean from the three values and would avoid discrepancies caused by miscalculation on the site’s part. Sometimes a protocol requires the investigator to perform calculations as part of determining the correct course of treatment. For paper-based studies, the study team should consider providing space and methods for the calculation on a worksheet or in CRF Design Considerations 15 an instruction area that is not entered as part of the CRF data. A monitor can double check the worksheet as part of normal source document verification. EDC or another automated system is the preferred approach in this case as the software can reliably calculate the appropriate values. Having warned against duplication, it is worth noting an example of when dupli- cation does provide value in data cleaning. Header information, such as subject iden- tifiers and initials, are such an example of useful duplication. Paper CRFs always repeat header sections on each and every page. In studies where pages can come in singly or become separated from the CRF booklet, a cross-check of subject ID with initials for the data on every page against enrollment information has proven invalu- able in identifying mislabeled pages. (EDC systems usually require the site to select a subject and then assign data entered under that subject automatically.) Eliminating Missing or Ambiguous Responses CRFs should be designed so that it is completely clear if the site overlooked a field or if a particular measurement was not available. For example, if a particular vital signs measurement was not performed, the site could just leave the CRF fields blank. But if they do, a query would be raised asking the site to provide a value. In a paper trial, the query would follow a complex resolution process; in an EDC sys- tem, the site would answer right away that the value was not available but someone representing the sponsor would still have to review the response. To avoid unneces- sary querying, sites participating in a paper trial are generally instructed to write ND (for “not done”) or something similar in the field or to check a not-done box to avoid raising a query. EDC systems have various ways of providing the same functionality, but the system must be configured by the CRF designer. The CRF designer—paper or EDC—must always consider what instructions or actions are appropriate for each field that could be “not done” or “not available,” and the ques- tions on a CRF should generally not allow “blank” to be an expected response (see example in Figure 2.3).", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Volume Collected", "definition": "1/22/2011 14:30 19:00 — 1/22/2011 19:00 22:20 — 1/22/2011 22:30 04:45 — 1/22/2011 04:45 10:10 — 1/22/2011 10:10 15:30 — FIGURE 2.2  A re-creation of an actual CRF designed to track urine collection. This CRF form generated a large number of queries because sites were asked to duplicate data—the stop time from one row was nearly always to be copied to the start time at the next row without a gap. Note an example of a typo in the third row, highlighted, that would have to be queried. In addition, the date was to be entered at the start of each row and sites frequently forgot to change the date after midnight on the first collection day as shown in the highlighted cells in rows 4 and 5. 16 Practical Guide to Clinical Data Management, Third Edition While having a single missing value here and there is to be expected, it is also worthwhile to make accommodations for an entire set of questions to have no responses. When questions on a page are logically connected, they are usually grouped into a module and often visually linked via a box around the group. For an EDC study, they would appear together on a single eCRF form. If it is in any way possible that the site will have no responses for the entire module, consider including a not-done box for the module or an indicator at the start of the eCRF form. If the not-done box is checked, no discrepancies will be raised when the values are blank. To make this concrete, let us consider the case of some laboratory values that come from a single blood draw and are evaluated by a local site laboratory. If one of the required values could not be obtained, the site could write NA for the result on a paper form. If, however, the site was not able to obtain a blood sample, they would have to write NA for each result or mark a not-done box for the entire lab result group of fields. The module not-done box can be taken up a level, because ambiguity of missing values can also occur at the page level. For example, some adverse event forms have a header section followed directly by the fields used to report adverse event data. If that page or form data comes in to data management with all header information filled in but the rest blank, there is no way of knowing if there were no adverse events or if the page was inadvertently left blank. Other types of important data, such as hospitalizations or concomitant medications, are collected on pages that have the same characteristics. These kinds of pages or modules should have an indicator field after the header that asks “Were there any adverse events?” (or medications, or hos- pitalizations). To be consistent with the previous suggestion, the indicator variable should have both a YES and a NO box to ensure the response is unambiguous. CRFs WITH DATA PROCESSING IMPACT Certain kinds of CRFs or eCRFs may be necessary and appropriate but still have a significant impact on the way the study is processed. When reviewing CRFs for a", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Discharge", "definition": "□ Several options would make the response unambiguous, including: •\t YES and NO boxes for each question •\t A “check if none” box at the beginning or end of the list •\t A preliminary question such as: “Were any of these symptoms present? Yes □ No □” FIGURE 2.3  In this example, if none of the symptoms were present, the entire section would be blank. This represents an ambiguous response—did the site overlook it or were no symptoms present? CRF Design Considerations 17 study, data managers should keep an eye out for these kinds of designs and plan for their impact—not just on one particular data value or set of values, but also on the flow of the data through the trial. Examples of designs that impact processing include: •\t Log-type forms for AEs, etc. •\t Questionnaires •\t Diagrams or analog scales •\t Early termination pages In the following sections we look at these in more detail with considerations for paper and EDC trials.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Log Forms", "definition": "Most CRF pages collect information associated with one particular visit or treatment round. For some kinds of clinical data, CRF pages that collect information across visits for the entire course of the subject’s time in the study make more sense. These kinds of forms are referred to as log forms. The site enters information on that page or form as the study progresses, completing the page or form with the final data as the subject completes the study or, in the case of paper, as the page is filled up. These log forms are used because they have advantages for certain types of data, but they have an impact on how the data is processed. Data management must be aware of the advantages and implications. Adverse event and concomitant medications are good examples of the kind of data that can be collected either with a visit-based form or with a log form. In a visit-based version of a concomitant medication form, the data collected on a page is the list of concomitant medications for that visit/treatment or since the last col- lection only. There are usually some medications that are continuing from the last time, some that have started and stopped, and some that have started and are con- tinuing. When analysis of this kind of data is made to determine interactions with other medications or the relationship with reported adverse events, statisticians try to collapse all the reports into a single list of start and stop times. This turns out to be difficult. Subjects and investigators have a hard time keeping the continuing medica- tions straight from visit/treatment to visit/treatment. (See the previous caution abve about not requiring that data be copied by the site from visit to visit.) Some are for- gotten; others are described differently so that it is not clear whether they match up with a previously noted medication. When using a log form CRF data collection, the concomitant medications are listed independent of visit, with just their start and end times or an indicator that they are continuing past the end of the study. When the sub- ject starts a medication, it is added to the page. When the subject stops or takes just a single dose, the end date and time are filled in. This does away with the problems of collapsing the data into a single, coherent list. Adverse event log forms have similar advantages in collapsing events. The difficulty with log forms centers on when they are considered “complete.” The data may be immediately available in an EDC study, but there may not be end dates until the end of the study or the medication may be continuing past the end 18 Practical Guide to Clinical Data Management, Third Edition of the study. In paper-based studies, the forms will only be sent to data entry when they are full or when the subject completes the study. These pages may show up on “missing” or “expected” page reports for individual subjects until that time. More importantly, there won’t be any data until the pages come in for data entry. That means there will be no AE or medication data whatsoever for some subjects. When midstudy safety summaries or reports to data safety monitoring boards are due, sig- nificant amounts of data may be missing. At some companies, early versions of log forms are sent in as a fax or copy as the study proceeds. This allows for data entry and discrepancy checking of the available data. Unfortunately, data entry staff must be trained both to enter any new data and also to check the previously entered data to see if anything has changed each time the page arrives. One approach to dealing with these entry issues is to perform first entry on the data when it first comes in and then to do a final, second-entry pass of the entire set when the final version arrives. While this method assures that the data and the final version agree, not all data entry applications support this kind of partial entry very well. Neither the sponsor nor the site know how many AE, concomitant medica- tions, or other log pages will be needed to record all of the data for a given sub- ject, so the CRF designer for a paper study includes multiple copies of the blank page. These are often called repeating pages. See the discussion in Chapter 7, “Overseeing Data Collection,” for more information on data entry and tracking for repeating pages.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Questionnaires", "definition": "Quality-of-life questionnaires introduce interesting considerations to the data man- agement process. Questionnaires are one form of patient-reported outcomes and are to be filled out by the subject during a visit to the site. This is true for both paper and EDC studies. Because in EDC studies the subject won’t typically have access to the site’s computer, the subject fills out a paper questionnaire. (There are exceptions; for larger or important studies, some sponsors use handheld devices to collect data directly from the subjects.) In paper-based trials, data entry proceeds as normal, but in EDC studies, special provision must be made to get the questionnaire results in the system. Many sites balk at performing questionnaire entry, which means that data management must specify alternate entry procedures to be followed by data management or by an external contract research organization (CRO). Access to the EDC system for entry of questionnaire data must be strictly limited to very specific forms and the sites must be able to see, but not to modify, those forms. This is one of the rare examples of where data flow for an EDC study is more complex than for a paper study. Even in studies conducted solely in the United States, questionnaires will be offered in languages other than English. Typically the CRF pages will be sent out to a certified translator. The least impact on the data management process can be achieved if the translator is instructed to keep the same questions on the same page. That is, if questions 1 through 10 appear on the first page in the English version, questions 1 through 10 should appear on the first page in the Spanish and Chinese versions. This will allow the CRF pages to have the same page number and for the CRF Design Considerations 19 entry screens to remain the same. The entry staff will simply enter the responses indicated by the subject using the question number regardless of the language used to ask the question. When a site requests a special language version of the question- naires, they simply swap out the English pages for the customized pages. Tracking and missing page reports need not be changed. When the questions do not map to the same pages, the impact to database design, entry screens, cleaning programs, and tracking will be huge. To facilitate the trans- lations, CRF designers should keep the English language versions fairly sparse as other many languages require a longer text length to convey the same meaning. Note that data management groups should consult with clinical and biostatistics team members to determine whether there is value in noting the language of the question- naire that is the source of the data for any given subject. Diagrams and Analog Scales Some studies collect information in areas such as disease history and efficacy using diagrams or analog scales. When the study is paper-based, data from either of these can be very tedious to transcribe for entry to produce data that can be analyzed or categorized. A computerized application (usually not simple EDC entry screens) is a much better choice for producing accurate and consistent data. When diagrams are used, they are typically preprinted figures or portions of the body. They allow the investigator to provide very specific details as to the location of the condition in question (e.g., lesion or tumor). Collecting the information from a pre- printed diagram for storage in a database typically involves categorizing the figure into sections and entering section identifiers. On paper CRFs, a CRA or data entry operator would determine the value to enter using a tool or grid, and a second person would make the determination independently as a double check. Computer applications make the determination or translation to data automatically, accurately, and consistently, but these are specialized applications and not the EDC system used to enter eCRF data. Because of the difficulty in getting data from diagrams, they are rarely used. Analog scales, however, are very common. The most common analog scale is a line with two extremes described at each end; the left side may be labeled “0” and the right “10.” The subject makes a mark on the line to indicate the appropriate level between the extremes allowing subjects to indicate exactly how they feel. The con- tinuous scale avoids forcing a subject to fit their responses into a predetermined set of categories, such as “mild, moderate, or severe.” Analog scales are often used in quality-of-life-type questions, such as an indication of a subject’s level of pain or of the level of satisfaction with a given treatment. Capturing data from an analog scale usually entails measuring the point at which the subject’s mark crosses the line. The line is of a fixed length, and the distance from the low extreme (e.g., zero) to the subject’s mark provides the data value. To work across subjects, the length of the full line has to be exactly the same for each subject so that magnitudes appear the same. In paper-based studies, the length of the line may vary somewhat due to variations in printing and paper size, and two measurements are needed to be truly accurate: that of the actual length of the line and that of the position of the mark. The person making the measurement must be 20 Practical Guide to Clinical Data Management, Third Edition very careful and the rulers must all be exactly the same (no inexpensive schoolroom rulers). Frequently, the site or the monitor will be asked to make the measurement and record the number on the CRF. In some cases, however, data entry will make the measurement and enter it. In this case, it is critical that the actual measurements be verified by a second person performing the measurement. Some electronic CRFs used in EDC and some electronic patient recorded out- come (PRO) tools, can handle analog scales extremely well. The subject marks a point on a scale and the computer stores a resulting data value. No rulers or grids are needed. All data management departments should consider using these tools for diagrams or scales in large trials, even if the rest of the data is collected on a paper CRF. As in the case of paper, the lines of should appear roughly the same for each subject. The application cannot scale the analog scale to the display screen of differ- ent computers or to the current size of an application window. Early Termination Visits Even in Phase I studies, it is likely that some subjects will leave a study before they complete the course of visits or treatment. Leaving a study early is commonly called early termination. Clinical protocols typically include instructions to the sites on what to do if a subject terminates early. The protocol provides the instructions in the form of which tests should be performed, what samples should be taken, and what additional information is to be gathered at that last visit. The CRF designers are left to represent these instructions as CRF pages. Because the protocol instructions are often of the form “in the case of early ter- mination, the subject should fulfill the requirements associated with the Day 50 visit, as well as the study drug and study termination pages …” the CRF designer may choose to designate the pages associated with Day 50 (in this example) as “Day 50 or Early Termination.” Unfortunately, while this design does meet the requirements of the study, it has a negative impact on the data. In this case, it may not be possible from the data itself, once it is in the database, to determine whether it came from Day 50 or from an early termination visit! The question is further confounded if the subject did in fact complete the visit prior to Day 50 and then terminated early. If the subject goes past Day 50 and then terminates, there will be two sets of data for Day 50. While all this can be clarified at the end of the study by manual inspection, during the course of the study it will impact discrepancies, CRF tracking, and lab data received from central labs. A separate set of early termination pages may add to the amount of paper sent to the site or the number of eCRF forms that have to be developed but will result in much clearer data and easier processing during the course of the study.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "REVISIONS TO THE CRF", "definition": "During the course of the study, a clinical protocol may change in such a way as to impact the CRF pages. Two common changes are the addition of lab tests and changes to the inclusion or exclusion criteria. Whenever a revision to a CRF page or screen is required, the data management group together with the clinical team members must evaluate the appropriate course of action and thoroughly understand CRF Design Considerations 21 the implications of the change. Both paper and electronic CRFs will require a data- base change (see Chapter 25, “Change Control”). When site level institutional review board (IRB) approval is required, distribution of paper pages is easily managed. For EDC studies, however, the EDC software system will determine if site-by-site level roll out of changes is even possible or if all approvals must be received before the changes are made available to sites. For both EDC and paper, the clinical team should think through handling of pages and data that already exists: •\t What to do about pages/forms already containing data; must they be tran- scribed or reentered? •\t For new fields, will existing subjects be queried to fill in the data (paper) or asked to go back and fill in new fields (EDC)? •\t For paper-based studies, will both old and new pages continue to come in or will blank pages be swapped out so only new pages will come in going forward? •\t How are data cleaning rules (edit checks) affected—add, delete, or modify cleaning rules? •\t Can the change impact queries already open at the site? •\t How about existing data; are any new manual queries required to confirm previously received data based on new protocol requirements? Clearly, a careful coordination between the clinical and data management team members is essential. It is fairly common for a question to arise after a CRF change regarding the ver- sion of the CRF page that was used by a site for a given subject. Consider storing the CRF version information in one of the systems being used in data management, or at least the version after a revision. Many statistical programmers like having the ver- sion number with the data in the database in studies where the CRF changes impact how the data will be reported. QUALITY ASSURANCE FOR CRFs Having experienced members of the CRF team carefully review each page and the complete booklet or visit structure is the best form of quality assurance for CRF design. As a team or as individuals they should be checking the following: •\t That all tests and data required by the protocol are found at the appropri-", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "ate visit", "definition": "•\t That the data to be used in analysis is actually being collected •\t That standard or historical CRF modules have been used, if they are available •\t That checklists or codes for categorical fields are consistent within this study and across related studies •\t That instructions printed on the CRF are clear and unambiguous Teams typically perform this kind of review on unique pages or forms, but some of these will appear multiple times in a study, such as vital signs collection that might 22 Practical Guide to Clinical Data Management, Third Edition appear at every visit. At least some of the team members should be sure to view the entire CRF booklet or study eCRF to check that the modules all appear as required.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "SOPs ON CRF DESIGN", "definition": "A standard operating procedure (SOP) on CRF design or eCRF specification will likely be a cross-functional one developed and approved by all groups involved. The content of the SOP can be fairly light, perhaps only outlining the expected flow from who is responsible for initiating and/or leading the process through all the people that will sign off on a final version. Many companies require a review against com- pany standards early in the development process. There must be a procedure in place for amending the CRF and approving that amendment. All versions of the CRF must be stored in the clinical data management or trial master files. REUSE AND REFINE CRF MODULES Companies, not just data management groups, benefit from the use of standard mod- ules for CRF design. When a standard module is used, the completion instructions are the same, the database design is the same, the edit checks are the same, and the associated listing and analysis programs are the same. It is also easier to com- pare or combine data across studies. Medium and large-sized companies typically have a standards reviewer and sometimes a process for requesting deviations from standards from a committee to ensure that individual studies get the benefit of the standard sections. While reusing standard sections has high value, a very common problem that companies face is not changing a module when it is not working. If data management has found problems processing data from a certain CRF module, it is clinical data management’s responsibility to take that into consideration and refine the module for the next study or to request changes from the standards committee. 23 3 Database Design", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Field Data Types", "definition": "Specifying the data type of a field tells the database what kind of response is expected, and so defines storage characteristics as well as properties or attributes of the data. For example, numbers can be multiplied together but texts cannot. Dates can be subtracted to give you the number of days in between, but adding them does not work. You can do spell-checks on texts. In the chapter on CRF design, we talked about collecting analyzable data and how that translates into the need to know what the data type of the responses would be. To do numeric analysis, we needed to collect numeric data, not text. When we design a paper CRF we lay out a place for a field on the page; when we design the database, we specify that the field will be a numeric field. In some systems, it will only be possible to type a data type consistent with the database field definition into that field; in others it will be possible to store it, but it will be flagged as being unacceptable and will not be included in analysis. In the next Database Design Considerations 25 sections, we look at the most typical data types for fields (numeric, date, and text) and the design considerations associated with those data types. Some databases have other data types for clinical data, including coded; coded data is also discussed in the following sections. After defining each type, we will look at potential problems and limitations associated with fields of that type.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Numeric Fields", "definition": "Numeric fields mean that a number is expected. Values stored in these fields have attributes and characteristics of numbers; for example, they sort numerically, and arithmetic operators such as plus, minus, and divide work on them. Some databases differentiate between integers (whole numbers) and floating point or decimal num- bers; integers may be associated with an attribute of the maximum number of digits allowed (3 for the number 999) and decimal numbers with an attribute that specifies the number of digits before and after the decimal (for example, in PL/SQL number [7.2] would be 5 digits before and 2 digits after the decimal). Text in numeric fields occurs in all kinds of clinical data and creates a tension between the need to record exactly what is in the source documentation (or on the paper CRF) and the need to collect data that actually can be analyzed in a sensible way. Examples of this text for a numeric value include: the word trace found among lab measurements, a value of <5 found in an efficacy measure, or a range of 10–15 found in a count of lesions. With assistance from clinical staff, data managers must identify which fields may contain data like this, and if the system does not support it, they must decide on a storage option. Options for handling this type of data include: •\t Designing the database field to be a text field so that both numeric and text values can be entered. At the time of analysis, those values that can be converted to numeric will be. This can severely limit the kinds of field-level checking that are possible and adds additional steps to all data cleaning functions known as edit checks (see Chapter 4). •\t Using a numeric field to store the data and issuing a query to the site if there is text in the field. This makes the most sense when the data represents a critical measurement expected to be numeric. •\t Creating two fields: one text and one numeric. All data from the CRF is entered in the text field. The values in the second, numeric field would be automatically derived (calculated) from the first when the first contains number-like values. Data cleaning is performed only on the converted numeric value. •\t Using a numeric field to store the numeric values and create an associated text or comment field to hold text values only when they occur. The site (if using EDC) or the data entry staff must be trained to use that second field. Contrast this with the previous approach. Different clinical results may warrant different approaches. It is in not necessary to pick a single option and apply it to all of these kinds of fields in a study. Consult with both clinical experts and statisticians to make appropriate choices. 26 Practical Guide to Clinical Data Management, Third Edition When numbers are used as identifiers, such as a subject ID or lab center ID, a problem can arise in displaying them that can make using a text data type a better choice. Identifiers such as 1002003 certainly look numeric and that one would work, but when the values are very long, many database and analysis systems will display the number in scientific notation. That is, a subject number such as 110001999 may display as 1.1e8. Even though the storage is correct, this is not a useful way to display subject identifiers. Other examples of fields prone to this problem for clinical data that include sample IDs and kit numbers. Subject number fields may also have a leading zero problem. That is, a subject number may be 010004 for a site 01 and subject 0004. In most systems, leading zeros are stripped from numeric fields so that the value will appear as 10004. While this does not invalidate the content, it would be inconsistent with values that appear on paper or in other systems and would likely confuse human viewers of the data. To avoid this problem and the one described previously, define these special integer fields as text fields.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Dates", "definition": "When a database field is defined as a date, the database knows some properties of the content of the field. There are acceptable choices for months entered as numeric or text values. Obt is not an acceptable month, nor is 13. Dates can be compared, as some dates are greater, or more recent, than others. Subtracting dates works to pro- duce a number of days in between as does adding days to one date to get a later date, but adding dates does not work. Dates seem to be a clearly defined data type that should not cause problems for data management; however, problems arise because some databases cannot handle incomplete dates when the complete date is not known, such as 6/2001 for June of 2001. Dates on a CRF typically fall into three categories: 1.\tKnown dates related to the study (visit date, lab sample date) 2.\tHistorical dates (date of previous surgery or prior treatment) 3.\tDates closely related to the study but provided by the subject (concomitant medication and adverse event start and end dates) The first kind of date is needed for proper analysis of the study and adherence to the protocol and, in theory, should always be complete. The second kind of date is often not known exactly by the subject, and so partial dates are common. The last type is particularly difficult because the dates are actually useful to analysis but not always known exactly to the subject, especially if there is significant time span between visits. A normal database data type of date usually works just fine for known dates related to the study. If the date is incomplete, nothing is stored in the database, a discrepancy can be issued, and it is likely a full resolution can be found via the normal query reso- lution process. Dates in the second category are often not analyzed but are collected and stored for reference and medical review. A good option for these kinds of dates is a simple text field that would allow dates of any kind (e.g., June 2001 or 06/2001). Database Design Considerations 27 The third category of dates presents data management with many problems. These dates may be used for analysis, and so they should be complete, but the subject may not know the exact full date. A few companies have had data entry or site staff members fill in the missing date parts according to entry guidelines. Unfortunately, this has resulted in inconsistencies and misunderstanding. More typically, database designers address this problem by creating three separate fields for the month, day, and year, and have a fourth, derived or calculated field either in the database (see fol- lowing text) or in the analysis dataset that creates a complete date when all parts are available. Depending on the circumstances, the algorithm to create the full date can make some assumptions to fill in missing pieces. For example, if the day is missing, it might assign 01. (Sometimes there is a fifth field that is associated with the set that indicates whether the date was fully specified as collected or is based on an assump- tion or has the date stored as a text string in an alternate format.) See Figure 3.1 for an illustration of this approach. A study may well have dates in different fields that fall into all three categories. Discussions with biostatisticians on the types of analyses to be performed will clar- ify which dates must be complete, which can be stored as partial dates for reference, and which can be approximated to allow some information to be extracted from them. Some database designers will mix and match date field approaches while oth- ers will opt to decide on a single multifield approach and use it for all date fields to assure consistency for entry and reporting. CRF: Adverse event start date: __/__/____ (month, day, year) Database fields:\t AE_START_MM defined as 2 characters to allow leading zero AE_START_DD defined as 2 characters to allow leading zero AE_START_YYYY defined as 4 characters AE_START_DATE defined as a derived date DATE_STATUS defined as a character but also derived Derivations: AE_START_DATE would be the combination of date parts converted to a date data type if all three pieces were present. Depending on the meaning of the field, one option would be to fill in “01” if the day were missing and even to fill in “06” (for June) if the month were missing. The value would be left empty if the year were missing. DATE_STATUS might be derived to a value of “Y” if all three parts are present, to “N” if day or month is missing, and to “D” (for discrepancy) if the year is missing. Examples:", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "DATE", "definition": "06/12/1998 06 12 1998 06/12/1998", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "FIGURE 3.1  An illustration of using a set of fields to avoid problems in collecting and stor-", "definition": "ing incomplete dates. 28 Practical Guide to Clinical Data Management, Third Edition Another problem that can arise for larger global studies is the limitation some databases have regarding the format used to enter dates. In the United States, dates are often entered using a mm/dd/yyyy format, where 07/04/2001 is July 4, 2001. In Europe, the format is more typically dd.mm.yyyy, where 04.07.2001 is July 4, 2001. Some companies, when faced with limitations on entry of dates, are moving to a more global ddMONyyyy (04JUL2001) format, which seems to be well received by most sites.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Texts", "definition": "Text fields are very flexible as there are almost no limits on the contents of a text field. But, even text fields have their problems, and they are all associated with length. For most databases, the designer specifies a maximum length for the field. While there is plenty of space on servers to store large text lengths, longer fields do adversely affect the display of the contents on the screen, in reports, and in analysis datasets. So, the designer selects a reasonable number of characters based on previous experience. Every so often, data will come in that is longer than the specified length, meaning the database must go through a modification to widen the field in the database to store the longer text. As we will see in Chapter 25, “Change Control,” a field revision can be a time- and resource-intensive task, so to avoid a change for a single value, the monitor will often work with the site first to see if it is possible to arrive at some equivalent, but shorter, text. The longer the text the bigger the problem: even when database storage has been appropriately defined to capture longer text fields, some query tools and analysis programs have limitations on the length of text fields to which easy access is pro- vided. That is, very long comments take more work to extract and display. A char- acter length of 200 is one of the common limits. In some situations the combination of database limitations and reporting tools will make it necessary to have several numbered text fields (e.g., Text1, Text2, etc.) to store a long text. A series of related text fields has several drawbacks. The designer must guess the number of fields to create, and data entry staff or the site must determine the best way of breaking up the text between lines. Also, review of the complete text requires the extraction and reformatting of the entire set of fields, which usually makes ad hoc retrievals of the text impractical.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Coded Data", "definition": "In clinical trials, many fields have a limited set of possible responses and most clini- cal database applications have a special type of field “coded” to support these called. Common examples of coded fields include yes/no answers, male/female for gender, and mild/moderate/severe for severity. For coded fields, it is usually not possible to enter a value outside of the list of acceptable responses, which is typically known as a codelist. In most systems, codelists have values that are displayed and values that are stored, and they do not have to be the same data type. A codelist for severity might be MILD=MILD, MOD=MODERATE, and SEV=SEVERE or it could be 1=MILD, 2=MODERATE, 3=SEVERE. The numbers that are stored in the second example Database Design Considerations 29 have the nice property of sorting in increasing severity. Whenever data is displayed or reported, the stored value must be decoded. Most software used by biopharmaceu- tical companies has this functionality built in. Coded fields are meant to be used in cases where the response is a single value selected from a list (“Select one …”). When more than one answer from the list is possible, the database design changes from a single field associated with a codelist to a series of fields, each of which may be yes/no. See Figure 3.2 for an example. Data management challenges for coded fields arise when the response frequently falls outside the predefined list. It is most common to add a code for the category “other” when the designer expects that sometimes the responses provided may not cover all the possibilities. When the choice of “other” is provided in the codelist, the designer frequently adds a text field to the collection form identified as “if other, specify” to allow entry of a short free text. There are specific statistics that apply to fields that are categorical like coded fields, but they won’t do much good if a high percentage of the responses fall into the “other” category and show up in the", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "SINGLE RESPONSE", "definition": "CRF/eCRF Treatment required (check one): [ ] None  [ ] OTC [ ] Prescription Drug [ ] Hospitalization [ ] Non-drug Therapy", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Database Design", "definition": "TRT_REQ defined as a coded numeric field TREATMENT codelist defined with the possible values: 1 NONE 2 OTC 3 PRESCRIPTION 4 HOSPITALIZATION 5 NON_DRUG_THERAPY", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "MULTIPLE RESPONSES", "definition": "CRF/eCRF Treatment required (check all that apply): [ ] None  [ ] OTC [ ] Prescription Drug [ ] Hospitalization [ ] Non-drug Therapy Database Design #1 TRT_NONE defined as a coded field with YES/NO TRT_OTC defined as a coded field with YES/NO TRT_PRESCRIPTION defined as a coded field with YES/NO TRT_HOSPITAL defined as a coded field with YES/NO TRT_NON_DRUG defined as a coded field with YES/NO Database Design #2 TREATMENT defined as a single text field; entry staff enter a string of comma-separated numbers, such as “2, 3”. FIGURE 3.2  An example of a database design for a coded field with a single answer and two designs that permit multiple answers. Notice that the CRF page or form is the same except for the instruction text. This example refers to a treatment-required field typically found on adverse event CRF pages. 30 Practical Guide to Clinical Data Management, Third Edition additional field. It is worth noting that if the data manager reviewing the data notices certain values in the other-specify field that show up frequently, it would be worth considering adding codes to the codelist through a database revision. Another problem that can arise with codelists is when the list gets too long. For example, if a sponsor decides to categorize the location of a lesion on the body, and the entire body is possible, the list could get too long. If the site cannot easily see an overview of codes and cannot quickly find the appropriate code, they may pick the wrong one. The meaning of too long is dependent on the type of information in the codelist, but the clinical team should consider using a different approach if the number of options or codes goes above 20. The options for long lists of codes or responses would then be free text, which cannot be analyzed at all, or a field that is automatically coded (autocoded); that is, the short reported text is automatically checked against a large codelist by the computer system. Large codelists (sometimes called dictionaries or thesauri) are standard for a very common class of short free- text fields that need to be categorized: adverse events, medications, and diagnoses. These kinds of free text are often called reported terms, and the matching of the terms to a dictionary is a complex coding process. See the coding section of Chapter 11, “Collecting Adverse Event Data,” for more information on coding reported terms and Chapter 26, “Coding Dictionaries and Systems,” for more information on how dictionary coding works.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Identifier Fields", "definition": "When the data appears on a paper CRF or in an eCRF form, the subject number is visible to identify to whom the data belongs. When the data is analyzed, it is extracted from the underlying database and it must always have the subject identi- fier associated with it. For data that appears only once per study for a given subject, such as that subject’s date of birth, only the subject number is required to uniquely identify whose date of birth it is. But other data is gathered more than once over the course of the study and must also be completely identified. For example, if weight is taken at each visit, it is important that each weight is associated with a visit identi- fier. For blood pressure taken multiple times over a single visit and at many visits, the nominal time within a visit is needed in addition to the subject and visit. That is, if blood pressure in a given study is measured pre-dose, 2 minutes post-dose, and 20 minutes post-dose, it is important that this information appear in the data records so that a statistician can compare the blood pressure pre-dose to that taken 20 minutes post dose and compare it across subjects and visits. The subject and visit are common to all clinical trials, so CDM systems and EDC applications have those as built-in identifiers known as context or header fields. The additional levels of identification, such as the blood pressure interval described previously, have to be designed in. In EDC studies, subject identifiers are typically registered to the system when the subject is enrolled. Sites enter data by selecting the subject and clicking on the appropriate visit. When the system raises discrepancies in the data, those discrepan- cies automatically appear on the right field. Paper has many places for incorrect tran- scription built in and has the added difficulty of having to send queries to the sites on paper in such a way that the site can fully identify the fields that generated the query. Database Design Considerations 31 To support identification and cleaning, paper studies require additional identifying information. For example, because sites sometimes write the wrong subject identifier on a page, it is common practice in paper studies to include subject initials on each page in the header. While this violates the CRF design guideline of not duplicating data, as we saw in Chapter 2, this is one area where duplication helps more than it hurts because it provides a tool for checking data. Other fields commonly found in the headers of paper studies include the page number so that queries can be very precise and sites can quickly find the location and page type or name, such as vital signs or dosing to assist in tracking missing pages so that site monitors know that the missing page 12 is a page containing vital signs. Some identifying data is preprinted on the CRF or predefined in an eCRF. When we create a paper CRF, the information that identifies a response may actually be preprinted on that page. That is, we don’t typically ask the site to enter Day 1 in a field labeled “visit identifier.” That would be found preprinted at the top of the page. But it has to get into the data somehow to provide context to the rest of the data found on that page, so the database designer will create a field for “visit” and prepopulate it on the data entry screen used to transcribe the CRF data from that page into the database. Similarly, the nominal time points mentioned previously for blood pres- sure readings would likely be preprinted next to the field on a paper CRF with fields for the actual time and the results for a paper study. Again, those would be actual database fields, prefilled in the entry screen. A similar practice holds for eCRFs: identifying values must somehow get into the database without requiring the site to enter all kinds of text beyond the results of required procedures. So here, too, we create fields for that identifying information and prepopulate them so they appear on the screen, and we would often make them non-enterable so that the site could not change predetermined identifying informa- tion such as pre-dose. Calculated or Derived Values Clinical data from a CRF, eCRF, or electronic file is not the only data associated with a study. There are internal fields that can be convenient and even very impor- tant to the processing of data that are calculated from other data using mathemati- cal expressions or are derived from other data using text algorithms or other logic. Examples of calculated values include age (if date of birth is collected), number of days on treatment (when collecting treatment dates), weight in kilograms (if it is collected in pounds), or lab values in standard international units (when a variety of lab units are collected). Examples of derived values include extracting a site identi- fier from a long subject identifier, assigning a value to indicate whether a date was complete (see previous sections), and matching dictionary codes to reported adverse event terms. Some of these values are calculated or derived in analysis datasets; oth- ers are calculated or derived in the clinical database. Database designers should identify the necessary calculated and derived fields and determine whether they will be assigned values as part of analysis or as part of the clinical database. If the values for internal fields are needed for discrepancy identification or report creation, then storing them in the database may be the most 32 Practical Guide to Clinical Data Management, Third Edition sensible approach. If the values are used only during analysis, there may be no need to create permanent storage for them in the database. Calculating or deriving values in the database means that the expression or algorithm is written only once and run consistently throughout the course of the study, whereas calculations performed at the time of analysis may have to be duplicated in several datasets. In this case, filling the internal values centrally in the database reduces the effort to write, validate, and run the calculation or derivation. TALL-SKINNY VERSUS SHORT-FAT TABLES Our discussion of database designs up to this point has stayed away from the under- lying structure of tables or records because many of the problems that clinical data fields present impact all database systems. One design discussion that does warrant recognition of the underlying application is the discussion of the normalization of a database. Database normalization, in general, is the process of creating a design that allows for efficient access and storage by minimizing redundancy and creating smaller, well-structured tables. It usually involves a series of steps to avoid duplica- tion or repetition of data by reducing the size of data groupings or records. In some systems, database records are intrinsically linked to the CRF page so no choices regarding normalization in the underlying system are available to the designer; in other systems, a high level of normalization is enforced and the designer has no say. In other clinical data management applications, the database designer may choose how much to normalize a design. This discussion is geared at providing background for clinical data managers making those choices. Figure 3.3 shows an example of vital signs data stored in a nonnormalized form and then the same data stored in one type of normalized form. (There are several lev- els of normalized forms that are not discussed here.) The normalized version of the Data Storage in a Short-Fat Form", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Visit", "definition": "BP_ DIA_1 BP_ SYS_1 BP_ DIA_2 BP_ SYS_2 BP_ DIA_3 BP_ SYS_3 1001 2 120 72 118 70 117 68 Data Storage in One Tall-Skinny Form", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Measurement", "definition": "BP_DIA BP_SYS 1001 2 1 120 72 1001 2 2 118 70 1001 2 3 117 68 FIGURE 3.3  An example of three blood pressure readings stored in a nonnormalized (short- fat) form and the same data stored in one normalized (tall-skinny) form. Notice that blood pressure is always stored as two fields in databases, systolic and diastolic, to allow analysis of the components individually. Database Design Considerations 33 table has fewer columns and more rows. The visual impact that normalization has on a table has led to the colloquial, easily remembered nicknames for these structures: short-fat for nonnormalized tables and tall-skinny for the normalized form. Both kinds of structures store the data accurately and allow for its appropriate cleaning and retrieval, yet the choice impacts data management and analysis in several different ways. For example, data cleaning checks that compare start and end values of blood pressure at a single visit are much more easily performed in the short-fat format. Missing values are more easily detected in the short-fat form unless the entry and storage application ensures that a row exists even when values are blank. Creation of the structures themselves and the associated checks is easier in the tall-skinny form, since there are fewer fields to create. Data querying is also easier in the tall-skinny form, since the field names containing data are clear and there is only one column per kind of field blood pressure component (systolic and diastolic). The tall-skinny format does duplicate header data and other context data to each row. Clinical data contains many examples of repeated measurements that lend themselves well to storage in tall‑skinny tables. These include physical exam (with columns for examination, normal/abnormal, and explanation if abnormal), medi- cal history, x-rays (site, measurements), tumor assessments (site, description, size), adverse events (event plus assessment), concomitant medications (name, route, dos- age, frequency), and so on. In general, any data collected in a tabular format on the CRF/eCRF is a candidate for storing in (at least a partial) tall-skinny form. Lists of related questions, each of which has the same kind of response, may also be stored this way if convenient. Inclusion and exclusion criteria are examples of this kind of list, with each question having the same yes/no response (as long as there are no measurements tucked in amongst the questions). In all of the previous examples, the data in each column of the table is of the same kind and type. That is, a column contains data from a single kind of measure- ment. A data cleaning check, such as a range, applied to the column makes sense for each value in that column. The tall-skinny form is so flexible that it is sometimes applied to data where the values in a single column are not the same kind of mea- surement. Laboratory data is the classic example of this use of the tall-skinny form in clinical trial databases. One column may give the test name, one the test result, another the test units, and so on. (Note that a range applied to the test result would not be sensible as the values represent a variety of measurements.) See Chapter 9, “Managing Lab Data,” for more discussion of using tall-skinny structures for this type of data. Taking this idea of a tall-skinny table a few steps further, we can reach a point where a table contains the names of all the fields to be measured, their value, and other information related to the measurement. The additional information may include status of the data, such as whether the value has a discrepancy associated with it, whether it has been monitored at the site, and so on. This structure is some- times called hyper-normalized and is the basis of some of the newer homegrown and vendor-developed clinical data management systems. Features and tools to conveniently access the data and also to reconfigure it for analysis are critical to these systems. 34 Practical Guide to Clinical Data Management, Third Edition", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "USING STANDARDS", "definition": "Most companies have some standards in place for use in the design of CRFs and databases. Standards simplify the process of designing and building database appli- cations and also speed the process. Designers use existing fields, codelists, modules, tables, and other database objects, within reason, since it is wrong to force a value into a field that does not truly reflect the content of the field and intent of the value. Yet without strong requirements to use what is there (and sometimes even with those requirements), human nature and the desire to work fast always causes a proliferation of database fields that are really the same but are defined with different names and slightly varying characteristics. Besides causing more work at the time of creation of the database, such nonstandard fields greatly increase the effort required for valida- tion both of the database application and also of the analysis programs associated with that data. Because the effort to test and validate EDC studies is greater than that for traditional systems, the effort to stick to standards has an especially significant impact on study timelines for EDC. Applications vary widely in how they support and enforce standard attributes of database objects. Some, such as systems linked tightly to CRF pages, may not have much support for standards at all. Others, such as large CDM applications that support a range of data management activities, may support different levels of standards enforcement that must be set or “turned on” after installation or even on a study basis. That is, a special standard study or standards area must be created and all new studies must link to it. In some of these applications it is also possible to create one or more hierarchies of standards to support different indications or geographical areas. The concept of standard object libraries is also appearing in EDC systems. No matter what the technology to support enforcement of standards (and espe- cially if there is no systems support at all), companies should put a process in place to guide use of standards. In smaller companies, the process may be the honor system where clinical data managers request review for new objects, or variations from a standard. Larger companies may have a standards committee or a standards “czar” who reviews all new items and enforces standards with special tools. The committee or czar may be the only ones empowered to define new objects and sometimes they are the only ones with permission to actually create those objects in the system. These committees have value in that they create a person or group that has good oversight over both the philosophy of database design and the par- ticulars of the database objects used by all projects. Unfortunately, they can become unwieldy and delay work by not meeting frequently enough, by taking too long to produce a new object, and by not providing enough people with privileges to create objects, thereby creating log jams in database object creation when people are out or there are many studies starting up. (Note that the approvers for new objects do not necessarily have to be the same people who can create those objects.) Even small data management groups will benefit from having one or two people who review database designs so that the philosophy across studies is similar and the fields are defined consistently. Even a little bit of standardization can go a long way in reducing setup time and in the associated validation of the study database. Database Design Considerations 35 In all companies, true database standards can only be achieved when the CRF or eCRF module is standardized as well. The most successful standardization efforts involve clinical teams, programmers, and statisticians working with data management and within database system restrictions. No standardization effort will work unless all department managers commit to following the standards that are developed. AFTER DECIDING ON A DESIGN Determining the design is just the first step in creating a database—and the most time consuming. The design is documented in a specification that guides the actual database building process and will later be used in testing. For paper studies, an annotated CRF is commonly used to document the database. For EDC systems, it is more often a separate design or specification document because the visual repre- sentation of the CRF is not as important in EDC applications. (However, annotated CRFs are still produced, often by the EDC application itself, for use by other groups.) Chapter 5, “Preparing to Receive Data,” discusses the specification further as well as the testing of databases and EDC applications in detail. QUALITY ASSURANCE FOR DATABASE DESIGN Use of standards and reuse of similar modules is the best way to ensure quality in database design. Every time a new database object is used and put into production, it opens up a chance for design errors. Even when standard objects are used for new studies, the designer may choose the wrong object. Because database design is a critical step in conducting a study, a policy of do and review should be in place. That is, one person does the work and another reviews the design (and/or built database) for accuracy and consistency. Review is important here because a poor database design may adversely impact entry and data cleaning, extraction or listing programs, and analysis. Something as simple as a missing field in the database can impact production timelines as CRFs stack up for paper studies or a new version of an EDC application must be released. Just as programmers on critical applications in other industries have software code review, database designs should always be reviewed by a second person. If the data- base builder is different from the designer, as is frequently the case for EDC studies, then that can constitute the design review. If the designer and builder are the same, an independent review is called for. Even the smallest of data management groups should be able to arrange for some level of review; after all, it is not good business practice to have only one person able to do a particular task. SOPs FOR DATABASE DESIGN Standard operating procedures (SOPs) on database creation or study startup typically include sections to guide database design and review and use of standards. If there is a separate SOP on design, it might discuss the use of standards and the process for requesting new kinds of database objects or deviations from the standards. All SOPs 36 Practical Guide to Clinical Data Management, Third Edition on study database creation must require as output some kind of document that will act as specification for, and documentation of, the database. For paper studies, this might be an annotated CRF; for EDC studies, it would be an appropriate design and configuration document or documents. RESPONSIBILITIES IN DATABASE DESIGN As clinical data management systems become both more sophisticated and at the same time easier to use, more data managers are becoming involved in the design and creation of databases and entry applications. This is both appropriate and effi- cient. Data managers know the typical data and are often aware of the problems associated with a particular collection method. With training in the application, a little background on the issues, and a good set of standards from which to build, database design or database building can be an interesting addition to the tasks of experienced data managers. Development of EDC systems is still largely performed by programmers, but because data managers are much more familiar with the characteristics of the data than a typical programmer (even if that programmer is part of the CDM organiza- tion), they are a critical component in the design of entry forms and underlying data- base objects for these systems. As we will see in coming chapters, they are likely to be involved in user acceptance testing for these systems if they are not, themselves, building the database. 37 4 Edit Checks Edit check is an old term for the data cleaning performed by clinical data manage- ment either via a program or manually by inspection. An edit was a change to data and the check was the way to figure out if a change was needed. Today, data manage- ment and clinical staff across the industry recognize edit check as a generic term for the ways we inspect data to identify discrepancies. While the term is generally recognized, at any given company, edit checks can have different names including: data validation, rules, and logic checks, which come from, or are associated with, the specific clinical data management or electronic data capture (EDC) software system being used to build study databases. In order to perform the same checks on all the data consistently throughout the course of the study, data management groups create a list of checks at the start of the study, often called an edit check specification. (The term data validation procedures is also common.) These specifications typically use a template built in a Microsoft Excel® spreadsheet with one row per check. The data manager uses the study data- base design (see Chapter 3) to identify the fields and create checks using the edit check template as a guide. Most edit checks will be run automatically by the clinical data management (CDM) or EDC system being used for the study. A few checks will be performed manually, and others will be run outside of the data management system. In this chapter, we look at how edit checks are chosen and specified. Chapter 8, “Cleaning Data,” describes how these edit checks are put into practice for a given study. (Author’s note: understanding the process of cleaning data can be helpful to understanding how edit checks are defined. Readers new to clinical data manage- ment should read Chapter 8 first as a background to this chapter.)", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "CHOOSING EDIT CHECKS", "definition": "After the database is fully defined (although perhaps not yet built), the data manager will go through every field and determine what assumptions are to be made about data values in that field. Some of these assumptions will be enforced by the database itself and do not have to be defined as an edit check. For example, fields defined as dates will automatically restrict values to valid calendar dates and coded fields will restrict responses to the values in the codelist for that field. Most other assumptions on the data such as valid ranges will be programmed to run within the database or EDC system. Those that are difficult to program within the limits of the system will be run outside the database or EDC system in programs such as SAS®. A few checks require medical knowledge or other human insight and will be performed manually. In deciding on edit checks for a given study, a clinical data manager can use these categories to help identify the necessary checks: 38 Practical Guide to Clinical Data Management, Third Edition •\t Missing values •\t Simple range checks •\t Logical inconsistencies •\t Cross-form or cross-page checks •\t Protocol violations", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Missing Values", "definition": "For fields where we always expect to have a value, we can impose a missing value check. That is, a discrepancy (see Chapter 8) will be raised if the field is left blank. For example, we always expect a value for visit date since that is a known piece of data for which source documents would exist. The data manager would either specify in the database design or as a separate edit check that a value for visit date cannot be missing. Care has to be taken when defining a required field at the database level rather than as a postentry check. If a field always requires a value at entry, it cannot be used for a field where there is a chance that the data is, in fact, not available. So for paper-based studies in particular, where data is entered from a case report forms (CRF), it is best to check for missing values after entry through an edit check.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Simple Range Checks", "definition": "Ranges are a very good way of checking numeric data to identify typos and to flag potentially incorrect values. An example of a range check for weight in kilograms is to flag data when the weight of an adult is outside the range of 45 kg to 200 kg. While it is possible for a person to be heavier or lighter, a double check with the site is appropriate. It should be noted that the ranges must not only be appropriate to real life but should also be sensitive to the patient population with the indication being studied. If a study had children as subjects, a lower range limit of 30 kg could raise too many queries for perfectly acceptable values. Dates can also have ranges but they are more typically relative to the subject’s course in the study and are discussed more in the following sections in logical and cross-form checks. As with the missing fields, some databases allow fields to have hardwired ranges associated with the database itself rather than as a postentry check. If that is the case, the database designer must take care to allow the ends of the ranges to be exceeded, overridden, if it is at all possible that the value really does fall outside. Softer ranges at entry to detect typos and tighter ranges programmed as edit checks are a better choice. Logical Inconsistencies This is a very broad area of edit checks that is the hardest for new data managers to apply. Logical inconsistencies are possible all through the data because there are assumptions behind most fields that go beyond whether a field can be blank and the value must be in a reasonable range, but it takes practice to be able to identify them. Here are several examples of logical assumptions representing various data types:", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Edit Checks", "definition": "Edit Check Specification, EDC tab", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "PROTOCOL VIOLATIONS", "definition": "Protocol violations may fall into any of the categories previously mentioned, but it is often convenient for data managers and study teams to address these checks as a separate category since they may require additional tracking or attention. Some examples of protocol violation edit checks that fall into the categories of edit checks we have looked at previously include: •\t Missing: a missing assessment of infection •\t Range: an age that is out of the protocol range •\t Logical inconsistency: a visit date that is too long after the previous visit, where the acceptable window is defined in the protocol 40 Practical Guide to Clinical Data Management, Third Edition •\t Logical inconsistency across forms: a blood sample drawn after a dose was given (when the protocol specified that the draw happen before dosing) An example of a protocol violation that might not be included in the typical cat- egories would be when a medicine is listed under concomitant medications, but is prohibited by the protocol. SPECIFYING EDIT CHECKS As the data manager identifies edit checks by reviewing assumptions on the data for the various categories described previously, each check is added to the edit check specification (or edit check spec). The edit check spec is used by the clinical data manager or clinical database programmer to add additional requirements to the data- base or to program the check in the system. The specifications typically take the form of tables or Excel spreadsheets with one row per check. Each check is identified by the CRF page or eCRF form and/or the module or grouping in which the value being checked appears. The data assumption being tested by the edit check is usu- ally described in a way that is understandable to all readers (that is, in English, not programming language). Associated with the check is a descriptive message that will appear when the check finds a discrepancy; this is what will go to the site as a query. See Figure 4.1 for an example of an edit check specification table. As noted above, most checks are automatic system checks, some are carried out by external programs or browsing tools, and some are based on manual review of data or listings. Many companies will list all of these types of edit checks in a single document, others will list only those that will be programmed in the database or EDC system in the edit check spec and create a separate document to document the manual medical and quality reviews that will take place. The latter document is often called a data review plan. QUALITY ASSURANCE OF EDIT CHECKS The edit check spec is a critical tool for ensuring data quality, so as with CRF design, the entire clinical team should be involved in its review. Each team member must review the checks identified by the data manager against the protocol and other study requirements. They should also look for checks that the data manager missed in draft- ing the specifications. The review by several functions aims to get the checks right before the study receives subject data. (Especially for EDC studies, changes after the system contains live data can be very resource and time intensive.) However, clini- cal teams are often put off by the many hundreds of edit checks that are typical for even very simple studies. The data manager can assist the team review by identifying standard unmodified, standard modified, and study specific checks.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "SOPs FOR EDIT CHECKS", "definition": "Every company should have the process for defining and building edit checks cov- ered in a standard operating procedure (SOP). The SOP might be the study database", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "DEMOG", "definition": "DOBD_MISSING Date of birth is missing or incomplete. The subject’s date of birth is missing or is incomplete. Please clarify.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Auto", "definition": "FIGURE 4.1  Example of an edit check specification table for some demographics information. In this example, the system allows for naming each check for ease of reference and copying. The “method” column is auto for system checks. 42 Practical Guide to Clinical Data Management, Third Edition specification and build SOP or an SOP specific to edit checks. An edit check spec, under any appropriate name, is considered an industry standard document crucial to producing quality data and should exist for every study. Key members of the clinical team including the clinical trial manager, biostatistician, and statistical programmer should review and approve the edit check spec along with the data manager. Many companies also require that the medical monitor review and approve the checks. THE CONNECTION TO QUERIES The edit checks in the edit check spec are programmed into the system. For paper- based studies, the checks will raise discrepancies when data is found that fails the check. All of these discrepancies will be reviewed by data management and many will be sent to the sites for resolution. For EDC systems, the checks will immediately raise queries to the site. There is a cost associated with all queries. Some are process- ing costs, which can be quite large for paper studies. For EDC studies, there is an “annoyance factor” for the sites. When specifying checks, make them count; avoid: •\t Ranges that are too tight •\t Raising multiple queries that are all due to a single field in error •\t Excessive checking of data that is not a primary or secondary endpoint When we specify edit checks, we also program a default query message; good practices for wording queries are discussed in Chapter 8. 43 5 Preparing to", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Receive Data", "definition": "Before data can be entered from a traditional paper case report form (CRF) into a database, that central database and the associated entry screens have to be created. Before data can be entered into an electronic data capture (EDC) system, that sys- tem must be built and deployed to the sites. In both cases, because the database is used to create electronic records subject to 21 CFR (Code of Federal Regulations) Part 11, the systems must be tested to demonstrate accurate storage and reliable behavior. In this chapter, we will look at the steps required to build the database, which has been specified according to guidelines in Chapters 2 and 3, and release it to receive data. OVERVIEW OF CREATING STUDY DATABASES In the case of paper, the sites can transcribe data from source documents onto the paper CRF as soon as it arrives. Paper is a good storage system and the values are reliably safe until they can be source document verified by a clinical research asso- ciate (CRA). Only then will the CRA send or transport the CRFs to the data man- agement group for entry into the database. That gives the database builders several weeks lag time before the database must be ready for entry and usually means that the study can start as soon as the CRF design is approved and sites are initiated. In the case of EDC, no entry by the sites can take place until the system is fully built, tested, and deployed to the sites. The sites could delay for a few days after a subject’s visit because values defined by the protocol will have been entered onto source docu- ments during the subject’s visit (see the “Introduction” in this volume), but the whole idea of using EDC is to get the data into the database so that it can be cleaned and assessed close to the subject’s visit. While both systems require a database to be built and tested as described in the following sections, for EDC systems, the timeline for the study must consider date of first patient in (a common milestone in clinical trials) and the date when the system will be deployed to the sites. While a CRF design and printing will quickly follow a final protocol, the build and test of a database will take from 8 to 16 weeks depend- ing on the complexity of the study and how many similar studies have already been built. For an EDC study, those weeks should come before the first patient enters the study. For Phase I studies on a tight timeline, this can present a serious challenge to the team. For paper studies, the sequence of building the database is roughly: •\t The protocol is finalized. 44 Practical Guide to Clinical Data Management, Third Edition •\t The CRF design is finalized, though it may have begun before the protocol was final. The CRF can be printed. •\t The CRF design is used to create a database design, typically in the form of an annotated CRF. •\t The database structures and entry screens are built and tested, after which data can be entered into the database. •\t The edit checks are specified after the database structures are defined, dur- ing or after the database build. •\t The edit checks are built and then tested before release for data entry. A database for a paper study can be released for production entry of data before the edit checks are built or after. Many traditional database systems permit the edit checks to be programmed and released individually or in small groups, which pro- vides even more flexibility in the build sequence. For example, the edit checks for data received in the first visit could be released first, while those that apply to data collected in the second visit are still being developed. For an EDC system, the sequence of building the database is as follows: •\t The protocol is finalized. •\t The electronic CRF (eCRF) design and the database are finalized together in a design document that contains both the screen layout and field charac- teristics. Again, this may begin before the protocol is final. •\t Once the database design is final or near final, the edit checks can be specified. •\t The eCRFs are built and the edit checks are programmed into those eCRF forms. •\t The combination of eCRFs and edit checks are thoroughly tested. •\t The entire system is made available to sites. There is no easy time buffer in the process for an EDC study in the way there is in a paper study. In many EDC systems, even adding edit checks after a study has data in it can be a significant undertaking because of the way the software makes the study accessible to the sites. And even in those few EDC systems that support adding edit checks with existing data more easily, it defeats the purpose of using EDC when data is not checked right at the time of entry. VALIDATING STUDY DATABASES Building a database and preparing a study for production entry of data is building an application. It may not look like programming, but using a software system to build or configure a database is just like using a high-level programming language, even if there is no if-then-else in sight. Because the study database will be used to create records of clinical data, and that data is the basis of decisions on the safety and efficacy of the treatment, and because the data may be used to support a sub- mission to the Food and Drug Administration (FDA), a database for a study falls under good clinical practice (GCP) guidelines and 21 CRF Part 11 requirements for Preparing to Receive Data 45 validation. The Society for Clinical Data Management (SCDM) also makes a point of this approach in the “Database Validation, Programming and Standards” chapter of the Good Clinical Data Management Practices Publication. As we will see in detail in Chapter 23, “System Validation,” validation is more than just testing. Roughly speaking, validation requires a plan, a specification of what is to be built, testing after it is built, and change control once it is in production use. The following steps describe how validation can work for a study database: •\t A standard operating procedure (SOP), along with associated forms and templates, acts as the validation plan. •\t The annotated CRF (paper) or database design document (EDC) is the specification. •\t The database is built, typically resulting in updates to the specifications. •\t The final database design and associated entry screens are tested. •\t A change control system is put into place as soon as production data is being entered. A STUDY VALIDATION PLAN If you don’t have a plan, you won’t know what is required to pronounce an applica- tion as validated and ready for use. For study setup in a clinical data management system, an SOP that describes the steps necessary to build, test, release, and main- tain a database application for a study can act as the validation plan for all studies. The validation plan in the form of an SOP will detail all the steps necessary to build and release a system and keep it in a validated state. For each step in validating a study, the SOP should also describe what kind of output is to be produced as evi- dence that the step was carried out and that output would be filed in the study file. The SOP might also have templates and forms associated with it. The templates can provide a place to document any study-specific testing requirements and steps or can summarize the testing process. The forms might include approval to release the study for production use. DATABASE SPECIFICATIONS Database specifications are the way the database design is communicated to the data- base builder. The approach to specifying the design is different for paper and EDC, but as we will see in the next section on testing, the specifications are critical to testing.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Paper Studies", "definition": "Chapter 3 introduced the concept of designing a database before building it. The design process results in a specification of the database to be built. For paper studies, specification is most typically an annotated CRF and may or may not also include a more detailed design document. In clinical data management, the annotated CRF is usually a blank CRF that has written on it (by hand or as an electronic overlay) the names of the database column, field, or item associated with each CRF field. The 46 Practical Guide to Clinical Data Management, Third Edition CRF page is also clearly marked to show how questions are grouped into modules or tables. Because the annotated CRF is used not only by the database designer but also by edit check writers, entry screen designers, and even those browsing data through database queries, it is helpful if the codelists associated with an item are present along with any hidden, internal, or derived fields associated with each module. The use of annotated CRFs for paper studies is widespread enough to be considered industry standard practice. A separate design document, while not required by all data management groups, can provide information that is not readily obvious from the annotated CRF. This document might include simple overview information such as the list of all group- ings or tables and the names of all codelists used. In companies where there are firm CRF page and database standards, the design document can focus on deviations from those standards (if any) and introduce any new objects created specifically for this study. It might also include a more detailed discussion of design decisions that were made for problematic fields or tables.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "EDC Studies", "definition": "There is less consistency across companies for what acts as a database design for EDC studies. The document can look like an entry screen (eCRF) or not, but it usu- ally includes detailed information about the database design and often about the structure of analysis datasets created from that design. After the database is built, EDC systems can generate a blank eCRF, analogous to a paper CRF, which can be used to train sites. The EDC system can also create an annotated CRF with data- base attributes printed on the fields. That an annotated CRF is considered a feature of EDC systems, demonstrates the value of the annotated CRF for many different activities related to any CDM system! How Building Impacts Specifications The database builder uses the specifications (annotated CRF for paper or other docu- ment for EDC) to create the database objects for a study. The building process itself acts as another form of database review. The builder may notice a problem with the design or may not be able to implement the object as specified. The builder and designer then work to come up with a solution, updating the specification documents appropriately. When the system is deemed ready to test, a version of the specifica- tion documents must match the built version so the testers know what the expected behavior is. TESTING STUDY DATABASES Validation always involves testing as one element of the process, and all study data- bases should be tested—without exception! A mistake in creation of the database or a poor design choice will impact data storage and possibly analysis. Testing aims to identify these errors before production data is stored in the application so that changes can be made without impacting live data. Just as with software system Preparing to Receive Data 47 testing, one has to take a practical approach and decide what kind and what amount of testing is most likely to identify problems, without taking up too many resources and too much time.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Testing Environment", "definition": "Typically, the builder will work in a development environment that mimics the actual production environment; that is, the software system and standard objects are the same but development work is taking place “on the side.” When the application is ready for testing, that testing may take place in the development environment, another area specifically set up for testing, or in appropriate circumstances, the pro- duction environment itself. If the study is tested outside of the production area, the move to the production environment must be a controlled and reliable one.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Testing Paper Studies", "definition": "Testing of a study database for a paper study most naturally takes place when the entry screens are ready and uses patient test data written on CRFs. Depending on the complexity of the study, data management groups will typically test from 2 to 10 subjects’ worth of data. If the goal is purely to test the entry screens, the test data will be mostly normal data with typical exceptions in date formats or text in numeric fields. Some companies use data that will later be used to test edit checks as test data, in which case many values will be chosen to later fail the edit checks. Ideally, data entry staff will perform the entry of the test data as if it were real. Any problems that they identify in the fields or in the flow of the screens should lead to corrections and an appropriate level of reentry of the test data. But the testing does not stop once data entry is flowing smoothly. After the data has been entered, the responsible data manager should run the process or programs to fill in any derived or calculated fields. Then, a tester should compare data extracted from the database against the original test CRFs as if this were a database audit. In addition to looking to see if the data values match, the tester is also checking: •\t Is the data stored in the correct field? •\t Are the calculated variables calculated and correct? •\t Are all hidden variables filled in as specified? •\t Has any data been truncated or otherwise improperly stored? •\t Are there unexpected blank records? •\t Are fields that should be carried over to multiple rows or groups prop- erly carried? There may also be additional checks that are related to the application used to capture the data. This step of comparing the extracted data to the original is often overlooked but should be considered the real evidence of a functioning study. Finding any of the problems previously discussed is a very serious situation and it may be impossible to correct once data is in production. 48 Practical Guide to Clinical Data Management, Third Edition", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Testing EDC Studies", "definition": "Testing for an EDC study would typically be even more thorough than for a paper study because the database is used directly by inexperienced users at the sites. Testing strategies vary; some companies perform extensive entry testing as if it were a paper study, others test the database design without entering data typical for a trial subject. When the specification of the extracted dataset is included in the database design, entering typical data and extracting it as described previously for a paper study should be considered an essential part of testing. Because edit checks are built into the entry screens for EDC studies, these must also be tested before the system is released for subject data. In many cases, testers will use the edit check specification as a guide (see Chapter 4) to enter data into a single field that passes the check and then some that fail the check. They also attempt to leave fields empty where data is required and check edge values on ranges. Given the many hundreds of edit checks for a typical study, this is a very time- and resource-intensive task. Many sponsors new to EDC contract with a contract research organization (CRO) or an EDC vendor to have a study built. Sponsors should expect the vendor or CRO to undertake extensive testing, and in addition, they should expect to be asked to par- ticipate in additional testing, often called user acceptance testing (UAT). The CRO or vendor uses UAT to ensure the sponsor sees the final, implemented eCRF. This is analogous to having the sponsor sign off on CRF design undertaken by a CRO. Some sponsors report that the study-build vendor expected much more extensive testing than they expected—more in line with the study validation testing just described. Both sponsors and vendors should be sure to make very clear in the contract or state- ment of work for the project what kinds of testing will be performed and which side will carry out that testing. Final Steps in Testing Throughout testing, someone, usually the data manager, keeps track of all issues or deviations from expected behavior. Some of the issues may be an error in the study build, some may be an error in the specification, and some may be due to differences in interpretation of expected behavior. The data manager, in consultation with appro- priate roles involved in building and testing, assesses each issue and determines an appropriate course of action. The study database may be corrected, the specification may be updated, or there may be a bug report for the software system itself. Before the system is put into production, all of the issues should have been addressed and the specification and the study database should match.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "MOVING TO PRODUCTION", "definition": "We know that validation is not just testing. Therefore, completing the testing does not mean that validation is complete. Most auditors expect an official statement that the study is ready to put into production. This step often comes after all the test- ing issues have resolved and a (brief) summary written. Some kind of approval of Preparing to Receive Data 49 the results and summary is appropriate. For databases supporting paper studies, approval is typically within CDM only. For EDC studies, approval may also include roles outside of CDM who were involved in testing, such as the clinical team lead. Since validation requires documentation of each step, the test data and results should be filed in the data management study file as evidence of the process. Training in the use of systems that generate electronic records is required by 21 CFR Part 11. For paper studies, training focuses on entry staff (who would pre- sumably have already had training on the application). This training is not a com- plex formal training on the application and company standards; rather, it focuses on study-specific issues. Typical preproduction entry training will include a discussion of difficult or nonstandard entry screens and a review of standard and study-specific data entry conventions or guidelines. Evidence of the training should be filed in the study file or in each employee’s training binder. Frequently, a CDM group will also require a record of signatures and initials in the study file for anyone who will work on the study; this is a good point at which to collect the initial set. It is a best practice for entry staff to be given access or permissions to the production study only after training. (For more on training, see Chapter 16; for more on security and accounts, see Chapter 17.) For EDC studies, the focus is on the sites as they will be doing data entry. They require training both to use the EDC software and on the study in question. Principal investigators also need to be trained on the electronic signatures that all EDC sys- tems feature to satisfy requirements of 21 CFR Part 11. (Paperwork documenting the principal investigator’s signature and understanding of the electronic signatures is also required by regulation.) As in paper systems, accounts to do work in the produc- tion database should only follow successful completion of training. In addition to training, it is quite common to have additional requirements that must be met before production use of a study application. These may include, for example: •\t Moving the study database into a production area •\t Setup of related tracking and reporting applications for the new study •\t Initiation of interactive voice response systems (IVRS) applications for", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "randomization", "definition": "•\t Activation of company-specific integrations When there are many steps involved in releasing a study for entry, it is always worth having a checklist so that no step is ever overlooked. Even if the checklist is not a controlled document or required as part of an SOP, it provides value to the peo- ple doing the work and improves quality because critical steps will not be missed. STUDY DATABASE CHANGE CONTROL During the course of carrying out a study, it is very likely that a change to the data- base will be needed. A protocol amendment may require the collection of additional data. Or, the change may be due to unexpected data coming in from the sites, such as texts longer than originally anticipated or perhaps texts showing up where only numeric values were expected. After having carefully validated the application, a 50 Practical Guide to Clinical Data Management, Third Edition change made willy-nilly can result in putting the database application in an unvali- dated state and, most importantly, brings into question the integrity of the data that already resides in the system. Once a system has been validated, it will only stay validated if no changes are made to the application. Larger software systems, such as the database systems themselves, are under change control once they have been validated (see Chapters 23 and 25). Changes are carefully tracked and appropriate testing and documentation is required. The same should apply to individual study databases. In the case of a data- base for a study, it is important to consider what a change to the database application is and what is not a change. Adding, deleting, or modifying patient data according to standard practices and under audit trail is not a change to the application and does require change control. In most systems, adding users and granting access (again, using appropriate procedures) is not a change to the system. Adding fields, lengthen- ing text fields, and modifying entry screens are all examples of changes that should be carried out under a change control process. A change control process can be quite simple. It requires that responsible staff members do the following: •\t Identify what the change is and how it will be made •\t Assess the impact of the change on the application, the data itself, and training •\t Get review or approval for the change prior to making it •\t Show evidence of appropriate testing after making the change If a sophisticated change control system is not available, this information can be recorded on paper and stored in a change control binder or it can be entered into a simple log file or spreadsheet. Many of the most common changes can be documented and carried out with a few sentences in the log or on the form. However, more com- plex changes that have several interlinked requirements or those that impact several groups would benefit from having a longer document, in addition to a simple change control log entry, to describe the change process and impact in detail. Some examples will help clarify the requirements. The case of lengthening a text field provides us with an example of a low-impact change. Existing data is unlikely to be affected. The database definition will change, but (depending on the system) the change may have no impact on entry screens. It is worth considering whether edit checks, listings, and so forth are affected by the greater text length, but if the answer is no, then a simple log entry with this information in the impact assessment field would be sufficient. Now consider the example of adding a field to the database because a protocol amendment requires that the sites now record the time a certain procedure is per- formed. Adding a field is an example of a change that might have broad impact. In addition to the actual database change, adding a field touches on: •\t The annotated CRF •\t The CRF or eCRF completion guidelines •\t One or more entry screens •\t Instructions on what to do if the old form comes in with no time field Preparing to Receive Data 51 •\t Edit specifications and the associated cleaning rules for the new data •\t Possibly data transfer programs •\t Possibly listings or other reports If this change is made in an EDC system, the change would impact all sites, so notification, if not training, would be required. This case shows us that if multiple areas or users are affected, or any impact can’t be described in one or two sentences, then a more detailed change plan is warranted. It is worth saying a few words about changes to EDC studies that come from a protocol amendment. The updated EDC study database should not be deployed to any site that has not yet received institutional review board (IRB) approval for the protocol amendment. For most studies, it will be possible to wait until all approv- als are obtained before deploying. For large, multinational studies with many sites, it may be a business need to deploy the changes to sites as they obtain approval. Some EDC systems support deployment of new versions of the study database on a site-by-site basis, but others do not. In those systems that do not, some changes can be supported by adding user programs and edit checks to permit entry to new fields when the site indicates IRB approval by filling in a special field. However, this technique usually does not work for removing fields. Deployment of any EDC change contingent on IRB approval requires close working with clinical operations. Deploying updates to sites incrementally is a very complex and resource-intensive task and requires even more coordination and planning. QUALITY ASSURANCE FOR BUILDING STUDIES Care in building and releasing a database can’t be stressed enough. Because even the best and most experienced designers and builders make mistakes, those respon- sible for setting up a database should implement a policy of do and review. Review of the specifications helps identify both system-related problems and protocol issues. Building the database from specifications is another form of review. Then, after building is complete, we have data entry and data comparison testers who are reviewing the data from the database build. In all of these steps, the review is not a policing action where the reviewer is looking to catch someone who has made a mistake; it is a collaborative effort to identify potential problems before they impact the conduct of the study. As we will see later, many groups find that errors are introduced not during initial entry of data but when values need to be edited later. Similarly, many companies have a solid process for releasing databases but introduce errors when they make changes. Putting into place a careful change control can improve quality of the pro- cess and the data. SOPs FOR PREPARING FOR DATA Preparing a study for data entry (sometimes called database go live) is a critical point in the conduct of a study and will appear as a key milestone in study time- lines. The business need for a solid process leading to a reliable database is high 52 Practical Guide to Clinical Data Management, Third Edition and the need to comply with 21 CFR Part 11 is essential, so the need for thorough and clear standard processes cannot be ignored. Auditors tend to focus on the two ends of a study—startup and lock—and will ask for these SOPs first and investigate compliance with those SOPs to assess the integrity of the database (data). The SOP for startup activities that release a study for production should cover the required elements for validating a study database as just described, but should also add in the other activities such as training and integration configuration that must be completed before a study goes live. The study setup SOP(s) may cover the process for making changes to a live database or this can be addressed in detail in an SOP of its own. When developing these SOPs, companies find that prerequisites or require- ments for moving from one phase in the procedure to another generate the most discussion. Is a final protocol required before the CRF or eCRF design can begin or is a protocol summary or draft enough? Can database specification begin based on a draft CRF? Can study build begin prior to getting approval for the database design? The answers to these questions often depend on the level of standard data elements available to CDM, but it is perhaps surprising that the answer can depend on specific technical behavior and requirements of the CDM or EDC sys- tem being used. STUDY CREATION IS PROGRAMMING In most of today’s EDC systems, it is still clear that study setup is a programming task. In the traditional data management systems, the setup process may not appear to be programming since it is performed through menus and forms, and yet it is programming. In fact, it is programming that has a huge impact on the data that is to be collected from a clinical trial. For that reason, every study database should be validated as an application that will affect safety and efficacy decisions for the treat- ment in question.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Study Conduct", "definition": "Once subject data begins to come in to the CRO, questions will arise. The sponsor’s data management liaison must be available to answer these questions. The CRO lead", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "TRANSCRIBING DATA", "definition": "Accurately transcribing data from the CRF to the database is essential. Errors in tran- scription are usually due to typographical errors (typos) or illegibility of the values on the CRF. Companies aim to reduce transcription errors using one of these methods: •\t Double data entry with third-party reconciliation of differences •\t Double data entry with a second person resolving differences •\t OCR as first entry with one or more subsequent entry or review passes •\t Single entry with extensive data checking Even after transcription errors are reduced to a minimum, there remains some variation in what an “accurate” transcription means. Does it mean that the data is an exact dupli- cate of the values found on the CRF, or are there deviations or variations permitted?", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Double Entry", "definition": "With an error rate often given as 0.1%–0.2%, double data entry has long been used as a reliable method of transcription. In double entry, one operator enters all the data in a first pass, and then an independent second operator enters the data again. Two dif- ferent techniques are used to identify differences and to resolve those differences. In one double entry method, the two entries are made by different entry operators and both values are stored. After both passes have been completed, a comparison program checks the entries and identifies any differences. Typically, a third-person reviews the report of differences and makes a decision as to whether there is a clear 56 Practical Guide to Clinical Data Management, Third Edition right answer (for example, because one entry had a typo) or whether a discrepancy must be registered because the data value is illegible or is in some other way unclear. This method of double entry is sometimes known as blind double entry since the operators have no knowledge of each other’s work. The other double entry method uses the second entry operator to resolve differ- ences. After first pass entry, the second entry operator selects a set of data and begins to reenter it. If the entry application detects a mismatch, it stops the second operator who decides, right then and there, what the correct value should be or registers a dis- crepancy to be resolved by a third person. This double entry method does not have a common name but will be referred to as heads-up second entry. Blind double entry is well suited to the use of temporary or untrained staff for both entry passes. A more experienced operator or coordinator acts as the third per- son reviewing only the differences identified by comparing the passes. Heads-up second entry works best if the second entry pass is performed by a more experienced operator, but many companies have seen good success with this method even when using temporary staff. If the entry application supports it, it would be worth consid- ering using different methods at different times or with different studies, depending on available staff. Extensive checks at entry time are rarely incorporated into entry applications when data is to be entered in two passes. A check at entry would only slow down the operators and would bring little value. Checks on the data are run after differences have been resolved.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "OCR Plus Review", "definition": "Optical character recognition (OCR) is a technique whereby software attempts to read text in a document. OCR has been used for many years to successfully read pre- printed header information. As the software has improved, it has also been used to identify handwritten numbers and marks in check boxes. However, success rates in reading handwritten free text are still rather poor. As more companies move toward imaging or fax-in of CRFs, the opportunity to use OCR has increased. When used, OCR becomes the first data entry pass on numbers and check boxes and is followed by at least one other pass to verify those numeric values and fill texts or other fields that could not be read by the OCR software. The second-pass operator (after OCR) visually checks values read by the OCR software and types in any text values that appear on the form. Sometimes, the OCR software will provide an indicator of how sure it is about reading each value to guide the operator to fields that need review. Because the visual verification is hard to enforce and because the operator may fill in a significant number of fields, there is a danger of introducing transcription errors. Companies generally address this by doing yet another pass and also by including extensive postentry checks.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Single Entry", "definition": "While relatively rare in traditional clinical data management, single entry is an option when there are strong supporting processes and technologies in place to Receiving Data on Paper 57 identify possible typos or errors because of unclear data. (Interestingly, electronic data capture [EDC] is a perfect example of single-pass entry; the site is the entry operator transcribing from source documents, and checks in the application imme- diately identify potential errors.) Single entry also shows up in data management applications built by small start-up companies wanting to do some data manage- ment in-house without making the commitment required for purchasing a vendor- supplied system. Single-pass entry could be considered as an option in a traditional data manage- ment system when there are extensive checking routines built into the data entry application and further checks that run after entry. The entry operator would have to be trained in the protocol and encouraged to view the entry process as a quality and review process rather than as a simple transcription process. To address the concerns that there is a higher error rate, companies using single pass entry could consider a more extensive audit of the data than would be used for double entry. HOW CLOSE A MATCH TO THE CRF? We have discussed methods to help assure accurate transcription of the data from the CRF—but what is accurate? Does accurate mean that the data in the database exactly matches that written into the CRF field? Are data entry operators given some leeway to substitute texts, make assumptions, or change the data? These questions must be clearly defined by each and every data management group for the entry staff. Some companies do ascribe to the entry philosophy that the data transcribed from the CRF must match the CRF to the highest possible degree. Their guidelines tell the entry operators: Type it as you see it; if it cannot be stored in the field, issue a discrepancy. One of the few exceptions at these companies would be if the CRF field contains symbols such as “↑“ for increasing. These are considered acceptable substitutions and are predefined and standard across studies. Some firms allow more flexibility in the transcription of text fields, and some also permit changes to values found in numeric or date fields. However, it should be noted that the current industry trend is away from any changes of site values at the time of entry. The trend is that except for symbols, data should be entered as seen or left blank; any necessary changes are made after entry during the cleaning process so that there is a record of the change in the audit trail of the database along with the reason for the change. That being said, there still may be some changes permitted at entry time in addi- tion to replacing symbols. Permitted changes to texts might include: •\t Using standard notations for common units (e.g., g for grams) •\t Abbreviating some texts to fit in fields (e.g., using subj to replace subject) •\t Correcting some misspellings in comments and reported terms An example of a permitted change to a numeric field would be to instruct data entry staff to enter the midpoint when a range of values is entered in a single field. That is, they enter 5 when the CRF reads 0–10. (See also the discussion of self- evident corrections found in Chapter 8, “Cleaning Data.”) 58 Practical Guide to Clinical Data Management, Third Edition An accurately transcribed value may be a wrong value. That is, the value written on the CRF may be obviously incorrect or simply missing. If checks at entry time have been built into the entry application, those checks should never prevent entry staff from entering what they see. DEALING WITH PROBLEM DATA No matter how well designed a CRF is, there will be occasional problems with val- ues in fields. The problems may be due to confusion about a particular question or they may be introduced by the person filling it out. The most common problem is illegibility; another is notations or comments in the margins. Sometimes preentry review of the CRFs can help manage these and other problems—but this can cause a whole different set of process problems in exchange. Because companies deal with these data problems in different ways, each data management group must specify the correct processing for each kind of problem in data entry guidelines. These guide- lines may apply to all studies or be study specific.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Illegible Fields", "definition": "Illegible writing on the CRF always causes problems for data entry, data man- agement staff, and clinical research associates (CRAs). Each data management group should consider the following questions when planning an approach to illegible fields: •\t Can entry operators discuss the value with each other? •\t How do entry operators indicate illegibility at second pass? •\t Leave the field blank? •\t Guess and flag the field? •\t Type special flagging characters (e.g., ###)? •\t Should data managers make educated guesses based on a review of other pages? •\t Can the CRA make a decision based on medical information or experience with the site? Even when staff tries to appropriately identify values, some data is just illegible and will have to be sent back to the site for clarification during the data cleaning process.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Notations in Margins", "definition": "Investigators will sometimes supply data that is not requested. This most frequently takes the form of comments running in the margins of a CRF page, but may also take the form of unrequested, repeated measurements written in between fields. Some database applications can support annotations or extra measurements that are not requested, but many cannot. If site annotations are not supported, data management together with the clinical team must decide what is to be done: Receiving Data on Paper 59 •\t Can the information be stored in the database as a comment or annotation (but then it could not be listed or analyzed)? •\t Can the comment be ignored? •\t Should the site be asked to remove the comment or transcribe it some- where appropriate? Many data management groups do not store unexpected information at all, but since it can contain medical information, senior data managers and/or CRAs will review the extra comments and measurements to look for important safety information.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Using Preentry Review", "definition": "In the past, many companies had an experienced data manager conduct a preentry review of all CRF pages. The reviewer wrote in code values for categorical fields (if they were not preprinted), dealt with problems in header information, replaced UNK with ND, tried to clarify texts that might be difficult to read, and so forth. The idea was to speed up entry by dealing with significant issues ahead of time. The problem is that extensive review and in-house annotation circumvents the independent double entry process and its proven value. Because entry staff enter whatever the reviewer writes, and do not consider whether it was appropriate or not, it becomes a one- person-decides process. Also, in cases where the reviewer’s notes changed the data or made assumptions, there was no audit trail of that change. Some companies today still do a preentry review, but it is minimal and focuses on issues that will prevent entry. More and more is being left to the entry operators. The entry staff is trained to identify and flag problem data appropriately for data manager review. This is a better use of staff resources because senior staff members work only on problems, not on normal data. It also encourages data to be entered as seen with any changes and reasons for change recorded later in the audit trail during an edit pass. CHANGING DATA AFTER ENTRY Initial entry is not the only entry task performed or overseen by data management. Following initial entry, there are edits or corrections to the data. The corrections may have been identified internally in data management, by the CRA during a site visit, or through an external query to the site (see Chapter 8). Just as there is a pro- cess for entry, there should be a well-defined process for making these corrections to the data. The industry has found that errors are often introduced when changes are made because most data management systems do not support a second-pass (verification) step on edits to data. Clearly, rerunning cleaning checks on these new values is essential and those checks may identify some typos or other kinds of errors. Many companies have also instituted a visual review of any data changes by viewing the data in the entry screen, receiving a report, or through the audit trail. Again, if the system does not support it, data management groups must put into place a process that ensures the visual review takes place to verify that changes were made correctly. 60 Practical Guide to Clinical Data Management, Third Edition Any changes after initial entry, made by any person, must be recorded in an audit trail. The Food and Drug Administration (FDA) requires audit trails to record changes made to clinical data (see 21 CFR [Code of Federal Regulations] Part 11), and it should be possible to view this audit trail at any time. There are, however, dif- ferences in how the term audit trial is interpreted and implemented. Data manage- ment systems vary as to when, or at what state of the data, audit trials are turned on, and several leave it to the data management group to decide. The central issue is how to identify the point when data entry is complete. Some companies turn on the audit trial for changes after first entry, others turn it on after the second pass is complete, and still others do not turn it on until postentry cleaning routines have run the first time since any of those could be validly considered the endpoint of initial entry. QUALITY ASSURANCE AND QUALITY CONTROL FOR ENTRY Quality assurance (QA) is a process, and quality control (QC) is a check of the pro- cess. QA for data entry builds on good standards and procedures and appropriately configured data entry applications. The approach that assures quality data entry is documented in the data management plan and is supported by standard operating procedures (SOPs) and entry guidelines. QC for data entry is usually a check of the accuracy of the entry performed by auditing the data stored in the central database against the CRF. This is usually referred to as a database audit and is an industry standard for paper-based studies. Data management staff carries out database audits to fulfill the QC function. Ideally, the auditors are not people who participated in data entry for that study. (External quality assurance groups at some companies perform this task to ensure independence of review.) They identify the CRFs to be used, pull the appropriate copies and associated query forms, and compare those values against the ones stored in the central database. The result of the audit is usually given as a number of errors against the number of fields on the CRF or in the database. To conduct an audit, there must be a plan for the audit that includes: •\t What data will be sampled •\t A definition of an acceptable error rate •\t A plan for what to do if the error rate is unacceptable If the plan is consistent across studies, it can be defined in an SOP. If the plan is study specific, it can be laid out in the data management plan or in a separate audit plan document. After the audit, a summary should document the final count of fields, total number of errors, error rate, and any action taken.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Audit Plan", "definition": "The number most frequently used in selecting the data to sample for an audit is 10%. This is often supplemented by a 100% audit of safety fields such as those for adverse events. Some companies also audit 100% of a selection of key efficacy fields and/or primary and secondary endpoints. It is very important to note that a 10% audit still Receiving Data on Paper 61 does not tell us exactly what was or will be audited. Is this 10% of the subjects, full CRFs, pages, or data? Ten percent of the subjects may be easy to select, but does not guarantee good coverage of investigator sites. Small studies also require more coverage so when the number (N) of subjects is small, the sample taken is the square root of N minus one. Ten percent of CRF pages, chosen randomly across all subjects, provides an excellent sample but is hard to audit when the CDM system does not retain the link from page to dataset. Similarly, ten percent of data from a dataset also provides a good sample but it can be difficult to pull the pages if the system does not retain the link. Many companies say that their acceptable error rate is 1%–5%. This is, however, much too forgiving an error. For data as well controlled as clinical data, a wide vari- ety of journal articles say that errors should be limited to 10–50 per 10,000 fields. This translates into 0.1%–0.5%. This latter figure also is in line with numbers for high-quality double entry in any industry and should be a good and reasonable target for most organizations.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Audit Process", "definition": "To perform the audit, a data manager or programmer takes the sample size and method (by page, by subject, etc.) and randomly selects the data to audit. That data will have to be listed in a report of some kind for use by the auditor. Ideally, the list- ing will present the data in such a way as to make comparison to the CRFs as easy for the auditor as possible, as opposed to whatever is easiest for the programmer in order to keep auditing errors to a minimum. The auditor will take the listing and compare it to the CRF data, marking any differences that are not accounted for by data entry instructions. These differences are not yet errors! It is possible that these differences come from corrections to the database made during the cleaning process as the result of a self-evident correction or response to a query (see Chapter 8). The auditor then looks at any evidence from cleaning, such as query forms associated with the page, and determines if these can account for the differences. If any discrepancies between the database and the listing remain, the auditor must ask one final question before declaring them to be discrepancies: Have these differences been introduced by errors in the programming that created the listing? After eliminating this final source of differences, those that remain are documented by the auditor as true entry errors. When the auditor has worked through the entire data sample, the information goes back to the data manager who reviews the errors and the differences marked as not-errors. This should give the data manager a sense of where the errors are occur- ring. The data manager then divides the total number by the total number of fields in the sample to give the error rate. One important caution here: calculating the total number of fields is not simply multiplying rows by columns in a database listing. For a typical study, that method would overestimate the number of fields entered rather than those that appear in the listing and produce a better error rate than is warranted. During entry, the subject identifier would be entered only once per page or in some systems only once per subject, but in a typical listing the subject identifier would appear on each row of data. Also to consider are the fields that are prepopulated by 62 Practical Guide to Clinical Data Management, Third Edition the system because they are preprinted on the CRF and prefilled in the entry screens to provide them in the data for analysis (e.g., time points for taking vital signs). Once these computer-supplied fields are all taken into account, we have arrived at a more accurate picture of data entry errors for a given study.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Audit Report", "definition": "The audit report is essentially a cover memo to the actual audit listings. It lists the number of errors, the number of fields counted, the final error rate, and any action taken. The package consisting of the audit plan (if not an SOP), audit listings, and audit report are filed in the study files to show evidence of the quality of the data. One tenet of quality assurance and quality control is feedback. When errors are found, they should be used to improve the process. Even when the error rate is acceptable, it may be possible to detect some identifiable source or sources of errors. Retraining of data entry staff or data managers is always a possibility. It is also com- mon to provide feedback to CRF and database designers. What is to be done if the rate as a result of the audit is unacceptable? If the audit is early in the data management process, it may well be possible to correct or adjust the process or systems to remove some of the sources of error. If the audit is performed at the end of the study, it would be advisable to increase the number of fields audited to confirm the rate. Some companies immediately move toward performing a 100% audit of all of the data when the error rate is exceeded. Other companies perform another 10% sample. Still, others examine the result first and try to determine if any particular type of data or specific data modules are the source of the problem and then conduct a 100% audit of just that data. Because audits are very resource and time intensive and because identifying the cause of errors has a high value, spend- ing time analyzing the audit results instead of just reporting the numbers can have a major impact on data entry efficiency.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "SOPs FOR DATA ENTRY", "definition": "At companies with a very consistent entry process, the process itself may be laid out in an SOP. For example, a data entry SOP may always require blind double entry with third-party arbitration of discrepancies. It may also indicate the level of preen- try review and outline the audit process. At companies with variations in data entry across groups or studies, the SOP may only state a commitment to accuracy and indicate that study-specific guidelines are to be followed and methods documented at the study level. The data management plan is a good place to identify any study- specific exceptions or changes to the standard procedures.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "ENTRY QUALITY", "definition": "As the saying goes, garbage in, garbage out. Data in the database is only as good as the collection method, so when data entry is used, it should be considered an essential step in assuring quality. Quality comes from using people and technology appropri- ately and efficiently. When using inexperienced or temporary help, encourage them Receiving Data on Paper 63 to think quality rather than speed. They are the first line of defense on identifying problems with the CRFs, the sites, the entry application, and sometimes the central database. When using technology, have it do what it is good at—this usually means checking the data and may also include initial entry through OCR. In general, don’t duplicate manual tasks with system tasks; find a way to make better use of both and apply the resources where they provide the most benefit. 65 7 Overseeing Data", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Collection", "definition": "The goal of the data collection process is to collect all the data from the sites in a timely manner. For electronic data capture (EDC) studies, the process is simple and requires only that we know what subjects have been enrolled. The process for paper- based studies centers on tracking case report form (CRF) pages. In both cases, a close partnership with clinical operations, and the clinical research associates (CRAs) in particular, is essential. MONITORING EDC DATA COLLECTION All EDC software systems have built-in reports on the number of subjects enrolled and the number of EDC electronic CRFs (eCRFs) completed for each site. The one area in which problems can arise is that the EDC system only knows about subjects that have been enrolled through the software. If the site has not yet performed that step, the system has no way of identifying subjects who have been seen but for whom data has not yet been entered. If the subjects are enrolled through an interactive voice response system (IVRS, or IxRS if it is not purely voice driven), a software integra- tion may be able to automatically load all enrolled subjects into the EDC system so that an accurate status is always available. When IVRS is not used or an integration with the EDC is not possible, the status relies on the sites and monitors to ensure that all subjects are made known to the system in a timely way—not that this is much different from what is done with paper trials. Historically, it has been a data management responsibility to report on the status of the data to the clinical team and to provide details to each CRA. With built-in reports available in the EDC system, each CRA can get a report at any time without going through data management, and the clinical project team can also obtain a cross-site summary at any time. The CRAs will be comfortable with the EDC appli- cation (and have access to see the data) because they use it to monitor. The clinical team lead should also have an account to view site and subject status. MONITORING PAPER DATA COLLECTION When data is received on paper CRFs, it becomes critical to ensure that all the CRF pages make it from the sites to the data entry group, and then that all the data makes it from the CRF pages into the database. Today, many companies use imaging sys- tems to scan paper on arrival (or scan after processing), and while this provides a reliable backup to the paper page and makes pages available for entry down the hall or across the globe, it adds yet another place where a page could be misplaced. 66 Practical Guide to Clinical Data Management, Third Edition Tracking can be performed successfully entirely on paper and by hand. But the most useful tracking systems are those integrated with the data management system. The benefits of a good tracking system are surprisingly high and can result in a considerable reduction in the time spent on annoying administrative tasks associated with shuffling paper. The primary goal of any tracking method is to assure that data is not lost, and this goal must be met.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Paper CRF Workflow", "definition": "Do you know where your pages are? Tracking a CRF—paper or image—involves knowing where the page is in the data management process. The path the pages follow and the stops they make (or states they are in) is often known as the work- flow. Some workflow states common to most clinical data management processes for CRFs are: •\t Received in-house •\t Entry—first pass or initial OCR •\t Entry—second pass •\t Completed and filed Some systems may also be able to identify if a page has queries outstanding (for more on this topic, see Chapter 8, “Cleaning Data”). In manual tracking systems or simple computerized systems (using a Microsoft Excel® spreadsheet, for example), when a page goes to a new stop in the workflow, the person handling the page updates its state manually. That is, when the page is received, someone adds it onto a paper or electronic log, and when the first entry operator receives it, that person updates the log again. More sophisticated or inte- grated systems can update more—generally not all—of the states automatically. For example, a more integrated system may require that a page be logged in manually with a state of received, but it automatically updates the page to a state of entered by detecting data records in the database and then later changes the state to second-entry when the entry operator completes the second pass. Balancing the desire to know the exact status of a page with the effort to determine that status is a challenge to all tracking systems. If a group needs to know that a page has been received independent of whether it has been entered (and imaging is not being used), then a manual update of state is necessary. Another group may feel that first entry is sufficient evidence of whether a page has been received and may choose to avoid the initial manual logging step altogether. While updating one or two states manually is not uncommon and is readily accepted by data managers, manual updat- ing of several page states can present a large burden to the staff and may result in a delay in having the information available or even a serious backup in the workflow. The ability of a computer system to update a page’s state automatically based on the data requires that the system know exactly which page the data came from— demographic data is from page 1, but are the vital signs values from page 6 or page 32 or both? Some data management systems where the database design is heavily page based (see Chapter 3) can closely link and store the data as a page throughout Overseeing Data Collection 67 the data management process. In other systems, the data is stored in datasets or tables independent of the page layout (though related, of course) to aid in standard- ization and pooling of data across studies (e.g., all the vital signs data from any page is stored in a single table). In those systems, the link to the paper page may need to be stored with the data by entering the page number along with subject identifying information or through the use of a unique document identifier stored with the data. The use of a document identifier is just a simplification of the entry of the subject ID and page: the unique document ID is associated with subject identifying infor- mation and a specific page number when the page is first received. This document ID alone is then used to link data to the original page. Because of its simplicity, this method is used even when the page header information is also stored with the data. Note that the document ID is usually a number or barcode and that it could have information such as page and protocol number encoded in it, but a simple sequential number, unique throughout the study, associated with page information on receipt works just as well and provides flexibility in supplying replacement pages.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Tracking Challenges", "definition": "Tracking systems and their associated processes need to be able to manage a set of typical situations that arise with CRFs: •\t Repeating pages with the same page number •\t Pages with no data •\t Duplicate pages •\t Pages with no page number", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Repeating Pages", "definition": "In Chapter 2 we learned that whenever we don’t know how many pages will be needed for a particular kind of data, repeating pages are used. These are pages such as those for concomitant medications or adverse events where the sponsor may not know how many copies are required to capture all the information obtained from each subject. Sometimes the CRF designer will guess an upper limit and repeat those pages—each with its own page number (e.g., pages 140, 141, and 142 may all be adverse event [AE] pages). A more common design is to provide several identi- cal copies of the page with the same page number in the CRF binder and provide a blank repeat or sequence field for the page. The site fills in the blank with a number or letter such as: 140.0, 140.1, 140.2", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "or", "definition": "140, 140a, 140b The database design associated with page number fields often divides the page number into two fields: one that is the base page and another that is a sequence number, as in page = 140, sequence = 2. Whether it is one field or two, a CRF page 68 Practical Guide to Clinical Data Management, Third Edition tracking system would have to handle a base page 140 with multiple occurrences for a single subject. Given the goal of entering all of the data for all subjects, some companies prefer to have an indicator on these pages to mark the last page. The CRA or site will mark a last page box when collecting the final of a repeating set of CRF pages for a subject or closing down the site. Other companies trust the monitoring to ensure that all the data is collected. Note that asking sites to provide page   of   does not work well because it does not permit filled pages to be collected and entered while the study is ongoing since the site would not know the total number of pages at the time the first page is complete.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Pages with No Data", "definition": "Some pages in a CRF booklet will not have data to enter into the database. Some, such as pages designed to hold copies of specimen labels, are designed to have no data, but the sponsor does expect to receive them as part of the study. In other cases, a subject may miss an entire visit or the study design may call for alternate pages to be used in specific circumstances. In those examples, the site is frequently instructed to send in the empty pages or not-applicable pages with a line drawn through them and “not done” or “no data” written on them. If pages are being tracked through initial data entry, repeated double-checking of why data from those pages is missing (because it has no data) can be avoided by tracking the pages as no data pages when they are received or when they arrive at first entry. (This would require a database field or cross-check with the tracking system.)", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Duplicate Pages", "definition": "No matter how well designed the CRF or how carefully monitors instruct the sites, sites seem to find ways to send in duplicate pages for a subject. Some actual examples help illustrate what can happen: •\t The site notices they filled out a form incorrectly, but it was already col- lected. So they fill out a new one transcribing much of the data but correct- ing some of it and then send that in. •\t The site sends in a page marked as having no data and realizes they made a mistake, but the page has a line through it, so they fill out a new one. The monitor collects both. Ideally, the tracking system will identify duplicate (nonrepeating) pages when they are logged in. When problems are caught early in the process, they are usually much easier to fix. Unfortunately, not all tracking systems catch duplicates early and so it falls to data management and/or statistical reports to identify the problem further down the road. Once duplicate pages are identified, it is usually necessary to remove one of them from the system or correctly sequence them as versions. Studies without Page Numbers Historically, not all studies had page numbers on all the CRF pages. Some of these studies did not follow the typical structure where visits happen in a predefined Overseeing Data Collection 69 sequence ending with a termination page. Studies with repeated applications of a treatment or dosing cycles (possibly by the subject at home) are one example. In other cases, the CRF pages were not numbered to reduce the number of unique page templates needed for printing, which used to reduce the printing cost significantly. In some of those studies, a page template name identified the kind of page (e.g., Demog page, PE page, Lab page, and so forth) and the pages repeated in visits or cycles. In others, a unique document identifier was deemed sufficient to uniquely identify and track the page. With advances in printing and because of the convenience of page numbers, we see this much less frequently. In fact, most tracking systems require a page number. Should a CRF design arise without the normal page numbers, consider carefully how (if at all) CRFs can be tracked with existing tracking methods.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Missing Pages Reports", "definition": "Missing pages reports are an essential part of tracking for paper studies. There are two kinds of missing pages reports and they have separate goals. Data management groups have long used the first kind of report, which checks a list of pages against the database data to ensure that all pages that have been received have been entered into the database. These reports only make sense if there is a list somewhere (paper or electronic) of pages that have been received by the group. When tracking is only done at the time of first entry, a reconciliation of pages received to pages entered is not possible and other procedures must be in place to insure that no data was inad- vertently overlooked. The second kind of missing pages report lists pages that are expected but missing by subject and site. This information is used by CRAs and monitors to determine whether there are pages at a site that should be collected at an upcoming visit or pages that were missed during a previous visit. For companies that use paper CRFs, data management is usually responsible for providing this information to the clinical group. Ideally, the programmers creating these reports will use existing tracking and database objects and data so that data management need not duplicate information into yet another table and so that the report program need not be customized for every study. In any case, the program will only be able to report missing pages if the list of expected pages is clearly defined somewhere. What Pages Do You Expect? When setting up a missing pages report, nearly every group starts by writing a ver- sion that lists all the pages of the CRF for each subject for the entire study period as expected. Right at the start of the study, the report will list nearly all of the pages as expected or missing. Site monitors and CRAs stop using these reports almost imme- diately because they are overreporting and it is difficult to find what is truly missing among the pages that are actually not really missing yet. This kind of report only has value near the end of a study, which is late for detecting missing pages and data! What we expect in such a report is for it to only show pages missing through the last known visit for a given subject. That is, if there are any pages logged in for a subject in visit 2, then the report expects all required pages through visit 2 but 70 Practical Guide to Clinical Data Management, Third Edition does not expect any pages for visit 3. This way, the monitor will easily notice if the physical exam page from the first visit is missing. Taking this approach another step, the program could be configured to expect visit 3 two weeks after the visit 2 date (plus a buffer to permit monitoring). Another problem in overreporting occurs when a subject terminates from the study early. In this case, the report should only list required pages up through the last visit plus any termination pages. Pages from visits beyond the termination visit are not typically collected and should not be con- sidered missing. If there is a way to identify a termination page or termination visit, then the reporting program can look for that page and apply the appropriate logic for considering a page to be missing. Alternate visits pose a huge problem for missing page reports. Some study designs have the subject follow an alternate visit if certain conditions are met. For example, consider the case of a protocol that says if certain test results come up positive, the subject follows Alternate Visit 1 rather than the normal Day 15 and then goes to Alternate Visit Follow-up instead of Day 30, after which the subject resumes the normal schedule. In this case, to avoid writing custom missing pages reports for this study, consider having the program require all pages for an alternate visit if one of the pages in that visit shows up. Also instruct the site to send in the Day 15 pages with a line through the pages and marked as “not applicable” or “no data.” Even though this is a bit of extra work for the site and monitor, the missing pages report will run appropriately because the required Day 15 pages are there and all of the alternate pages are accounted for. With a little care, no visit or page will be inadver- tently overlooked. That assurance justifies the extra work. The more advanced and useful versions of missing page reports previously dis- cussed described required visit information. This implies that the tracking system is configured with the visit information manually or the program brings it in from other sources. It is also worth pointing out that just listing page numbers on these reports is not as helpful as including a brief text describing what kind of page it is. The information that subject 1001001 is missing page 23, the vital signs page from visit 2 is more helpful to the site monitor than just reporting that subject 1001001 is missing page 23. CROs and Tracking Pages Contract research organizations (CROs) frequently have better tracking systems than sponsor firms—because they need them. CROs not only need to track paper CRFs during a study to avoid loss of pages, they must also be prepared to transfer all the pages with associated query forms to the sponsor at the end of the trial (see Chapter 18). In addition, they need the detailed metrics on the data management process for billing and future proposals that tracking provides. When CROs perform data man- agement, the sponsor can expect to receive regular reports on data collection status and missing page reports from the CRO’s data management group. One other circumstance puts a spotlight on CRO page tracking: when CRFs are transferred between the sponsor and the CRO during the course of the study. There are cases where the CRO either receives CRFs from the sponsor or receives the CRFs from the sites but sends them to the sponsor during the course of the study rather Overseeing Data Collection 71 than after study lock, which is the more common case. Either of these cases calls for tracking on both sides. Periodic reconciliation between pages received on both ends will help assure that no CRFs or query forms were lost in transit. PRINCIPAL INVESTIGATOR SIGNATURES Good clinical practice (GCP) requires that the designated principal investigator (PI) be aware of the data for a subject in a clinical trial and Section 8.3.14 of ICH E6 GCP explicitly calls for “signed, dated, and completed case report forms (CRFs).” One of the aspects of overseeing data collection is monitoring PI signatures for paper CRFs and eCRFs. For paper studies, that task falls more on the monitors at the sites and for clinical data management (CDM) it is purely administrative; for EDC systems, data management and CRAs must work together because the PI signature is implemented as part of the system. Even though regulations require a PI signature, they do not actually say that the PI must sign each page. The industry standard for paper trials is to collect the signature on a specific page in the CRF booklet once for the entire booklet along with the date in the expectation that the signature comes at the end of the trial or end of a given subject’s participation. (Corrections are signed by the PI on query forms; see Chapter 8.) The signature is monitored by the CRA but typically not stored in the database, though some companies will send the signature page to data entry and have a data- base field for entry staff to mark if the signature is indeed present. In EDC trials, the software implements an electronic signature, which has to meet the requirements of 21 CFR (Code of Federal Regulations) Part 11, the rule on electronic records and electronic signatures. As with paper trials, the most com- mon approach is to have the PI sign electronically at the end of the trial or end of a given subject’s participation. In EDC systems, updates to the data after it has been signed for will break (invalidate or remove) the signature—so unlike paper, if the data changes, the PI must re-sign. Some companies assign monitoring of the PI signatures to clinical staff, typically the site CRA, and others assign it to data man- agement. The EDC software should have reports to monitor PI signature status but because it can break when a site changes data, knowing when and how often to check signature status becomes an important skill. USING TRACKING FOR QUALITY ASSURANCE", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "AND QUALITY CONTROL", "definition": "One measure of quality for a study database is that the data is complete. At a mini- mum this means that for a paper study, evidence exists that paper CRFs and query forms have been accurately transcribed. For EDC studies, it means that checks are in place to show that the sites have provided data in all the required forms. While overseeing of data collection takes place throughout the course of the study, most companies also require final completeness checks prior to study lock. (For further discussion of study lock, see Chapter 13.) 72 Practical Guide to Clinical Data Management, Third Edition Tracking also can be used to assist in the quality control audits of CRF data against database values (see Chapter 6, “Receiving Data on Paper”). A good tracking system can easily produce lists of pages using various criteria such as: •\t Random selection across all pages totaling 10% •\t Selection of 10% from each investigator site •\t All adverse event pages •\t All pages 14, 21, and 35 The lists of pages may be used to call up scanned images of the CRF using a doc- ument ID or they may include investigator and subject numbers for quick retrieval from a paper filing system. A company might even opt to update a tracking system field for those pages that were included in an audit of data. SOPs FOR OVERSEEING DATA COLLECTION For EDC studies, no special mention of tracking in an SOP is required other than the final checks mentioned previously to demonstrate that the data is complete prior to lock. The situation is quite different for paper studies where an SOP should require CRF tracking, outline minimum standards for tracking, and call for reconciliation or reports to show that entry is complete at the end of the study. An SOP on workflow for paper studies or on data entry is usually the place these requirements are found. TRACKING THROUGHOUT THE PROCESS Unfortunately, in classic CDM database systems that support paper studies, tracking is often an add-on application. This is a shame, since a good CRF tracking system can support and make more efficient the entire data management process. A close integration with the underlying or supporting data management system makes more automatic updates of page states possible and supports data cleaning efforts. Further integration with discrepancy management can tie the whole process together by pro- viding data managers with easy access to and information on pages, data, and dis- crepancies interchangeably. Even some of today’s most used EDC systems lack a full compliment of reports so that companies end up writing custom software within and sometimes outside of the system to provide the various views of subject status that allow data managers to oversee data collection for the study. 73 8 Cleaning Data Even when data is accurately transcribed from source documents and carefully moni- tored, there will be errors and inconsistencies that require research and resolution. Discrepancies may be identified when someone reviews either the case report forms (CRF) or electronic CRF (eCRF) or the data in listing or graph form, but most com- monly, discrepancies in the data are identified by the data management system auto- matically at entry or after entry via rules defining acceptable data. (The rules are called edit checks; see Chapter 4.) Reporting or analysis in systems external to the data management application may also turn up inconsistencies and discrepancies. In paper-based studies, data management staff may be able to resolve some of these dis- crepancies by referring back to the original CRF or by viewing the subject’s data as a whole. Any discrepancies that must be sent to the site for resolution are known as que- ries. In electronic data capture (EDC) systems, most discrepancies immediately show up for the site to resolve and so automatically become queries. As a consequence, data managers using EDC often use the terms discrepancy and query interchangeably. As we will see next, the distinction is most important in the case of paper studies. The discrepancy or query that is generated automatically or created manually is stored in the data management or EDC system permanently as a record of database cleaning and site responses. System software will track the status of each discrep- ancy and query until it is resolved. (This is in addition to the audit trail of the data that all 21 CFR [Code of Federal Regulations] Part 11–compliant systems support.) The workflow of identifying a discrepancy, generating a query, and obtaining a final resolution varies somewhat across companies because of restrictions placed by the software system being used and also because different companies have different phi- losophies on query resolution and different resources to apply to the process. After a general discussion of how discrepancies are identified, we will look at data cleaning for EDC and paper studies. The EDC discussion is quite short compared to the paper-based discussion as simplification of query processing and management is one of the biggest benefits of using EDC to collect data. For both paper and EDC we will also consider the most common variations in the workflow or responsibilities for discrepancy and query management. IDENTIFYING DISCREPANCIES Automated checks run by the clinical data management (CDM) software systems identify most of the issues or errors that require further research, and these are built into the system. Outside of the clinical data management system or EDC system, errors can be identified by members of various groups looking at the data through various different tools. These usually have to be entered manually into the system so they can be tracked, sent to the site, and resolved. 74 Practical Guide to Clinical Data Management, Third Edition", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Automatic Checks", "definition": "Automatic checks to identify discrepancies are frequently called edit checks. In order to perform the same checks on all the data consistently throughout the course of the study, data management groups create a list of checks at the start of the study, often called an edit check specification. An edit check specification lists all the checks that will be programmed into the system and/or entry forms. (See Chapter 4 for further discussion of edit check specifications and an example.) Data manage- ment groups strive to program most edit checks because they are more reliable if they are automatic and the discrepancy is automatically logged or registered in the system. Of the automatic checks for a given protocol, perhaps 80% will fall into missing values, simple range check, and inconsistency detection categories, which can often be defined (programmed) by experienced data managers. The remaining checks would be considered more complex, and if they cross subjects, datasets, or pages, a programmer experienced with the underlying database and database programming language may need to define them. (See Chapter 4 for additional information on specifying automatic edit checks and Chapter 5 for programming and testing edit checks.)", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Manual Queries", "definition": "Some manual review of subject data is considered standard practice for clinical tri- als. When review is performed manually it should be because the check is especially complex and requires human intelligence or programming externally. It should never duplicate automatic checks! At some companies, the kinds of manual review to be performed are added to the edit check specification. At other companies, there is a separate data review plan. These checks or reviews are usually small in number com- pared with the automatic edit checks but are often very important checks for medical review, data quality assessment, and compliance issues. Whatever the source of the manual discrepancy, it must be registered in the sys- tem so it can be tracked to resolution. In order to enforce careful checking and con- sistency, companies typically have data managers create the actual discrepancies no matter who identifies them. The data management group must be very careful to review all existing discrepancies associated with the same data before creating a manual discrepancy and sending it to the site as a query. Sites find it very annoying when they get two queries that ask them the same thing—as might happen if two different people saw the same problem during different kinds of review or if a data manager did not review current discrepancies. Clinical and Listing Review One kind of manual review that does have high value and is very common practice is often called clinical review. Reviewers will cross-check adverse events against medications, check indicators of health being collected, and generally look for inconsistencies. A review of all text fields is a common part of medical review or is carried out separately. Data managers would not typically perform clinical or medi- cal review unless they have a medical background or training.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "CLEANING DATA", "definition": "1.\tUse of edit checks 2.\tFor paper: data management self-evident corrections (SECs) 3.\tManual cleaning or review of data 4.\tClinical review of data (if appropriate) 5.\tFor paper studies: flow of queries including tracking 6.\tFor EDC studies: process for reviewing and closing queries Associated document(s): Edit check spec, self-evident corrections (SEC, paper only) ELECTRONIC NON-CRF DATA 1.\tAll sources of electronic data (e.g., interactive voice response (IVR) data, central labs, electronic patient diaries) 2.\tFrequency of load and format Appendix A: Data Management Plan Outline 237 3.\tProcess steps, output: log of loads Associated document(s): File loading and/or transfer specifications; log of", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "THE EDC QUERY PROCESS", "definition": "As mentioned in the introduction to this chapter, discrepancies in EDC systems typi- cally appear automatically at the site as queries so that the distinction between dis- crepancies and queries is less important than for the paper process described later in this chapter. When system edit checks trigger, the discrepancy or query appears at the site when the data is saved, though some systems allow for a workflow to be set that can send the discrepancy to the clinical research associate (CRA) for review 76 Practical Guide to Clinical Data Management, Third Edition first. Queries created manually also appear immediately at the site when the data manager or CRA creating them sets the status appropriately. Most EDC systems allow sites to see queries either in detail when reviewing a subject’s data in eCRFs or by viewing a summary page for the site. Creating Manual Queries EDC systems can often be configured for certain individuals to have permission to create a manual query. The configuration always allows data managers to create queries given their constant contact with the data as part of data review and cleaning. Most companies also give monitors access so that they can create queries as EDC provides them, along with immediate access to data. (Remote review of data by a CRA before a monitoring visit is very common.) Larger companies may also provide lab data administrators (see Chapter 9) and coders (see Chapter 11) access to create queries directly rather than routing them through the data manager. Resolving an EDC Query The resolution to a query (be it paper or EDC) can be a correction to the data, a confirmation that data is not available, or a note that although the data triggers a discrepancy, that data really is correct as it stands. It is standard practice, and sup- ported by most software, that when a site corrects data so that it no longer triggers an automatic system discrepancy, the original query closes automatically. The audit trail built into the software as a requirement of 21 CFR Part 11 records the change, who made it, when it was made, and the reason for the change. This is not true for queries created manually; that is, if a manual query questions the value entered by the site, it does not close automatically when the site corrects the data because there is no rule (edit check) to test if the new value is acceptable—it has to be reviewed by the creator of the query to see if the query has been satisfied. Review of a query response is also recommended for responses to automatic sys- tem queries when the response does not involve a correction to the data. When a site responds to a query that the data is not available or that the value is correct as is, there is a danger that the original answer is inappropriate and perhaps even repre- sents a protocol violation. Responding that a blood pressure value that is not possible in humans is indeed correct is an example of an inappropriate response. Responding that a disallowed medicine was indeed taken by the subject is an example of a proto- col violation that may require action. Because of the danger of such inappropriate or worrisome responses, many companies institute a workflow that requires review by data management or a clinical monitor for all queries closed by the site that do not include a correction to the data.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Getting PI Signatures", "definition": "Having the PI sign and date the query form sounds uncontroversial, but it is. At this time, most companies require that the PI sign each query form. A subinvestigator is permitted to sign if that investigator has been assigned this responsibility in the site’s regulatory paper work. Nearly every company that requires PI signature runs into the problem of investigators being away for a long time on vacation or business so that they cannot sign query forms. This problem is particularly serious as a study lock approaches. Despite the practical problems for data management and the CRAs who work with the sites, many regulatory groups insist that this procedure is the only way that the sponsor can be sure the PI is personally aware of all data changes as per ICH E6 GCP Section 8 (see previous discussion). In the past it was common to allow a study coordinator to take responsibility for, and sign, some or all of the resolutions on query forms. As the industry standard changed to requiring a PI be aware of query data, these companies made sure the PI was aware of all CRF changes by sending all the changes at the end of the study or as the subject closed out. The changes were presented in the form of lists of modi- fied values or as copies of updated CRFs (working copies) that showed the changes. The PI was asked to sign a form saying that he is aware of all the data. This proce- dure did allow for faster turn-around on getting discrepancy resolutions during study conduct but brought up the question of whether the investigator did, in truth, review the changes before signing off. This technique is presented here as it is possible for industry trends to change to make this an acceptable option again in the future. Applying the Resolution When a site provides the resolution to a discrepancy on a query form, they may write a paragraph explaining the correct action or justifying a value, and a CRA or data manager may need to interpret the response to determine what action to take. It may even be necessary to resend the query to get clarification on the exact resolution the site intended. Because of problems in interpreting site responses and because site responses may hint at a medical issue or even contain a subtle protocol violation, many companies have CRAs review all queries as they come in from the sites. This is not a universal approach, but anecdotal information indicates that it produces posi- tive results in quality of the data and improves safety surveillance for the companies that try it. For queries that require a change to data or provide a value that was missing, the change is made by editing the data using the normal data entry screens. As noted in Chapter 6, correction or editing of clinical data to reflect a resolution generally fol- lows a different path from that of initial entry of the data. In particular, data manage- ment systems often don’t support second entry of edits, so many companies require a visual review of the edits or a comparison of the audit trail of the database against the query form to confirm that the change was made correctly. Some data manage- ment systems support a query process whereby the data manager supplies a correct value for a field as a proposed action at the time the resolution type (or status) is 82 Practical Guide to Clinical Data Management, Third Edition recorded for the discrepancy. This proposed action may be reviewed before it is actu- ally applied. The review of the edit or review of the proposed action reflects the fact that many companies were finding that errors were being introduced more through the edit process than through the initial entry process, clearly indicating that extra attention is needed to assure accuracy in these steps. After edits are made, it is essen- tial to rerun all cleaning rules over the data as it is common for updates to the data as part of a discrepancy resolution to cause some other discrepancy to be raised! The site response to a query may not result in a change to the data. The original value may really be as reported on the CRF or it may not be possible to obtain a response or value. These responses must still be logged into the discrepancy man- agement system so that the discrepancy is closed. As with EDC query responses, a response of ok as is for a particular data value may not be possible or may be a protocol violation. When CRA review is used (see previous discussion), the CRA reviews for these cases and requests the discrepancy be resent. When data managers review the response and take action without CRA review, they should be trained on an appropriate action, but because most data managers do not have a clinical back- ground, they are less likely to detect a problem. Most discrepancy systems check for duplicate discrepancies on the same data. When a value is not changed, the system recognizes that the discrepancy has already been raised (and closed) and will not raise it again.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "tracked mail carrier", "definition": "•\t Monitors may carry them along for site visits •\t CDM staff or systems can fax the query to the sites •\t CDM staff or systems can send PDF versions by email to the site All of these are acceptable from a regulatory point of view as long as the queries are tracked (see following text) to ensure that queries sent and queries received back are not lost. Resolving Paper Queries The site receives the query forms, researches the questions, and provides resolutions on the query forms. The PI signs and dates each form, and the study coordinator files the forms in the subject binders at the site. Copies are sent back to data manage- ment using the appropriate method (see previous discussion) to data management for", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "TRACKING QUERIES", "definition": "For both EDC and paper studies, data managers must track the status of discrepan- cies and queries throughout the study. By the time of study lock (see Chapter 13), all discrepancies and all queries must be resolved or otherwise closed. Data manage- ment is typically responsible for providing frequent updates to the study team on the total number of queries, unresolved queries, and how long it is taking sites to resolve queries. EDC systems typically have reports built in to provide query numbers by status and site, and can also identify queries that remain unresolved past a reasonable period of time (e.g., beyond three weeks). Tracking queries is more of a problem for paper for several reasons: •\t CDM systems may track individual queries, but they don’t necessarily track the query form itself which can have multiple queries on it. •\t The paper query process has more stops in the workflow. •\t Data corrections found on the query form are a very important source of site data and are GCP essential records. The last point is very important: auditors have been known to ask sponsors to demonstrate that they have received all query forms from the sites and have applied all the corrections accurately. The goal then, for tracking paper query forms, is the same as that for CRFs: to make sure all query responses are received from the sites and that the corrections make their way accurately into the database. Similar to tracking paper CRFs, there is a tension between having lots of states for a query and having to manually update that status. Figure 8.2 shows how two", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "STORING LAB DATA", "definition": "When they set up database structures for a study, database designers also prepare the structures to store lab data and associated information. As we saw in Chapter 3, “Database Design Considerations,” database tables may be structured in a tall-skinny 88 Practical Guide to Clinical Data Management, Third Edition (normalized) or short-fat format. In the tall-skinny format, there are fewer columns, and data is collected in many rows. In the short-fat format, there is one column for each piece of data collected and one row per collection. Lab data lends itself particu- larly well to a tall-skinny format, despite the fact that it makes some kinds of queries and checks more complex. Advantages of the Tall-Skinny Format The reasons most companies prefer the tall-skinny format for lab data include: •\t The need to store information in addition to the actual test result •\t Ease of checking against normal ranges •\t The structure allows for flexibility in reporting and analysis •\t Loading routines for electronic files is simpler All these points are worth exploring in further detail. The need to store information in addition to the test result is probably the over- whelming reason to go with a tall-skinny format. One common CRF page design for collecting lab data has a single field next to the name of each lab test where someone from the site writes in the result, and next to that might be a box to indicate if the site finds this value to be clinically abnormal. There may even be a text field associ- ated with if abnormal. Besides the results and the abnormal indicators, lab results have further additional data associated with them, some of which are printed on the page and some of which are calculated or derived. Units are often preprinted on the page, yet they should always be stored with or linked to the data in the database (see following text). Derived or calculated data may include test values converted to stan- dard units. If a tall-skinny format is used to store the result, fields for abnormal, and then also units and a standardized result, this structure (a table or record) would have six columns; a short-fat format would have the number of columns equal to six times the number of tests. Figure 9.2 shows a tall-skinny lab table and a short-fat lab table for the case of the four lab results only (not including the context information). Normal ranges for laboratory values are nearly always stored in the tall-skinny format when they are collected (see following text). Checking routines that compare the test results against the normal range for that test are easiest to create when the", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "WBC", "definition": "FIGURE 9.1  Examples of values generally called lab data. Note that some assays, such as white blood cell count (WBC), show up in more than one group. Be aware that these are not the same values. They measure different things and have different units.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Managing Lab Data", "definition": "95 way. Later, someone at the lab transcribes the result into Microsoft Excel or Access. Neither of these packages is a 21 CFR Part 11 compliant system if used as is out of the box and creating a compliant application based on them is a significant undertaking. If the lab does nothing more than enter the data into an electronic file, there is no security against inadvertent or intentional changes, no audit trail, no checks to assure accurate transcription—indeed no guarantee of data security and integrity. A lab could use these off-the-shelf packages if they reliably verify 100% of the data and then immediately lock or store the files on read-only media (such as a CD) to prevent changes. While this is a simple approach, it does take a lot of discipline to satisfy the reliable part and the immediate part so that changes are not made after the data has been verified. It comes down to how good the procedures are and how likely laboratory staff are to follow those procedures consistently. The lab then ships the data to the sponsor who may run edit checks or other types of analyses on it. The edit checks are likely to identify some discrepancies in the data, so a process must be in place with the laboratory that details how corrections are to be made. Normal query forms may be the best approach here unless a large number of changes are expected. In that case, it may be more efficient to have the lab correct the original file (verified, of course, to detect inadvertent changes while editing) and resend. To make this work to produce reliable data entails significant investment in proce- dures and in manual review by both the lab and the sponsor. See Chapter 10, “Non- CRF Data,” which discusses handling of data in electronic files in detail, for more controls on electronic data.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "PLATELETS", "definition": "PLATELETS_UNIT STD_PLATELETS FIGURE 9.2  Lab results with additional fields for units and standardized results stored in a tall-skinny format and a short-fat format. (Both kinds of tables would also have columns for header and patient identifying information, which are not shown.) 90 Practical Guide to Clinical Data Management, Third Edition the results column, this means that the expected characteristics of the data cannot be enforced. For example, it is not possible to put simple range checks on the results column of a tall-skinny lab table because it contains results from all kinds of tests. In fact, since the results for some tests are text (e.g., +2 for glucose measured in uri- nalysis), the column itself must be a text type. These characteristics also dictate that results cannot be coded. That is, test results that otherwise might be coded (such as platelet estimate: 1 = adequate/2 = inadequate), would have to be stored as simple values (such as: ADQ and INADQ). Another disadvantage of the tall-skinny format is the extra work introduced when writing checking routines, derivations, and queries. Each check must include the name of the test as part of the logic. That is, a range check on Hemoglobin in a short- fat format might read: hemoglobin >= 11.9 and hemoglobin <=17.0 and in a tall-skinny format read as: test_name=HEMOGLOBIN AND (test_result>=11.9 and test_result<=17.0). It may not look like much additional work, but over the many lab tests common to most studies, errors are bound to occur in checking routines, reports, and queries. Despite these significant disadvantages, the flexibility of the format seems to have led nearly all data management groups to store lab data in the tall-skinny format.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Identifying Lab Tests", "definition": "Whether the lab results are stored in the tall-skinny or the short-fat format, the tests must be identified. In the tall-skinny format, the name of the test (or a code for the name) appears as a data value in the “test name” column. In the short-fat format, the name of the test is used to name a column onto itself. (Refer again to Figure 9.2.) In both cases, care must be taken to recognize the difference between similarly named results collected in different ways. For example, a test named Glucose appears both in blood chemistry tests and also in urinalysis tests—and they are not the same. Similarly, it might be important to differentiate between values taken in fasting and nonfasting states. Naming the tests differently is the method usually used for assays that appear in more than one grouping of tests. The hematology version of white blood cell count (WBC) may be called HWBC and that for the urinalysis version may be called UWBC. Another approach is to store the groupings separately so that blood chemistry values are separate from hematology values, which are separate from urinalysis values—in this case, simply using the name WBC may be adequate to fully identify the value. Storing the grouped values separately can be implemented in either tall- skinny or short-fat formats. Identifying the difference between conditions such as fasting and nonfasting for a test is more difficult. Naming would prove unwieldy, in particular for those studies", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "STORING UNITS", "definition": "Laboratory results stored without applicable units lose much of their long-term value. If the results do not have the associated units stored with them, there are difficulties in checking against normal ranges or comparing data across studies. To avoid these problems, database designers should store the units with the test results even if those units are predefined by the study or preprinted on the CRF. Whether or not the units are preset, the database design and data managers must be prepared to handle results reported in different units as different labs report results differently, and it is a risky practice to have sites do the calculation to the expected unit. If the unexpected units are an exception, they can be treated as such. However, when there are many sites or different laboratories participating in a study, variation in units will be the norm. When variation is expected, some companies choose to store not only the reported result and applicable unit but also a version of the result converted to a standard unit via calculation. This standardized result may be stored in the database as a derived value or appear only in analysis datasets. Converting results to a standard unit requires that biostatisticians decide on the preferred units (generally international standard [SI] units are used when available) and specify how to convert each possible combination of tests and units to those standards. It also requires that the units collected with the result be stored consis- tently. That is, grams per deciliter should be stored consistently as g/dl or gm/dl— not a mixture of both as may happen when units are written on a CRF by the site. A units codelist can help enforce company standards and would typically be used for an eCRF. For a paper CRF, the data entry operator enters or selects a value from the codelist that may not be exactly what appears on the CRF. (This can be clearly spelled out and documented in data entry instructions.) When all results are converted to SI units for analysis and reporting, a new option opens up regarding units: units can be taken from normal ranges reported by the laboratory (see the following text) providing the result rather than having the site enter them or taking them from central lab data. Normal ranges and results from a given lab must be in the same units. So if the normal range is stored with a unit once for all values reported for that analyte, then the results can be assumed to be in that unit. This saves reporting of units and can be especially helpful for values entered on a CRF or eCRF by the site. There is no danger of the result value losing its meaning without units because the SI version of the result is always used for analysis. RANGES AND NORMAL RANGES It is not unusual for companies to do two kinds of checks on laboratory data—one simple range check on whether the data is plausible or to check for transcription errors and one to evaluate the safety and/or efficacy of the treatment. In the first case, 92 Practical Guide to Clinical Data Management, Third Edition a query is issued to clarify with the site that the reported value is correct. The second case is used to provide information about the treatment in question. It is important to note that the lab, local or central, which evaluates the sample to obtain the lab result, is responsible for notifying the principal investigator in cases of seriously abnormal values in a short timeframe while the subject is still active—that is not the job of data management using the kinds of checking described here. The simple range checks used to issue discrepancies are frequently based on either textbook values or experience with the population in a particular study. These are programmed along with the rest of the edit checks for the study. To perform the other kind of check to look for trends of lab values being out of range, one must know what the values for that test normally are. These normal values are generally called normal ranges or normals. The normal range for a particular test depends on the method and equipment used to obtain the result and also may depend on other factors such as sex and age. The laboratory performing the analysis supplies normal ranges and they are considered part of the essential trial documentation (ICH E6 GCP Section 8.2.11).", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Laboratory IDs", "definition": "Because normal ranges depend on the method and equipment used, there must be some way of knowing which results—and which normal ranges—come from which locations. Therefore, a laboratory ID of some kind must be associated with each result and also with each normal range. In some cases, the laboratory will be the same for all subjects in a study. More typically, several laboratories process samples for a single study. Because of the importance of knowing which ranges are associ- ated with which values, it is better not to make assumptions but rather to store the laboratory ID for each sample taken from each subject. For example, although an investigator nearly always uses a given lab, there may be an emergency that requires an alternate lab for a set of subjects or a period of time, or a lab may do 4 of 6 analy- ses but send out for the remaining 2 to be performed elsewhere.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Normal Range Storage", "definition": "Normal ranges may be stored in the same storage area as the clinical trial data, or they may be stored centrally and available to multiple studies. Storing the data cen- trally reduces the workload to enter and manage the values. However, care must be taken to allow checking programs and appropriate staff to access that central data. Also, if the study is archived or transferred, the associated normal ranges might need to be extracted for archive or transfer also. As noted earlier in this chapter, normal range data is usually stored using the tall-skinny format. In addition to the high and low range values for the result, the normals data must have the laboratory ID and the units used. If ranges are collected more than once per study or if the normal ranges are stored centrally, an effective date must in some way be available. If there is an age or sex dependence, there must also be columns for that. Figure 9.3 shows one example of a group of fields to store normal ranges in a tall-skinny format.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "LAB RESULT TRENDS", "definition": "Both discrepancy checking and reporting on normal range values focus on a single, specific result. They do not detect when values have changed significantly from the baseline for a single subject nor do they identify trends for all or a subset of sub- jects in a study. This kind of cross-visit and cross-subject checking has traditionally been left to the statisticians to perform. However, in the interest of detecting prob- lems early, some data management groups have been given the task of reviewing the laboratory data before statisticians look at it using special tools and programs. While data managers typically do not have the training to make statistical or medi- cal assessments, they can detect patterns that have not been caught by simple range checks and bring them to the attention of the site or medical monitor.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "USING CENTRAL LABS", "definition": "In multicenter trials, a central laboratory is frequently used to deal with the com- plex issues of quality control needed at every laboratory. GCP requires that all labs have full documentation, systems with data audit trials, standard procedures, trained staff, archives of samples and data, and routine quality assurance inspections. The sponsor must be assured that the GCP requirements for every lab in the study are met. Shipping samples to a central laboratory for large trials makes this management LAB_ID: Identifies the source of the normal range values EFFECTIVE_DATE: Date the range became effective TEST_NAME: Name of the lab test or analyte; must be consistent with that used in storing", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "patient results", "definition": "TEST_UNITS: Units that apply to the range values SEX: If present, the range is sex dependent AGE_LO: If present, the low end of the age range to which the normal values apply AGE_HI: If present, the high end of the age range to which the normal values apply NORMAL_LO: Low end of the normal range NORMAL_HI: High end of the normal range FIGURE 9.3  An example of a group of fields used to store normal range data in a tall- skinny format. Each field creates a column in the database table. This example assumes that at least some of the ranges will be sex and age dependent. 94 Practical Guide to Clinical Data Management, Third Edition more reasonable. It also reduces the variations in the normal ranges that would be expected if several labs were used. Of course, there are factors that should weigh in favor of the use of local labora- tories including: •\t Expertise in particular area, perhaps with an unusual assay •\t Need for very fast analysis due to medical need or for screening purposes •\t Problems with logistics or cost of sample transportation In a trial with a single or few sites, the local laboratory may well provide a conve- nient and cost-effective option. When a trial calls for use of one or more central laboratories, the data invari- ably comes in as an electronic file and not on the CRF or eCRF. The next chapter (Chapter 10, “Non-CRF Data”) discusses how data arriving electronically is managed and cleaned.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "USING SPECIALTY LABS", "definition": "Many companies are developing drugs and devices on the cutting edge of science. This often means that the lab tests or assays they need to perform to determine the efficacy of the treatment are also on the cutting edge. When this is the case, the sponsor will sometimes need to turn to specialized laboratories to perform the tests. All too often, these are small labs or even investigator sites that are not set up with good practices, standard operating procedures (SOPs), and systems that are 21 CFR (Code of Federal Regulations) Part 11 compliant. The sponsor receives a shipment of data at the end of the study and quickly identifies a raft of problems or inconsistencies and then faces the question of how reliable and analyzable this data is. Must the study be rerun or can some other approach be taken to avoid throwing out the results of the trial? There are two important steps that sponsors can take proactively to help assure a level of confidence in the data: auditing the lab and set- ting up verification procedures.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Auditing the Lab", "definition": "The sponsor is ultimately responsible for data coming from a laboratory. When cen- tral labs are used, companies will typically audit the lab or will refer to a past, recent audit. For the most part, this is a cursory audit since large central laboratories are audited constantly and are likely to have reasonable practices and be in compliance with regulations. When a small laboratory or investigator site is needed for a study, the sponsor must audit much more carefully and pay particular attention to the com- puter systems that will be used for collection and storage of the data. There must be assurance that the data is reliable and reflects the actual results. In a very common scenario, the lab runs a specialized assay on a sample using some equipment they own or they follow an analysis procedure they have developed. The equipment is likely to be validated and reliable and it will often print out or dis- play a result. These results may be taped into a lab notebook or filed in some other", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Monitoring the Data", "definition": "Another approach to assuring the reliability of specialized lab results is to monitor the data from a specialty lab in a way similar to the monitoring of CRF data. In this approach, the lab processes samples and records results according to good proce- dures (one hopes) and ships the resulting data as an electronic file to the sponsor. The sponsor then loads or stores the data in the clinical data management system where it is then under audit trail. A monitor receives a listing of the data from the sponsor’s clinical data management system and possibly also a list of discrepancies that have been raised on the data through the normal edit checks. The monitor takes those listings to the lab and verifies 100% of the listed data against source data. As noted previously, the source data could well be printouts taped into lab notebooks. If the monitor identifies an error, the correction is made at the sponsor with appropriate documentation and under audit trail. Whether the lab updates their own copy of the data is up to them; in this approach, there is no resending of the data. Because this kind of independent verification is much like the monitoring of CRF data, the level of confidence in that data will be similar to that for CRF data. QUALITY ASSURANCE AROUND LAB DATA Lab data is crucial to assessing the safety and often the efficacy of a treatment and so should be considered explicitly in deciding appropriate data quality measures for 96 Practical Guide to Clinical Data Management, Third Edition clinical trial data. The first step is to identify all the sources of different types of lab data. The data management plan is a good repository for an overview list of the types of lab data applicable to the study and the various sources (vendors) of that data. When lab data is from a local lab and entered on a paper CRF, normal CRF process- ing and auditing provides assurance of complete and accurate collection of lab val- ues. When the data is entered into an eCRF, edit checks should be used liberally to identify possible transcription errors by the site. When data comes in electronically, steps must be in place to ensure that all the expected data is collected and that it matches up with other CRF- or eCRF-based data. Because it is common to have lab data from more than one source (some via the CRF or eCRF and some via electronic files), multiple quality assurance measures will apply for many studies. SOPs FOR PROCESSING LAB DATA Just as the quality assurance (QA) measures taken are tailored to the way lab data is collected, so too the appropriate SOPs will be contingent on the source of the data. Lab data on CRFs or eCRFs will be governed by SOPs for other CRF data. Lab data arriving via electronic file will be governed by specific SOPs as described in Chapter 10. The one thing not covered under data collection is the handling of laboratory ranges. Because this is something that is required by GCP and because there are hand-offs between clinical (who collects the information) and data management, (who processes it), a cross-functional SOP would be warranted. WHY LAB DATA NEEDS SPECIAL ATTENTION Somehow, laboratory data seems to be the most problematic of all the data in a clinical study. Even though the amount of lab data collected on the CRF has actually diminished over time, it still adds difficulties with units and management of normal ranges on top of the normal data management activities. In recent years, lab data management has been further complicated by introducing more sources of the data into trials. Today, most clinical studies will have some basic lab values entered by the site, but there will nearly always be additional lab values provided electronically by at least one central lab and then one or more specialty assays coming from yet more vendors. This adds tasks for clinical data management to manage timelines for data deliveries and the need to interact with multiple vendors regarding data problems. 97 10 Non-CRF Data In Chapter 9 we looked at laboratory data—a class of data central to most clinical trials in assessing safety—and learned that while some lab values are collected on a case report form (CRF) or electronic CRF (eCRF), many more are derived from samples sent to central laboratories that then return the results via electronic files. That is just one example of subject data associated with a clinical trial that is not recorded on the CRF. Other common examples include the following: •\t Electrocardiogram (ECG) readings made at a reading center for all kinds", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "of studies", "definition": "•\t Pathogen identification in studies of infections •\t Interactive voice response system (IVRS) data for randomized trials •\t Pharmacokinetic (PK) data in early phase trials •\t Data from electronic patient report outcome devices These types of data are critical to the analysis of the study, and just like CRF data, they must be accurate and complete and the sponsor must be able to show that steps have been taken to ensure the integrity of the data. In this chapter we will discuss how non-CRF data is received and stored in compliance with regulations and how it is cleaned to show evidence of completeness and quality. RECEIVING ELECTRONIC FILES FROM A VENDOR Clinical data in electronic format is subject to 21 CFR (Code of Federal Regulations) Part 11 requirements since it will likely be used as part of a submission to the Food and Drug Administration (FDA). Sponsor and contract research organization (CRO) computer systems used for clinical data management must meet the requirements of the rule, and this is also true of any laboratory or vendor providing data associ- ated with a clinical trial. In Chapter 9 we saw the danger in small independent labs that may not be using Part 11–compliant systems, but even when the lab or vendor systems are compliant, integrity and security must be maintained when the data is transferred to the sponsor or CRO and then moved through the data management processes for the trial.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Transferring Files", "definition": "Clinical data should not be sent by email without additional security. The federal regulation 21 CFR Part 11 considers email to be part of an open system and advises additional security such as encryption and password protection for data sent by 98 Practical Guide to Clinical Data Management, Third Edition email. The rule in Section 11.30 says, “Persons who use open systems to create, modify, maintain, or transmit electronic records shall employ procedures and con- trols designed to ensure the authenticity, integrity, and, as appropriate, the confi- dentiality of electronic records from the point of their creation to the point of their receipt” (emphasis added). At a minimum, the sender should use a zip utility and password-encrypt the file to prevent unauthorized decompression. The password should never be sent in the same email as the file; ideally, it is agreed upon prior to the transfer or set separately for each transfer and communicated by phone. Other secure methods of transmitting clinical data include sending CDs by tracked carrier, using secure drop boxes reached electronically, and giving the vendor access to the appropriate secure closed networks of the sponsor or CRO. Once the data is received, it must also be stored securely in such a way as to be able to provide evidence that the data received from the vendor was not purposely or inadvertently modified without audit trail. Many companies load vendor data into a clinical data management (CDM) database or other data warehouse to provide such assurance. Other options include creating secure read-only areas on servers to provide the gold copy of the data. (Gold copy or golden master refers to the original release or shipped copy of software or data.) Any later use of the data could then be compared to the original to show that it had not been altered.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Formatting the Data", "definition": "We talk about sending data from the vendor to the sponsor or CRO as an electronic file—but what kind of file is it? Is it Microsoft Word or Excel? Is it an SAS® transfer file? Is it a simple ASCII, comma-delimited format? What data is in the file? How are the subjects and samples identified? Because the vendor needs to know what to send and the receiver needs to know what is coming in, it has become industry standard practice to establish file transfer agreements. These agreements specify the format and content of a transfer and usually also identify the frequency and method of trans- fer. Both the vendor and the receiver should approve the document.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Loading Data", "definition": "Loading non-CRF data into a central CDM or warehouse database is done either through programs written specifically for each study or by configuring a utility within the system. As noted in previous chapters, whether users write a program or configure an application, if it affects clinical data, it should be subject to a validation process. In addition, whenever clinical data is copied or transferred, it is subject to 21 CFR Part 11 and loading would be considered a copy. The validation process for any application starts with a specification. A map- ping of the layout of the electronic file as described in the file transfer agree- ment to the database storage structures provides the basis of the specification. The specification will also have to address data issues as described in the following text. The validation continues with the program being written according to good company practices or the application being configured according to guidelines and manuals. Documented testing, release, and specific user instructions round out", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Non-CRF Data", "definition": "101 and the question will be whether the data was not transferred or never available. So, the one field that should appear on the CRF to match lab data is an indicator to be marked if for some reason the sample was not done or is in some way not analyzable. Companies that drop those fields in large Phase III trials with a significant amount of external data will find a very large number of reconciliation queries to the site when there are missing records in the vendor data but no way to know if the vendor missed something or the site did not perform that test. As the previous example implies, any questions about differences in expected data and received data could be due either to problems at the vendor or to problems at the site. Typically, inquiries to the vendor are informal, such as email or shared spreadsheets to track discrepancies. If the vendor confirms everything is correct to their knowledge, then the site must be queried via creation of a manual query (see Chapter 8). QUALITY ASSURANCE FOR EXTERNAL DATA Data from a vendor must be transferred and stored according to the requirements of 21 CFR Part 11. If a company does nothing else with electronic data received from a vendor, it must still ensure the integrity of the data received. This requirement can- not be overstated and should never be overlooked. The completeness of the data might be considered part of data integrity, but the steps to ensure it are also steps that provide confidence in the data’s quality. Data reconciliation as previously described is used to ensure that the company receives all the expected data and also no extra unexpected records. Data reconciliation against fields on the CRF or eCRF provides confidence that the data reported for a given subject and sample or reading is in fact the right sample for that sample or reading; that is, no samples have been inappropriately assigned to another subject.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "CLEANING NON-CRF DATA", "definition": "Initial cleaning of data will usually take place at the source—the vendor. Reliable and experienced vendors will perform reasonable cleaning to ensure proper tran- scription of the data and to ensure that the data is correctly associated with a sample ID that connects the sample and data to a given subject. The important words here are reliable and experienced, as some specialty labs, investigators carrying out new assays, and other small vendors may not be aware of good data management prac- tices and 21 CFR Part 11. It is ultimately the sponsor’s responsibility to ensure that quality data leaves the vendor’s computers! It is the responsibility of the data management group to ensure the completeness and accuracy of the data for the trial as a whole. To ensure completeness and accu- racy of data received from vendors, the receiving CDM group will compare what is known about the subjects in the trial to the data coming in via electronic file. For example, one would expect at least some results for all subjects in the trial, and fur- ther, one would expect results each time the sample was taken from the subject and there should be no results anytime a not done appears. This matching of data from an external source against data on the CRF or eCRF is often called reconciliation. Reconciliation is a word we will see again when discussing matching of serious adverse events in the trial database against those found in the safety database—in data management, reconciliation means making sure that data for a clinical trial subject stored in one place matches similar data stored in another. Reconciling against subject and visit makes clear sense, but it is often possible to do more reconciliation, and the question becomes whether or not it is worth it. For example, labs often have more information about a trial subject than just the subject identifier—they may know the sex and age of a subject, especially if the lab result normal ranges for a lab test are age and/or sex dependent. That information is also in the CRF data. Is it worth checking each time for every subject that the lab ver- sions match the CRF versions? The answer will depend on how important a match is and to some extent how likely the vendor is to have (or to ship) wrong informa- tion sent with the sample. In another example, some CRFs call for a sample identi- fier that is attached to the sample. The data management group might request that sample identifier on the CRF. They should match, but unfortunately, if this requires a transcription, the site often makes mistakes and there will be sample ids that don’t match. The important question then is, does this matter? Sometimes it will matter and sometimes it will not. It is best to have discussions of what fields will be involved in reconciliation when the CRF or eCRF is designed. While age and sex always appear, why use the sample identifier when it does not matter to the CRF since the data can be matched on subject ID? That being said, companies can take the idea of reducing reconciliation one step too far and collect no data at all on the CRF when a sample or reading is taken dur- ing a trial since the data will all arrive from a central lab. This will backfire if there is a chance the data could not be collected because nothing will arrive at the sponsor", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "SOPs FOR NON-CRF DATA", "definition": "Because transfer and copying of clinical data has to meet 21 CFR Part 11, a stan- dard operating procedure (SOP) should be in place to show that procedures used for receiving and loading electronic files from external vendors are in compliance with the rule. This SOP should either require transfer via a closed system or require that extra security is in place if the open Internet or email is used. That electronic file transfer SOP, or a separate SOP on data loading, should also require a transfer specification for every type of data and vendor. The transfer specification will act as the specification against which test transfers and loads will take place. Initial configuration and programming of loading programs must be tested to show that the data is not altered during the load (copy). It is also wise to require some kind of review or check of an error log for every transfer even when the receipt of data becomes routine during the conduct of the study because nearly all companies have had the experience of receiving files from a vendor without problems for a period of time and then having the vendor inexplicably change the data format. Finally, a study cannot be locked (see Chapter 13) until all the external data has been pro- cessed and reconciled. 102 Practical Guide to Clinical Data Management, Third Edition WHEN NON-CRF DATA IS OUTSIDE DATA MANAGEMENT At some companies, non-CRF data becomes the responsibility of groups outside of data management. For example, if the external data is being sent as SAS datasets, the results may go directly to the SAS programmers. If this is the case, the data man- agement plan or other company data handling agreement should make clear who is responsible for the reconciliation of the data against CRF data and how any discrep- ancies found in that reconciliation are to be handled. Even though data management is responsible for the completeness and accuracy of the data, this situation may make it impossible for data management to carry out tasks because they do not have access to SAS or SAS dataset storage folders. There is a danger here that no accounting and reconciliation happens in this case. There is another danger that other groups may not be as aware as data management of 21 CFR Part 11 requirements for transfer and storage. To avoid data problems, data management must take the lead in bringing these matters up to the study team for the good of the trials. 103 11 Collecting Adverse", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Event Data", "definition": "During clinical studies, subjects always have undesirable experiences. These may or may not be related to the study drug or device. These experiences are known as adverse events (AEs) or adverse experiences and may be considered adverse effects or adverse drug reactions if there is a relation to the treatment. The strict definitions of what is an adverse event, an unexpected adverse event, and a serious adverse event are found in the Code of Federal Regulations (21 CFR), and the International Conference on Harmonization (ICH) topic. Data managers typically do not make judgments about what is an adverse event and what is a serious adverse event, but they do need to understand enough about the data to process and store it properly. In many ways, adverse event data (AE data) is just like other clinical data. It is collected through case report forms (CRFs) or electronic CRFs (eCRFs) and stored along with the other subject data in the database. However, certain aspects of AE data add two important tasks to the data management process: (1) the regular coding of reported terms and (2) the need to cross-check serious adverse event reports in the data management system against those in the company’s serious adverse event sys- tem. Because both of these tasks can be made easier or harder by the way in which the AE data is collected and stored, we will look at collection first and then discuss coding and reconciliation. COLLECTING ADVERSE EVENTS People new to clinical trials may not realize that nearly all subjects in a Phase II or Phase III trial have some kind of adverse event or other during the course of the study. Remember that the events do not actually have to be related to the treatment, so sites correctly report everything from colds, to injuries from falls and car acci- dents, to death by murder, as well as all the typical medical conditions that might be monitored by any doctor. The longer the study and the sicker the subject population for the trial, the more adverse events there will be. Adverse event information for clinical trials can be grouped into three collection types or categories: •\t Open adverse event reports •\t Expected signs and systems •\t Serious adverse event reports Open adverse event reports do not prompt the investigator or subject for any specific problem. The event is reported either in the subject’s own words or using 104 Practical Guide to Clinical Data Management, Third Edition the investigator’s version of the subject’s words. This is the most common way that adverse events are reported during a clinical trial because the open format does not prejudice the investigator or subject in any particular direction. However, when a treatment has already shown a history of certain kinds of adverse events, the clinical protocol may call for an assessment of the frequency and severity for those specific events during the course of the study. In this case, a list of these specific signs and/ or symptoms may be used and the subject or investigator identifies which did in fact occur with a yes/no answer. In both types of collection, the investigator gener- ally makes an assessment of the severity, relationship to the treatment, and action taken, as well as providing information on start and stop dates or whether the event is ongoing. Either an expected sign or symptom or an open adverse event must be considered a serious adverse event (SAE) if it meets the regulatory criteria of resulting in death, is life-threatening, requires inpatient hospitalization or prolongation of existing hos- pitalization, creates persistent or significant disability/incapacity, or is a congenital anomaly/birth defect. Any SAE requires the site to quickly report it directly to the responsible safety group (the sponsor’s or contract research organization’s [CRO’s] as appropriate). In clinical trials, SAEs are also recorded with the rest of the subject data on paper or electronic CRFs and becomes part of the data for the study. The safety group reports are used to interact with regulatory agencies and the clinical data is used for long-term analyses. While the open adverse events and signs and symptom types of data will appear on different pages or screens, the data is similar enough that they can share similar structures (or even the same structure) for storage to allow for easy pooling of data. Serious adverse event information may be collected on the AE page or on a separate page as discussed in the following text. There are also options to consider both for the content of these forms and on their positioning within the overall CRF booklet or study design.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Adverse Event Forms", "definition": "AE forms will collect several kinds of data, including the following: •\t The text used to describe the event •\t Start dates (and possibly times) •\t Stop dates (and possibly times) or an indicator that the event is continuing •\t A variety of indicators including severity, relationship to drug, outcome,", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "and action taken", "definition": "•\t Additional comments or additional treatment or medication information It is very common to have the AE form include a question that asks: Is this a seri- ous adverse event? This is the trigger that lets clinical data management know that SAE reconciliation, as described in the following text, will be required. The example AE form in Figure 11.1 shows typical fields. For all studies, the investigator will ask the subject about any adverse events since the last visit or check point. For some studies, companies will transcribe these events Collecting Adverse Event Data 105 Protocol N013_06 Site: □ □ □ □ Subject: □ □ □ □ Page 121. Where any AEs reported from the time of study drug administration to final discharge from the study? Yes □  No □", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "if SAE", "definition": "1 □ 2 □ 3 □ Relationship to Study Drug 1 – Probably/Possibly Related 2 – Not Related", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Intensity", "definition": "1 – Mild 2 – Moderate 3 – Severe Action Taken with Study", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Med", "definition": "1 – None 2 – Interrupted 3 – Discontinued", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Outcome", "definition": "1 – Subject died 2 – Completely recovered 3 – Recovered with sequelae 4 – Condition still present 5 – Condition improving FIGURE 11.1  An example of a log-style AE form for a paper study. 106 Practical Guide to Clinical Data Management, Third Edition on a form specific to the visit, but the more typical approach is to ask about AEs at each visit and collect the reported events in a log portion of the CRF that spans the entire study. In the visit-specific case, the investigator has to look back at the previ- ous visit(s) and ask about any AEs that were continuing at the time that may have resolved. If it resolved, that information is included on the new form. The two differ- ent reports, on the previous and current form, have to use the same term for the event in order for the analysis programs to correctly match them up and report a correct start and end date. Going back across visits and recording the event the same way is very challenging for the sites. Instructions for sites and monitors must be very clear and input from all groups represented in the study team is needed to pin this down and determine the algorithm for exactly how the data is to be analyzed and pre- sented. This can be an overwhelming disadvantage to the visit-oriented approach. In the case of the log-type AE collection, the investigator still asks about adverse events at each visit, but they are all collected in one spot in the booklet or at one screen tab in the eCRF. For any AE that is ongoing, the site leaves off the resolution information until it does resolve. At each visit, the site goes through the log checking for resolution of any previously reported AEs. At study termination, the only AEs marked as continuing should be those that are still present at study termination for that subject. The example AE form in Figure 11.1 is a log-type AE form for a paper study with multiple entries possible on each page. An eCRF would look similar, though the limitations of screen size might lead to just one or two AE records per full screen. The instruction at the top gives the reporting period as well as serving as a way of ensuring that there are no blank pages. In this example, relationship to study drug, outcome, intensity, and action taken are coded fields and the site will supply the number provided at the bottom. On an eCRF, these would be either drop-down lists or buttons to click. Special Considerations for Paper AE Forms Adverse events for paper studies cannot be coded (see the following text) until they are monitored, collected, and entered into a computer system. No midstudy safety review can occur unless the AEs are available. Because midstudy safety reviews (by data safety monitoring boards or DSMBs) are common, it is important to understand the implications of visit-based or log-based AE collection when paper CRFs are used. If AEs for a paper trial are collected on pages associated with each visit as previ- ously described, they can be monitored and collected after each subject’s visit. If, however, a log approach is used, only resolved AEs can be collected. When the AEs are recorded on a log page with room for several AEs (see example), then the entire page is not collected until all the AEs on that page are resolved or the subject’s par- ticipation in the study is complete. For paper trials where regular summary review of AEs is required by the protocol, consider using the visit-based approach despite its negative impact on summarizing data or use the log approach but have fewer AEs per page or even just a single AE report on a page so that all resolved AEs can be collected at each monitoring trip, leaving only the unresolved AEs open. One other Collecting Adverse Event Data 107 method companies have tried in an attempt to address these collection problems is to collect the AE pages during a study—even when the resolution date of the AE is not known—and then to use query forms to request that resolution date later in the study. Clearly, every one of these methods entails careful coordination between clinical, data management, and biostatistics. The DSMB may also have to be notified of the resolution state or status of the AE data they are receiving. STORING AND CLEANING AE DATA Most AE forms request start and stop dates (and possibly time) of the event. If an event is ongoing at the end of the subject’s participation in the trial, compa- nies will frequently have the site check a box for continuing. Knowing when the event occurred helps the sponsor determine association with the study treatment. For example, was the subject even on the drug at the time the event was recorded? Unfortunately, in many studies, the time between visits is long enough that the subject may not remember the exact dates for each event. The study designer and clinical group must determine whether the investigator and subject should make a good assumption and provide a fully complete date or whether a partial date can be accepted. These decisions must be made at the beginning of the study as they will determine the type of storage used for the dates and the kinds of cleaning checks performed (see Chapter 3). Because the onset and resolution dates are so critical to analysis of the event, checks on these dates are critical. Clearly the resolution date should be on or after the onset date. But there are other checks as well: the protocol will usually give the period during which AEs are to be collected; for example, from screening to 30 days after drug termination. Dates must fall within this period (though many companies will never ask that any AE, regardless of when reported, be removed from study data). Checks against other dates in the study may also be required, such as a com- parison of the date of treatment discontinuation, when the action taken for an AE is treatment discontinuation. If an event can occur more than once and be recorded (with different start and stop dates, of course) on the same page or screen, then some kind of information to differentiate the two events in the database will help in processing. A unique line number or sequence number, which may or may not appear on the CRF page, can solve this problem. Even when all the events are different, unique numbers on AE pages that have multiple reported events are particularly useful in writing queries to the sites. Adverse event reports include the investigator’s record of the action taken in response to the event. Most frequently, the sponsor presents a coded list of possible actions. The study designer, database designer, and the investigator must be clear as to whether the question requires a single response or whether multiple actions are per- mitted. Storage methods will be very different if a single or multiples are an option. As we noted in Chapter 2, an indicator box that says whether there were any adverse events helps to clarify if the page has been overlooked or if there is no data when a page comes in empty. But, as in all such cases, the question of “any adverse events?” can cause some additional work in data cleaning and discrepancy 108 Practical Guide to Clinical Data Management, Third Edition management when the response is ambiguous. Data management must come up with guidelines for paper studies for how to manage inconsistencies, such as an answer of yes but there are no events listed, or an answer of no or blank when there are events on the page; with EDC studies, the inconsistency can be caught by programming. The regulatory requirements for reporting on adverse events are quite high. The requirements apply not just on a study-by-study basis but also on a larger scale of drug or device and may require across studies conducted anywhere in the world. Because the reports must combine adverse events across studies, it makes sense to standardize adverse event collection and storage from the beginning and to keep it consistent across studies over time. CODING ADVERSE EVENT TERMS In order to summarize adverse event terms, report on them, assess their frequency, and so on, analysts must group terms that are the same. Headache, mild headache, and aching head should all be counted as the same kind of event. This grouping or categorizing is done by matching (or coding) the reported adverse events against a large codelist of adverse events—more commonly called a dictionary or thesaurus. In the past, coding specialists performed this coding to the thesaurus by hand. Now, nearly all firms use some sort of computerized, automatic matching called autocod- ing. Data management, a safety group, or a specialized coding group may be wholly or partly responsible for tasks associated with running the autocoder and manually assigning codes to terms that do not match the dictionary automatically. Autocoders all involve some level of text matching with or without special lexical transformations (see Chapter 26, “Coding Dictionaries and Systems”), so the coding process is heavily dependent on the text found in the adverse event reported term fields. To aid the coding process, some companies request that data management correct certain clear misspellings in reported terms, adjust abbreviations, and make other minor text changes to facilitate coding during data entry and review. This is usually done in the term field directly, but the modified text may also be stored in a secondary field in order to keep it separate from the original reported term. When an autocoder cannot find a match, the term must be coded manually. Tools provided with the dictionary or with the coding software let the coder find the closest appropriate dictionary term and apply it to the reported term. Sometimes the coder may not be able to code a term at all. In this case, a discrepancy is created asking the site to clarify the reported term. Some sophisticated integrations can add these coding queries to the data management or EDC system automatically, but often the coder or data manager must add them as a manual discrepancy or query. When a term cannot be coded because it is a combination of terms (e.g., head- ache and nausea, which should be listed separately), data management may be asked to split the term themselves. While splitting the reported Phrase may not be a problem, it is not clear whether all the data associated with each term (onset date, severity, etc.) apply equally to both terms. As the Food and Drug Administration (FDA) becomes more vigilant as regards safety data, companies are becoming more conservative in handling safety data. The current trend is for data management Collecting Adverse Event Data 109 to issue queries to the sites for all discrepancies or problems associated with AE data—including splitting terms. RECONCILING SERIOUS ADVERSE EVENTS We have seen that SAEs from both clinical trials and marketed products are reported directly to a safety group or safety coordinator. Because of the detailed information related to the case of a serious event, and because of the reporting requirements, these safety groups frequently use a specialized software system for the process- ing and management of SAE data. The SAE report (called a case) is entered in the safety system initially and updated as follow-up information becomes available from the site. All reports to regulatory agencies are run from the case data in the safety system. Serious adverse events that take place during clinical trials also come into the company with the rest of the subject’s trial data on CRFs or via EDC. This version of the information is then stored separately in the clinical data management system database. The clinical data management system is the source of SAE data to be used in analysis, reports, and new drug applications to regulatory agencies. Before the end of the study, the SAE information in the safety system must be compared with that in the data management system to ensure that all SAEs were collected and reported properly in both systems. Data managers generally call this comparison process SAE reconciliation. When reconciling, data management staff look for the following: •\t Cases found in the SAE system but not in the CDM system •\t Cases found in the CDM system but not in the SAE system •\t Deaths associated with any case but found only in one system—perhaps because of updates to the SAE report •\t Cases where the basic data matched up, but where there are differences,", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "such as in onset date", "definition": "See Chapter 11, “Collecting Adverse Event Data,” for further discussion of SAEs and the reconciliation process. Another common source of duplicate storage occurs when a trial uses an interac- tive voice response system (IVRS) to randomize the subject or assign a treatment via a kit number. The IVRS will store responses and the assigned treatment group in its database system, and the CRF or eCRF design may also require that the assignment be recorded so it is also stored in the clinical database. Reconciliation between the CRF and the IVR system is a very good idea—in a trial of any size, expect that some of the information will not match. In fact, it is very important to know if the IVRS assigned one kit number but the site provided a subject with a different kit number (for whatever reason). Companies have also found themselves reconciling against paper in cases where sites are asked to provide additional information to the medical monitor for adverse events being reported more frequently than expected. That is, when the medical", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "SOPs FOR AE DATA", "definition": "Good process for managing safety data cannot be emphasized enough. Normal data management SOPs will cover most activities except SAE reconciliation and AE/SAE coding. SAE reconciliation involves the safety group, the medical monitor, and clini- cal operations in addition to data management, so SAE reconciliation SOPs should be coordinated and signed-off by every group involved in the process. The proce- dures should clearly spell out responsibilities for the steps in SAE reconciliation, including providing database listings, safety systems listings, discrepancy reports, site queries, and data updates to both systems. Traditionally, reconciliation has only been done after all the data has been col- lected, before study lock. As we see a higher expectation that companies be aware of possible safety problems with their treatments, most companies are going toward more frequent, even monthly, SAE reconciliation for Phase II and III studies, with final reconciliation again at study lock. The sheer volume of SAE reports from a Phase III study with seriously ill subjects will necessitate processing them through- out the course of a trial in order to keep up, but a Phase I trial may have no SAEs at all. No matter when reconciliation takes place, evidence of the reconciliation must be in the data management or clinical files. Ideally, a medical monitor signs off on the final reconciliation prior to lock, if not the intermediate ones. The data manage- ment plan is often the place where the frequency of reconciliation is recorded for a given study. The SOPs and guidelines governing coding will be presented in Chapter 26. If coding is performed by different groups in the two systems (clinical and safety), then it might be necessary to have two SOPs. IMPACT OF AEs ON DATA MANAGEMENT While adverse event data is, in many ways, like any other data collected during a clinical trial, it is critical to the evaluation of the safety and efficacy of the treatment. In particular, adverse events add coding of reported terms and reconciling of serious adverse events to the data management process of a study. Both of these tasks tend to be particularly active as close of the study nears. Reconciling, in particular, may not be possible earlier in the course of the study, and even if performed earlier, will have to be repeated at the end of the study. The effect then, of adverse event data as a whole, can be to impact the close of a study. Data managers may not be able to change this, but they can be aware of it and plan for it. When sign-off on coding and SAE reconciliation is required to lock a study, data management must notify the medical monitor or responsible party that his or her attention will be required in order to avoid surprises and/or delays in lock. 113 12 Creating Reports and", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Transferring Data", "definition": "Data management is frequently responsible for producing reports or listings of study data for internal staff and management. Some of these reports are standard repre- sentations of the data, which are run over and over on current datasets. Other reports are ad hoc reports—that is, the format and content are requested by a user of the data for infrequent or one-time use. Users of standard or ad hoc reports may be data management staff themselves, clinical research associate (CRAs) for medical review, management to oversee trial progress, QC auditors, and so on. The level of effort devoted to the creation of these reports should depend on the users and the use to which they will put the report. Transfers of data to internal or external groups may also fall to data manage- ment. For example, a small company may require the transfer of data to an external statistician for early or final analysis. In other cases, the transfer may be to a partner company or to a client (as in the case of a contract research organization [CRO] pro- viding data to the sponsor). Because transfers of data nearly always involve safety and efficacy data for storage or analysis, the level of effort devoted to a transfer must be much higher than for most reports. The layout of a report or the format of a transfer file is not its most important attribute—the content is. Users must know where the data comes from, how it was selected, and when it was extracted. Ideally, all of this information is included in, or otherwise evident in, all reports and transfers created by data management. Then, of course, the contents must actually reflect what was specified and this is where testing and validation come in. SPECIFYING THE CONTENTS The specification of what data is to be included in a report or transfer can be simpli- fied by asking: “From where?” “Exactly what?” and “When?” From Where? The study or studies from which the data is extracted identifies where the data comes from. The study or protocol information should appear, at least, in the header or notes of a report and should be somehow firmly associated with the data (via a file name, for example) when the data comes from a single protocol. If the report or transfer contains data from across studies, then the study identifiers should appear with the associated data as a field or column. While the inclusion of this information seems 114 Practical Guide to Clinical Data Management, Third Edition obvious, it is often left out of ad hoc reports under the assumption that the report is being run over a single set of data anyway. In the case of transfers, it is a common problem when multiple datasets are shipped as a group and are identified in a cover memo, and then the identifier is lost when the datasets are split up or reorganized. Exactly What? The selection criteria information used to extract the data is critical to its interpreta- tion. This is especially true if the variables or fields used in the selection criteria are not actually included in the report or transfer. For example, if a listing contains only data from male patients, the patient’s gender might not actually appear as a column in the listing but would impact any conclusions drawn from the data. The selection criteria should not disappear from the final report or data. For reports, the information might appear as text in a report header, title, or footnotes. Including selection criteria information in transfers is more difficult because transfer files fre- quently do not have titles, headers, or other places to store comments pertaining to the file. To be totally clear, the data fields used in the selection might be included as variables or columns in a listing or transfer even if they appear to provide redundant information. That is, in our example, include a column for “sex” even if all the values are “M” for “male.” Another option is to include the information in a transfer file (described further in the following text) that accompanies a data transfer. In addition to the explicit selection criteria, there are usually some hidden or implicit criteria or conditions applied to the data. These hidden selection criteria frequently have to do with the cleanliness of the data. A simple selection of all the demographic data in a database for a paper study, for example, could well select data that has only gone through first pass entry and still contains transcription errors. It would also very likely contain data that has not yet been cleaned and may not even have had edit checks run over it yet. In the case of electronic data capture (EDC) systems, a complete extract of that data would likely include data that has not yet been monitored. The specification or requirements of every report, listing, or transfer should clearly state what data is acceptable for the particular use. When? The date the report or transfer was run is important and obvious. In the case of transfers, it can sometimes be important to know the date associated with the data- set. This is becoming more important with the use of data warehouses. Typically, data is copied from the source clinical database or EDC system into the data ware- house on a regular basis but not constantly—the data in these two locations are not concurrent. If a report is run on data warehouse data, it might not reflect the current data in the source database, and the bigger the lag, the more important it is to tag the data with the appropriate date. This problem also shows up in EDC systems where electronic case report form (eCRF) data is transferred from an EDC vendor to the sponsor, and the sponsor then runs reports on snapshots of data that can be a week or more old. All reviewers need to understand that sites may have updated data shown in those reports. Creating Reports and Transferring Data 115 STANDARD AND AD HOC REPORTS Some reports can be well defined and are used over and over either within the con- duct of a single study or across studies. Examples of this kind of report include patient accrual, lists of outstanding discrepancies, and data dumps of lab data. These reports can be considered standard reports. Other reports are used infrequently or are created to deal with a particular data problem or need. An example might be listing of all subjects from whom a particular field is empty. These reports are fre- quently called ad hoc reports. Ad hoc reports are built to answer an immediate question or need and are typically not designed to work in other situations and so are quickly developed. The level of effort put into the creation of standard reports tends to be higher, since the intent is to make them generally useful. In both kinds of reports, careful thought must be given to how the report will be used to determine the level of validation necessary. If either a standard or ad hoc report is to be used to evaluate or make a decision concerning the safety and effi- cacy of a treatment, then it must be subject to validation. If a report is to be used for mainly administrative, tracking, or data management review purposes, it should still be properly designed and tested but would not need a full level of validation (see the following text). Some reports may be used in ways that do not clearly fall one way or the other and require a judgment call to determine the extent of validation necessary. A good rule of thumb is to assess the risk of getting it wrong. Let’s say a report fails to list some specific data; what would the outcome be of not having that data? Would the information be caught in another way or at another time? Would it make any dif- ference to the outcome of the trial or the analysis of the data? If the risk is low, the level of validation can be low or lower than for critical reports and transfers. Even when validation is needed, the level or extent of the validation effort should not exceed that needed to build the report. One possible validation approach for important, standard reports would be to require the following: •\t A short (<1 page) specification of what the report will contain or present (specification) •\t A description of the tool or tools to be used and the logic used in selecting data for the report (technical documentation) •\t Sample or mocked up results or output (technical documentation) •\t A summary of test data used and output of tests run (testing) •\t If appropriate, a short description of how to use the report or make it run and any limitations or assumptions (user documentation) •\t A restriction on changes or a change control process All standard reports would have to meet these requirements; the more critical the report, the more effort is put into the validation effort. Obviously, the output of the validation would be stored in an appropriate file. (See Chapter 23, “System Validation,” for more details on validation approaches and requirements.) Note that the validation effort for these reports does not include validation on the tool or software package being used to create the report. Any validation deemed necessary by the company and the associated general testing of such tools should 116 Practical Guide to Clinical Data Management, Third Edition have taken place at the time of installation. The validation effort for reports focuses on the correct or appropriate use of the tool or application. Contrast the validation effort for important reports against the process for creat- ing ad hoc reports: ad hoc reports might only require a brief specification and result of testing before being used on production data. The testing might even take place on real data, but someone, typically the creator, would confirm (manually) that the appropriate data was included and that the output was correct. While the creators of ad hoc reports may still follow good process (including asking for specifications and checking the results), few companies require that documentation on ad hoc reports be kept on file and they permit changes as needed.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "DATA TRANSFERS", "definition": "Transfers of data are different from reports in that the data is copied and sent else- where (either within or external to the company) to be analyzed, reviewed, and reported on. Transfers of data nearly always involve or include some safety and efficacy data. Because of this, data transfers should be guided by the requirements of 21 CFR (Code of Federal Regulations) Part 11 for validation of software and for verified copies. There are two elements to ensuring accurate transfers. The first is to create the extract program that pulls the data out of whatever database it resides in and puts it, perhaps with some reformatting, into a target file. This extract program or script must be validated to show that it works properly and that it creates an accurate copy during testing. The second element of transfer is the sending of the resulting file or files. The copy of the data is made into a target file or files that are then transmitted to some receiver. In addition to requiring evidence that the copy is accurate, 21 CFR Part 11 requires extra security if the data goes into an open system such as the Internet via email. (See Chapter 10 for further discussion of sending and receiving data by electronic files.) Two techniques for ensuring accu- rate copies and secure transfers are the use of transfer checklists and the creation of transfer metrics.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Transfer Checklists", "definition": "Even if the transfer is a one-time occurrence, a checklist of the steps needed to produce it helps assure that nothing is overlooked. If the transfer is repeated dur- ing the course of a study or studies, the checklist is essential to assure consistency and completeness at each transfer. The checklist should be created before the very first transfer, even if all the steps are not known until a few test runs have been completed: the act of documenting before doing helps point out undefined areas and allows cross-checks to be built in from the start. Figure 12.1 illustrates the steps that might be in a checklist for transfer from a computer to a CD. Because a CD is used in the example, the compressed files are not password protected. Files sent via email would need to be protected or encrypted. In this particular checklist, the person overseeing the transfer manually creates a trans- fer file and copies in the data metrics (see text that follows). A much better approach Creating Reports and Transferring Data 117 would be to have the extraction programs or scripts create all or nearly all of the information required for the transfer. Just creating the checklist as a tip sheet is usually not enough to make sure all the steps are followed. Even the most conscientious data manager may overlook a step. Guidelines that require printing out the checklist and initialing each step on comple- tion usually help ensure that all steps are followed. The checklist provides excellent documentation for the transfer and can be filed with a copy of the transferred data.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Transfer Metrics", "definition": "Transfer metrics are numbers that help verify that the data was completely extracted to the transfer file(s). Exactly which metrics will be useful for any given transfer will depend on the format and number of the transfer files and on the type of data being transferred. Some of the common transfer metrics include: •\t Number of files •\t File sizes •\t Number of subjects per file •\t Number of records per subject •\t Number of records per table or file Some companies also create a checksum (a number generated by an algorithm that is unique to the contents of the file) for each file. Checksums can help detect corruption of the contents of the file. Data managers review these metrics once the transfer files have been created to get a sense of whether the transfer program put the correct data in the file. This is use- ful even if the data manager does not know exactly how many subjects or records to", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Complete", "definition": "Create transfer directory Run program to extract data Review metrics generated by the extraction program Start a metrics file for the receiving group; copy in values Compress all transfer files Add compressed file sizes to metrics file Print out metrics file Copy all files (including metrics file) to two CDs Check both CDs on another computer for readability Store one copy; send the other CD along with a paper copy of metrics file CD sent by:                                                   Date: FIGURE 12.1  An example of a data transfer checklist. This example assumes the data is copied to transfer directory and is then written to a read-only CD. 118 Practical Guide to Clinical Data Management, Third Edition expect. The user receiving the transfer can also benefit from these same numbers to determine if all the data was properly loaded after receipt. Because the numbers are useful at the receiving end also, they can be sent along with the transfer in an infor- mation or metrics file. The metrics file can also provide a convenient place to docu- ment the selection or extraction criteria, the date the data was extracted, and any other assumptions or special conditions in effect at the time the transfer file was created. QUALITY CONTROL REVIEW OF PRINTED REPORTS AND PRESENTATIONS At some smaller companies, data managers are involved in quality control of reports created by other groups, such as tables in the study report, slides to be presented to upper level management, or data to be included in articles. They are asked to manu- ally check any raw data values (not statistical results, of course) before they are made public or presented. They look for appropriate selection of variables (for example, those in standard units rather than the raw values from the CRF) and scan the data to see if it fits with their knowledge of the study. The idea here is that data managers are the most familiar with the data and so can most easily check its accuracy. For small companies, this may make sense, but even then processes must be in place to assure that following: •\t The request is placed with enough lead time to carry it out. •\t The data is in fact updated if a data manager sees a problem. •\t That data is rechecked if the report or presentation is substantially changed. SOPs FOR REPORTS AND TRANSFERS At a minimum, companies should have specific standard operating procedures (SOPs) and/or guidelines that spell out the validation requirement for reports and the quality control or verification of transfers. The procedures need to address docu- mentation and testing requirements for the different kinds of data contained in the reports or transfers and the different uses to which they are put. PUTTING IN THE APPROPRIATE EFFORT Companies do not have unlimited resources, and management must make judgments for where to assign such scarce resources as database programmers or technical data managers. Because transfers contain data on which decisions will be made, the resources to validate the programs and verify and check transfers nearly always have high value. As just about everyone who has been the recipient of CRO or lab data knows, when a transfer goes wrong or is missing data it causes no end of trouble because the trouble does not show up until the data goes further “downstream” as the it is used by programmers and statisticians. Backing it all up and getting a fresh, correct transfer irritates everyone involved and has a serious impact on timelines. Creating Reports and Transferring Data 119 Companies should also take the validation of critical and standard reports seri- ously. Validation does have value by itself in improving the quality and ease of use of reports—not just value in meeting regulatory expectations. That being said, we do still see overkill in validation efforts undertaken for low-risk reports. Companies must set reasonable requirements and allow some variation in how the requirements are met to avoid making validation a bigger effort than writing the application.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Study Closeout", "definition": "As the date for the last subject visit—most commonly referred to as last patient out (LPO) or last patient last visit (LPLV)—approaches, study closeout activities begin. Study closeout includes final cleaning and review of data. When the data is deemed clean and complete enough for analysis, the database records are locked against any changes. Database lock is the trigger for the data to be unblinded and extracted for analysis. After the database lock, additional activities complete the study closeout. For electronic data capture (EDC) studies, one of the most important activities is to create and distribute copies of the electronic case report forms (eCRFs) to the sites and to prepare a version for inclusion in the trial master file. 123 13 Study Database Lock As the last subjects near their final visit, the race to close and lock the study begins. Locking means that no data will be changed; a locked database defines the point at which final analysis can start and conclusions can be drawn. Because there is usu- ally high pressure to make those analyses (and related decisions) as soon as possible, companies frequently keep track of the time to database lock as a corporate metric and work constantly to minimize that time. The pressure to quickly lock a database for analysis comes up against a long list of time-consuming tasks that need to be per- formed first. The list includes many individual steps, including: collecting the final data, resolving outstanding queries, and performing final quality control checks. In this chapter we will look at the most common steps performed in preparation for study database lock in both paper-based and electronic data capture (EDC) stud- ies and address some ways in which the time to study lock can be reduced. The next chapter discusses activities that happen after the database is locked and touches on what needs to be done if a data change is needed after lock.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "FINAL DATA", "definition": "Before a study can be locked, all the clinical data generated by the study must be present. The data, first and foremost, is the original data from the subject reported on case report forms (CRFs) or through electronic CRFs (eCRFs), but there is other data as well: corrections from the sites, calculated values, codes for reported terms, data from central labs, and any other clinical data from external reading centers. Any of this final data may generate discrepancies that will require resolution before study lock. To account for all the original data, data management uses tracking information to ensure that all expected CRF or eCRF data has been received; there should be no missing pages in a paper study or empty forms in an eCRF study. In addition, the data manager or lab data administrator checks that all central laboratory data was received and that any other electronic loads are complete. Once in the central data- base, this data will go through the cleaning process, which may generate discrepan- cies. (See also Chapter 10, “Non-CRF Data.”) As the final data comes in, the final calculated values also must be derived. Discrepancies raised by calculated values are usually traced back to problems with the reported data and may have to go back to the site. All reported terms (such as adverse events and medications) must be coded and any changes to terms that come in as corrections must also be rerun through the cod- ing process. When a term cannot be coded, a query may have to be sent to the site, but close to study lock, some companies will permit a medical monitor or clinical research associate (CRA) to make limited corrections to reported terms to allow 124 Practical Guide to Clinical Data Management, Third Edition them to be coded. Just to be sure everything is in a final, coded state, many compa- nies rerun coding over the entire set to catch cases where the assigned code changed due to a change in the dictionary or synonyms table and cases where the term was changed but the code did not receive an update (see Chapter 26 for more information on the coding process).", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "FINAL QUERIES", "definition": "Resolutions for new discrepancies identified as final data is collected, as well as those queries and discrepancies still outstanding from earlier in the study are also required for completeness of the data. Generally, all outstanding queries must have a resolution before a study can be locked—even if the resolution indicates that a value is not and never will be available. Getting these last resolutions and the required investigator signatures from the site can hold up the entire closure process, so CRAs frequently get involved in calling or visiting the sites to speed corrections. Because of the difficulties and time pressures at the end of the study, companies may choose not to pursue noncritical values at this stage of the data handling. Ideally, the list of critical values will have been identified at the start of the study in the protocol or data management plan and can be referred to when faced with getting a resolution from an uncooperative site right before study lock. Some companies call the point at which the last CRF data comes in from the site soft lock or freeze, but most companies wait until the last query resolution is in to declare a soft lock. In either case, this is the point at which the real work of assuring quality begins. The data is not locked yet because there may still be changes that come out of the quality activities, such as database audits for paper studies or final data review for any study, but the number of changes is expected to be small. The data is in a near-final state.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "FINAL QUALITY CONTROL", "definition": "The quality of the data will affect the quality of the analyses performed on the data. At the close of the study, there is a particularly strong emphasis on checking the quality of the data that is about to be handed over to a biostatistics group. Because there is, or should be, a high barrier to getting a study unlocked, it is worth making an effort to check the data thoroughly. All kinds of review of the data help provide assurance as to its quality and correctness, but study closure checklists frequently include these specific kinds of checks: •\t Audits of the database •\t Summary reviews of the data •\t Reconciliation against other systems", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Database Audits", "definition": "Data transcribed from a paper CRF or other source into the database is usually checked for accuracy through a database audit. Data managers compare data in the", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Summary Review", "definition": "There are certain kinds of cleaning or discrepancy checks that are better performed near the close of a study when the data is more complete. These include listing reviews, summary reports, and simple analyses of the data as a whole. The goal of these reviews is to detect unusual values that stand out in the context of a full set of data but that might otherwise pass cleaning rules or other discrepancy identification methods. A listing review of text fields is a good example of how trained humans pick up inconsistencies that cannot be programmed into edit checks. In paper studies, data managers may review listings of text fields to check for nonsensical words that are introduced because entry operators are focusing on what they see rather than the meaning of a phrase. For both paper and EDC studies, a separate listing review by CRAs is often required for study lock. The CRAs may notice nonsensical phrases, but more importantly, they may find problems with protocol compliance. For exam- ple, they may review medications and find some listed that are not permitted by the protocol. Or, they may find medications listed in the medical history section. They may even find serious safety problems listed in comments associated with lab results or in adverse event reports. Humans are very good at detecting patterns or unusual values. Listing reviews of numeric values may also work for smaller studies to detect unusual values or outliers. For large studies, summary reports created from ad hoc queries or simple statistics performed on the data can identify unusual patterns or outliers by looking at the following: •\t Number of records or values per subject 126 Practical Guide to Clinical Data Management, Third Edition •\t Highest, lowest, and mean for numeric values •\t Distribution of values for coded fields (e.g., how many of each code) •\t Amount of missing data These summary reviews can be run by data management staff, but in some com- panies, clinical programmers will look at the data using SAS®. Graphs of lab and efficacy data or other simple displays or analyses can also identify possible problems with units, decimal places, and different methods of data collection that might not otherwise be caught by simple cleaning checks. These graphs and list- ings will probably come out of the programming or statistical group. In the end, the best review of the data is to run the planned analysis programs on the data even before it is locked. The goal is to have no surprises when the final programs run!", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Reconciling", "definition": "In the best case, clinical data is stored in a single location and extracted for review or analysis from one location. However, in the setting of a drug or device trial, it is not unusual for some data to be stored in more than one location—and for very good reasons. When this is true, reconciliation may be necessary to assure consistency between the systems. The most common reconciliation with external systems is for serious adverse events (SAEs). Data on SAEs is typically stored in both the clinical data management system and also in a separate SAE system. When reconciling at study close, data management staff look for the following: •\t Cases found in the SAE system but not in the clinical data management (CDM) system •\t Events found in the CDM system but not in the SAE system •\t Deaths reported in one but not the other—perhaps because of updates to", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "the SAE report", "definition": "•\t Instances where the basic data matched up but where there are differences,", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "FINAL STEPS FOR EDC", "definition": "In a paper study, a clinical research associate (CRA) visits the site, checks the paper CRF against source documents, and then gets the separated pages to the data entry center. So, by definition, if the data is in the database, it has been monitored. In an EDC trial, the data is in the database right away, but it helps to know whether or not it has been monitored in order to judge how accurate it is. Most EDC systems build in a feature that allows the CRA to mark data that has undergone source document verification (SDV). Before database lock, all the data listed in the monitoring plan as requiring SDV must be marked as having undergone SDV. The CRAs can keep an eye on this throughout the study, but a final check prior to lock is still required. All EDC systems require that the investigators sign for the data according to 21 CFR (Code of Federal Regulations) Part 11 compliant features. The study is not final until the principal investigator has signed for all of the data. It is important to understand that when a discrepancy is discovered during study closeout activities, and it is added as a query, a change to the data by the site will “break” the investiga- tor signature. (In paper studies, the investigator just signs the query form to indicate knowledge of the data change.) The investigator has to re-sign. Because of this, all EDC studies need a check for investigator signature prior to locking. (See Chapter 8 for more information on principal investigator signatures in EDC studies.) USING A CHECKLIST TO LOCK A STUDY The final data collection and final cleaning steps are all critical to ensuring the qual- ity of the data. A checklist of procedures that must be completed prior to database lock is a standard tool in data management organizations. Generic checklists can be easily applied across studies and even across companies (see Figures 13.1 and 13.2 for examples); the more specific the list, the more valuable it is. If the database sys- tem or integrations with the database impose a certain order on the closeout proce- dures, reflect that in the database lock checklist. Such a checklist can be included as an attachment or appendix to the standard operation procedure (SOP) for study lock. Consider allowing the checklist to be modified to include study-specific elements such as sample data for a specialized substudy. Checklists work better when they require action by the user; just printing a list of steps to be completed prior to lock is not as effective as requiring a data manager to initial and date each step. (The date also provides useful information on the time required to complete each step, which can be used in planning future locks.) Most 128 Practical Guide to Clinical Data Management, Third Edition", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "All CRFs received", "definition": "All CRFs entered and verified All external data received All external data reconciled", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "All data coded", "definition": "Coding reviewed and approved SAEs reconciled and approved All queries resolved or closed Site permissions set to read only Approval to lock obtained All records marked as locked", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Initial and Date", "definition": "Sites have filled in all necessary forms SDV marked as per study requirements All PI signatures present All external data received All external data reconciled", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Request site PDFsa", "definition": "a\t See Chapter 14 for discussion of postlock activities for EDC. FIGURE 13.2  An example of a generic CDM lock checklist for an EDC study. A study- specific version of a lock checklist would list all the sources of external data individually or add steps to disable specific integrations being used to transfer data.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "SETTING DATABASE LOCK", "definition": "Once all of the items in the study lock checklist have been completed, the data man- ager obtains permission to lock the database. Typically the data manager signs to attest that the data is complete and accurate, the clinical project manager or study team leader signs to indicate that site activities are complete, the medical monitor signs to indicate that the medical content is accurate, and the biostatistician signs to indicate that the data is ready to evaluate. The signed and dated lock form should always be filed in the study file. Final data extracts for analysis should never have dates prior to the approval for lock. Locking involves setting the database records so that no further changes can be made. In the classical clinical data management systems, access for the internal users who enter and update data is revoked but read access to allow extraction remains. In EDC systems, the site users and the sponsor or contract research organization (CRO) users retain access to view the data but can no longer make changes or add queries manually. It is important to allow site users access to see subject data until they receive archival copies after study closeout (see Chapter 14). In CDM database systems, when permissions are removed for locking data, it usually means that the entire study must be locked at the same time because it gener- ally is not possible to remove permissions from individual subjects or from subsets of the data. If incremental locking is required, the records must be tagged in some way and functions built to prevent changes once the tag is set. In contrast, most EDC systems have ways of locking individual subjects and records built into the function- ality of the system. TIME TO STUDY DATABASE LOCK Time to study lock is one of the key data management metrics. It is also the one metric that gets a lot of attention from other groups as the study lock will show up on their timelines and goals also. Clinical study teams typically measure time to lock from the time of the last subject’s last visit, and often the goal is a set amount of time for all studies—often 6 or 8 weeks. However, there are so many factors that impact how quickly a study can lock that the time to lock will be shorter or longer through no fault of the clinical study team or data management staff. EDC versus paper is one impact; EDC studies can usually lock faster because there is no delay for data entry and the query resolution time is much reduced. Also, EDC studies do not require the database audit common for paper studies as a final quality control check. Note that some EDC system vendors used to say (and perhaps still do) that it is possible to lock studies in their systems one day after the last subject attends the last visit. Using the typical definitions of locking, this is not likely for any study. Discrepancies are raised (and many resolved) during entry of the data at the site and the data is also available for review immediately, but most companies still do require that that data be monitored. After monitoring (or in parallel), all the other 130 Practical Guide to Clinical Data Management, Third Edition activities of a study lock previously discussed still have to take place. In particular, any samples taken from the last visit, such as blood draws, still have to be sent to the laboratory, analyzed, and either included in the CRF/eCRF or sent to data manage- ment from the central lab. Because the time allotted for lock activities must include the CRA’s monitoring visit and time to resolve queries that result from the final data, the amount of “final data” is an important consideration in creating a lock timeline. If subject enrollment was climbing to the end of the study, there will be a large influx of new data and new queries as the study ends. If enrollment was tapering off and only a few subjects are still outstanding, the effort for monitoring and data entry (for site or sponsor) is much less. In a similar way, if data collected in final visits for all subjects is sparse, as fol- low-up data might be, the impact will be smaller than if the last visit has key efficacy measurements. Another kind of final data is results of assays on samples collected at the final visits. In cases where the assay takes time, as is often the case for pathogen identification, those data will hold up lock. If it takes two weeks to grow and identify what is in the sample, then that might become the gating factor to lock. QUALITY ASSURANCE AROUND LOCK Because lock is such a critical step, not just for the one study but for the overall project, the data must be as good as it can be. The two most powerful techniques for ensuring that a study database is ready to lock are 1.\tFollow a checklist so no step in the process is overlooked. 2.\tRun draft versions of analysis programs over the data prior to locking. The checklist, ideally with signature at the end, is a critical tool in ensuring con- sistency across study lock. Running the analysis programs before the data is locked (on data that is still blinded for blinded trials) is the only way to make sure there are no obvious surprises in the data that would require follow-up with sites. As we will see in the next chapter, unlocking a database and going back to sites for updates or new data is to be avoided! SOPs FOR STUDY CLOSEOUT An SOP for study lock should be considered high priority for any clinical data man- agement organization. The SOP should clearly repeat the minimum requirements for locking. In some cases, the list of steps to be taken before lock (lock checklist) will appear in the SOP, perhaps as a form. In other cases, the SOP will simply require that a study-specific checklist be used, and perhaps set minimum requirements for that list. In that case, the data manager would create the study-specific checklist as the first step in the study lock process. As noted previously, the final approval for data- base lock should come from an appropriate set of functions involved in conducting the trial. The signature should never be from clinical data management alone. Many companies write the lock SOP without ever thinking about the unlock pro- cess and then flounder when an unlock is needed. A lock SOP can address unlock", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "COMPLETE STUDY FILES", "definition": "The lock of a database is such an important milestone in a study that staff will push hard to meet the deadline and then breathe a sign of relief because the bulk of the work is done. Before everyone moves on to new projects and forgets details, data managers should allocate time to make sure the study documentation is complete, submit items to the trial master file, and record feedback from the study. Most data management groups try to keep the data management study file reason- ably current, but it is a very good idea to have a study file audit shortly after lock. The lead data manager should check files to ensure that all required materials and documents are present. Documents should be the most recent version (or all versions, if required by SOP) and have current signatures if required. If being stored, the list of who worked on a study and what they did should reflect the final status. This is also a good time to add notes to file to record any unusual circumstances or data issues related to the study. Some of the documents created as part of clinical data management need to be submitted to the trial master file (TMF).* For example, the data management plan is now a standard element in the TMF and many companies consider study validation documents to be good clinical practice (GCP) records (see also Chapter 5). Other documents that support clinical data management (CDM) activities, such as final tracking reports, may be filed in offsite or online storage for some number of years without attempting to permanently archive them. A postlock step to complete filing will ensure that the documents are available later should they be needed.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "ASSESS STUDY CONDUCT", "definition": "Very few companies schedule feedback meetings after a study lock. Unfortunately, people forget details and problems pretty quickly as they move on to new studies and so these problems are repeated in future studies. Right after lock is a great time to review the CRF or eCRF for fields that caused an unusual amount of trouble. Those *\t The trial master file is a file maintained by the sponsor that contains the essential documents associ- ated with the trial. Many of the required documents are specifically called out in ICH E6 GCP Section 8 (GCP), but others, such as the data management plan, are more of an industry standard. 134 Practical Guide to Clinical Data Management, Third Edition fields or modules should be modified if possible, not just reused for the next study. Similarly, edit checks that did not provide the results expected should be examined. This is also a good time to review the metrics from the study such as: •\t Total number of manual discrepancies •\t The percentage of discrepancies resolved in-house for paper studies •\t The top ten types of queries •\t Average time to resolve queries •\t Time from last query received to study lock The more information a data management group has from a past study, the more accurately the forecasts and estimates for the next study will be.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "SITE ECRF COPIES", "definition": "Section 4 of ICH E6 GCP requires that sites have a permanent copy of subject records after the study has closed for at least two years after the last marketing application or discontinuation of clinical development. EDC applications allow sites access to eCRF data until study closeout, but at some point the application needs to be shut down or be moved off a production server. At that point, the site will need access to archival copies of the eCRF data. Some companies print copies of eCRFs populated with subject data, but the more current process involves creating PDF files with audit trail records and sending those to sites on CDs. Sites are instructed to check that the disc is correct and readable and to file that disc per site standard operating and archiving procedures. Sites should be asked to return a confirmation form so that the sponsor has a record that its obligations have been met.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "UNLOCKING", "definition": "Once the study is locked and analysis begins, it is not uncommon to find problems with the data that require corrections. (This is particularly true if final quality control does not include the summary reviews of the data or draft runs of the analysis pro- grams as we discussed in the previous chapter.) New information regarding adverse event data found during site closeout or site audits may also require updates or addi- tions to the data. Because database lock triggers a cascade of other activities, unlock- ing the database has a serious impact in that many or all of the postlock activities will have to be rerun. Also, while the Food and Drug Administration (FDA) seems to accept an unlock here or there as being normal, if multiple unlocks of a critical study come to their attention, they will question the quality of the data. Unlocking, then, is a serious matter that should first be avoided by good locking practices, and if unavoid- able, unlock must be approved at a high level and conducted with care.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Avoiding Unlocks", "definition": "If, after a lock, data analysis or site closeout procedures identify data that is incor- rect, it is not always necessary to unlock the database and make the correction there.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "After Database Lock", "definition": "135 If the data is not a primary or secondary endpoint, the correction can be included in data analysis and study reports as known errors (“errata”). An example here would be a birth date that is off by a few years but not so that it would violate protocol requirements. Additional lab values that are not key variables and did not show any safety concerns would be another example. Even changes to primary and secondary endpoint data may not need to be updated if the new data would not change the result or outcome of the analysis. Significant changes to primary variables, updates to safety data including adverse events, and certainly serious adverse events (SAEs) and deaths all warrant an update to the database. This is particularly true for adverse events as it is possible that the data will be extracted independently of the clinical study reports for safety updates. If a correction were to be documented only in the study analysis or reports, it would not be included in these later extracts. Approval for Unlocking In general, a high-level approval should be required for any unlock. Often this is a senior director or vice president–level individual. The goal is to make the bar appro- priately high to avoid unlocks for administrative or noncritical data. It also serves to make senior management aware of unlocks. Too many unlocks (more than one for a trial or more than a handful across many trials) indicates a quality problem that should be addressed by senior management as a serious concern and may indi- cate the need for a review of lock procedures. The unlock approval form will also describe exactly why the database is being unlocked. The form may even require that the data or fields to be changed be explicitly specified. Unlocking for Paper Studies After appropriate signatures for unlock have been obtained, unlocking for paper studies involving a classic clinical data management system is perhaps too easy. As soon as CRFs or query forms are received to document the changed data, a database administrator restores access to internal staff. Unless the changes required are exten- sive, the database administrator will grant permissions to very few data managers or data entry staff. The kind of data modified or entered determines what happens next. At a minimum, edit checks are run and any discrepancies or queries that are gener- ated must be resolved. If some of the data is coded data, coding must occur, and if the new data includes SAEs, SAE reconciliation should take place again. Typically, the study’s data manager will follow a modified lock checklist, check the audit trail to ensure that no data was inadvertently or incorrectly changed, and then obtain approval for the new lock. After the database is relocked, the data can be reextracted and reanalyzed. Unlocking for EDC Studies Unlocks for EDC studies are more complicated because only site staff can make updates to site data. If an update to data is identified soon after the initial lock, it will 136 Practical Guide to Clinical Data Management, Third Edition probably still be possible to reactivate the site accounts. The site staff should still be there and will remember how to use the EDC system. If the problem is not identified until months (or longer) after lock, reactivating the site, identifying an appropriate user, and training or retraining as needed makes the update much more complicated than for paper studies entered by sponsor or contract research organization (CRO) staff into a clinical database management system. In fact, this is so complicated that it may not be possible to have sites make the changes. If this is the case, and if it is truly necessary to make the updates in the database, sponsor or CRO staff will have to make the change as they would in a paper trail, but this will have to be thoroughly documented in both the audit trail and in notes-to-file to ensure that there is no question of fraud! As previously noted for paper studies, the new or changed data determines what activities have to follow the updates: edit checks, query resolution, coding, and SAE reconciliation may all be required. Again, following a lock checklist after unlock is good procedure and reapproval for lock is always required. After lock, the data is reextracted and reanalyzed. For EDC studies, it will probably be necessary to re- create the eCRF archive disks that are sent to the sites.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "QUALITY ASSURANCE", "definition": "Study file audits after study lock are a quality process that ensures all the required documents are present and filed with the TMF or in other central records storage. Study feedback sessions close the loop and provide feedback that will actually enhance quality of trial conduct over time. Good lock practices will reduce the need to unlock study databases and so avoid the difficulties and resources associated with the unlock process. SOPs FOR STUDY DATABASE UNLOCK As noted in the previous chapter on lock, the procedures for unlocking a study may be combined with lock procedures in a single standard operating procedure (SOP) or may be split into a stand-alone SOP. Standard procedures for unlock should require a high-level approval prior to opening the database to changes. Those procedures should also specify that the unlock form, the relock form, and all evidence to show that the unlock updates were appropriately limited, should be filed in the data man- agement study files.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "AVOID UNLOCKS", "definition": "Unlocks should be few and far between. Good lock practices are needed to keep the number of unlocks to a minimum—but those practices should be put into place not just in clinical data management but also in clinical operations and biostatistics. Clinical data management focuses efforts on completeness and quality of the data, but that is not enough. Good practice and training in clinical operations will help ensure that monitoring is thorough to avoid missed safety or medication data. Biostatistics plays a role by being involved in defining required data cleaning and quality checks and by reviewing data using planned analysis programs prior to lock.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Section IV", "definition": "Necessary Infrastructure Without the appropriate infrastructure in place, a data management group can- not perform its work consistently and may not be complying with Food and Drug Administration (FDA) regulations. The following chapters on standard operat- ing procedures (SOPs), training, and security address regulations that specifically require written procedures, qualified staff, and restricted access to clinical data. The fourth chapter in this grouping covers data management’s interaction with contract research organizations (CROs). This interaction is also regulated, albeit less explic- itly, and occasionally subject to audit. Because nearly all clinical data management groups will use CROs, setting up guidelines for those interactions should also be considered essential infrastructure. 139 15 Standard Operating Procedures (SOPs) At nearly every training course, workshop, or seminar on standard operating proce- dures (SOPs) the question floating around in many peoples’ minds is: “Do I really have to have SOPs?” The answer is unequivocally, “Yes.” International Conference on Harmonisation (ICH) guidelines and Food and Drug Administration (FDA) regu- lations explicitly require SOPs. ICH E6 GCP states: 5.1.1 – The sponsor is responsible for implementing and maintaining quality assurance and quality control systems with written SOPs to ensure that trials are conducted and data are generated, documented (recorded), and reported in compliance with the pro- tocol, GCP, and the applicable regulatory requirement(s). (p. XX) The explicit reference to data in Section 5.1.1 means that clinical data manage- ment groups are not exempt and should not be overlooked. The FDA rule 21 CFR (Code of Federal Regulations) Part 11, which is one of the main regulations that apply to clinical data management, also refers in several places to having procedures and written policies, and the related FDA guidance document “Computerized Systems Used in Clinical Investigations” lists recommended SOPs. Given that there is no way around having SOPs, this chapter will look at what SOPs are, what data management procedures should be standardized (that is, what is the list of essential SOPs), how to develop those SOPs, and how to assure and document compliance with SOPs. WHAT IS AN SOP? A standard operating procedure (SOP) is a procedure that everyone (to whom it applies) follows to carry out a regulated task or a task where consistency is identified as a business need. SOPs define: •\t What the task is •\t Who carries it out •\t When it is to be carried out •\t How it is to be carried out •\t What evidence shows that it was carried out All companies have SOPs, and many also have other “lower” documents under a variety of names: departmental operating procedures, guidelines, working practice documents (WPD), company user manuals, and so on. The different names for stan- dard procedures reflect the level of detail they contain and the target audience. If the 140 Practical Guide to Clinical Data Management, Third Edition company is large and has several data management departments, they may have pro- cedures that are specific to their site or country. These are often called departmental operating procedures (DOPs). While it is not universally true, SOPs and DOPs tend to be written at a high level to outline required tasks, sign-offs, and checks performed without specifying details of the systems used, or the individual steps needed to carry out a particular task. It is more common to put details specific to systems, and steps detailed enough to be used as a training guide, in other documents. This kind of docu- ment might be called a work instruction or guideline. While the philosophy behind the way SOPs and lower, supporting documents are written and maintained might be different, an auditor can ask to see them all to understand how tasks are carried out and would expect that all effective procedures be adhered to. When a company uses the multilevel approach, the SOP-level procedures reflect company philosophy, regardless of underlying systems or with only light reference to those systems. They are written with the expectation that modifications will be infrequent. The specifics on how to implement those SOPs in a particular environ- ment are found in one or more group procedures, associated guidelines, or work instructions. Company-specific user manuals can provide yet further detail of how to use a particular software application to support the procedures. Guidelines and manuals are likely to be more frequently updated—in particular, as new versions of associated software systems are implemented. Here is an example of text from three different levels of procedure documents around data entry: •\t SOP: “All data from case report forms (CRFs) for paper-based studies will be double-entered.” •\t DOP or Guideline: “Data will be double entered using the third-party arbi- tration feature of the clinical data management (CDM) computer system. The first and second passes are to be performed by two independent entry operators.” •\t User Manual: “To begin third party arbitration after the data has been entered twice, open the ‘enter’ menu and select ‘arbitration’ from the drop down list.” Whether the detail is found in an SOP or in a guideline, the effect should be the same. There should be standard procedures covering all key elements of the conduct of the study. These procedures should provide enough detail to ensure they are con- sistently carried out, without providing so much detail as to end up with violations of the procedure because of normal variations in working. Also, each procedure should include a description of appropriate materials (forms, documents, checklists) that must be used or produced to document that the procedure was in fact carried out. This is the evidence needed to show the SOPs are being complied with. SOPs FOR DATA MANAGEMENT Clinical data management activities result in data that is used to make judgments about the safety and efficacy of treatments and could also be submitted to the FDA or other regulatory agencies. The result is that all key data management activities Standard Operating Procedures (SOPs) 141 should be conducted under standard operating procedures. The chapters found in the first three sections of this book discuss data management activities common to all companies and so can be used to derive a list of activities requiring SOPs. We add to that list any activities, not already covered, that deal with data covered by 21 CFR Part 11, which applies whenever data in electronic form is “created, modified, main- tained, archived, retrieved, or transmitted” to arrive at a complete list of SOP topics. These topics may be combined into a set of SOPs that suits the CDM organization in question. Appendix B contains a comprehensive set of topics compiled from a wide variety of sources and covers both specific SOPs mentioned in regulatory publica- tions and SOPs that have become expected, or industry standard. In addition to SOPs specific to data management tasks, the FDA lists recommended SOPs in its guidance on “Computerized Systems Used in Clinical Investigations.” In small companies, the data management group may need to make sure these are covered. In larger companies, these are generally covered by information technology (IT) SOPs or in SOPs developed for validation groups. In any case, it pays to be sure that some group somewhere in the company has procedures for: •\t System setup/installation •\t System maintenance •\t Data backup, recovery, and contingency plans •\t Security and Account Maintenance •\t Change control This is quite a long list of procedures for new data management groups to be faced with. Unfortunately, there is no example set of SOPs available as a starting point, and most companies keep their standard procedure documents confidential. Experienced staff can use their prior exposure to and experience with SOPs as a place to start, but companies, company philosophies, and company needs differ so much that the procedures have to be written from scratch. CREATING STANDARD PROCEDURES In small companies or new groups, data managers are sometimes faced with creat- ing a large set of procedures from scratch. (At established companies, they are more likely to revise general SOPs over time.) Both emerging and established companies have to deal with the need to write new procedures and guidelines covering new sys- tems and applications. When starting from scratch, small companies have to come up with a list and prioritize. When writing for a new system, all companies have to juggle the need to have something in place when it goes into production with the problem of perhaps not fully understanding the new system and sequence of procedures.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Starting from Scratch", "definition": "When faced with the task of creating a whole set of SOPs and guidelines, it helps to start with a vision and a plan. The data management team should start by coming up with a list of the procedures to implement—an index of sorts similar to the list 142 Practical Guide to Clinical Data Management, Third Edition in Appendix B. In reviewing the list, the group then provides a brief description of what the procedure will cover and whether it will be general or detailed. Since it is impractical to write and implement all SOPs at once, the next step is to prioritize. Since a group faced with the task of creating a whole set of procedures is likely to be preparing for an imminent study or perhaps the close of a single study, the best way to prioritize may be to begin with the SOPs necessary to complete imminent tasks. When getting ready to set up a new study, the group may prioritize the areas needed at the start of the study and those most critical to assure quality. For elec- tronic data capture (EDC), the focus would be on study database specification, build, and test then move to study conduct activities such as query management. For paper, we need a few more to get a study up and running: •\t CRF design and approval •\t Study database specification, build, and test •\t CRF tracking and workflow •\t Entering data If the group is also faced with the installation of a new system to support data management, an SOP on system validation—or at least a standard validation plan outline—is a must. An important part of a validation packet is information that is collected at the time the system is first installed and configured (see Chapter 23). It is hard to collect that information without knowing what will be required. Best practice today is to create a process flow for a task covered by an SOP before beginning to write anything. This flow should be widely reviewed before it is trans- lated into text for the description of the procedure. In fact, may companies now require that an SOP have both a process flow and a text description of the procedure. After determining a procedure, the next step is to review the company template for SOPs. (This assumes there is one; templates are available online and in books for those who are working from scratch.) The text produced should make sure that the goal of the SOP is met: Who? What? Where? When? and How? —though it should be noted that some larger companies expect the detailed How information to be in a lower-level document, as previously described. Quality professionals generally suggest that the people who actually perform the work be involved in writing or defining the procedure description—though ultimate authority in making it standard lies with senior managers. This often puts managers and the staff members who do the work in conflict. Those performing the work will write a procedure that reflects how it is being done now, or in the case of a new data management group, how those people did the work at a previous company. The man- agers, on the other hand, are often looking to write a procedure to reflect how they think the work should be performed. Managers must be very careful not to let the procedure become too theoretical. It is not a good idea to set up a theoretically good procedure and then find that the group disagrees strongly, has never seen anything like it, or simply can’t carry it out the way it is written. As the group defines the procedure, they should keep asking themselves: “How will we know people are following this procedure?” For any procedure important enough to warrant an SOP, it is worth having some kind of output, outcome, or other Standard Operating Procedures (SOPs) 143 documentation to show key aspects of the procedure have actually been carried out as defined (more on this in the next section). Procedures for New CDM Systems As we will see in the chapters on system implementation and validation, SOPs and guidelines are integral parts of bringing in a new software system. The appropriate SOPs must be in place when a new system goes into production use (at least in draft form). Any existing procedures and guidelines must be reviewed to make sure they still apply and are accurate. The problem is that internal staff may not be sure enough of how the new system works to provide the details. This lack of experience pres- ents less of a problem for SOPs written at a general procedure level than it does for detailed guidelines, manuals, or work instructions where precision counts and which are used to actually perform the work. At the time a system goes into production, the people with the most experience are those that have been responsible for the implementation, the users that tested the system, and those that have performed the pilot (if any). Reviewers of existing SOPs and writers of new SOPs and guidelines should tap into the knowledge and experi- ence of those groups. After installation, the implementation team (that has received early training) can provide enough information to create a draft procedure. If testing is performed before a pilot, the testers can provide feedback on some of the details. Ideally, by using these two sources, the procedure writers will be able to make a good draft available for the pilot or initial study that is to be brought into production in the new system. It is never wise to make an SOP effective if the process or system is so new that it has not been run end-to-end. Once the SOP is posted it must be fol- lowed; far better to work off a draft with appropriate documentation to that effect than to post an SOP that cannot be followed. One of the goals of the first study should be to try out the draft procedure. The team commits to working according to the draft procedure as much as possible and allots time in the middle or at the end of the study for reviewing and updating the procedure. If the first study is an actual, not “pretend” study, then care must be taken to document in the data management plan and/or study files that the procedures used for certain tasks were draft versions and those versions should also be retained. The goal should be to have a solid set of procedures approved before any more (or per- haps, more realistically, many more) studies go into production. COMPLYING WITH STANDARD PROCEDURES Standard procedures do no good at all—and a considerable amount of harm—if they are not followed. One way of assuring they are followed is to make sure that every- one knows what the procedures say and where to find a copy for reference, so we begin our look at compliance with a discussion of training and access. Another way to make sure SOPs are followed is to write them to assist in compliance. Finally, even if everyone is following a procedure, but there is no evidence in the form of a docu- 144 Practical Guide to Clinical Data Management, Third Edition ment or output to prove it, it doesn’t count. When writing SOPs, data management groups must be sure that the evidence is built in.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Training on SOPs", "definition": "Training is the first step in making sure staff members know the procedures. For new employees, this usually means SOP training soon after they start and before they are allowed to perform work on production studies. Smaller companies provide this training one on one; larger companies hold frequent SOP training sessions, pro- vide computer-based training, or simply require a “read and acknowledge” from the employee. Note that contractors and other temporary employees must go through the same process if they are to be involved in carrying out some or all of a particular procedure. And, as noted in the next chapter, “Training,” documentation of the train- ing must go in the employees’ training files. Despite the fact that SOP training for new hires is a standard practice, most people admit that new employees cannot be expected to remember the details of the SOPs after a one-time training. At a minimum, they need to have ready access to the SOPs when they come closer to performing the work. Making SOPs available used to take the form of providing a paper copy of the SOP and guideline set for each employee— but keeping paper copies current has always been a challenge. More recently, SOPs and guidelines are made available on company intranet websites. The websites have the advantage that they always have the most current copy of the SOPs and can sup- port additional notes, comments, templates, and examples. Training will also need to take place whenever an SOP undergoes a major revi- sion. Usually, the manager or trainer will focus on the difference or new procedures in the revised document for current employees. However, trainers often find it more practical to just review the entire procedure, and this has the added benefit of giving everyone a refresher course on what they should be doing. Because it can take days or weeks to train everyone on revisions, most companies have notification systems that build in the lag time needed for training before the SOP actually becomes effective. That is, everyone is notified that a revision has been approved and that it is ready for training, but that new procedure does not become effective (meaning people have to follow it) until sometime after the approval date to allow everyone to complete training. Designing for Compliance Even training and ready access still cannot guarantee compliance. The best guaran- tee of compliance is to write SOPs and guidelines that actually can be followed. The following characteristics, when found in an SOP, will often lead to noncompliance: •\t Too-tight timeframes: Be sure to allow enough time for the natural and appropriate workflow. Can you really get a signature within one day? Do you really have to have sign-off on all edit check specifications before any programming can begin? Perhaps it is sufficient to have sign-off before edit checks can be run in a production environment. Standard Operating Procedures (SOPs) 145 •\t Too many signatures: Many companies like to share the responsibility for key documents by requiring broad review and approval. For example, CRF or eCRF approvals often involve all core members of the study team. But the key purpose of the signature should be how it improves the quality of the document. If drug safety data collection is standardized, does the drug safety representative really have to review and approve CRFs/eCRFs for all studies? •\t Overspecified details: Do notations really have to be done in green ink or is any pen color other than black acceptable? Does a batch transmittal form always have to have initials by the clinical assistant who opens the mail or can it just be whoever opens the mail? •\t Documentation produced after the fact: Asking for burdensome documen- tation after a particular task has been completed is asking for trouble. This is especially true if the work has to be performed after study lock. It is much better to require only documentation that fits naturally into the process and actually adds value to the procedure rather than trying to document some- thing that happened in the past. For example, don’t manually create and sign a list of subjects who were locked for an interim analysis; produce a list of locked subjects from the system or create a list of all subjects from the system and verify which ones were in fact locked. •\t Overuse of the words require and will include: If something is required by an SOP, it had better be there during an audit. Don’t require documentation, forms, or output if it is not always part of the process. If something doesn’t always apply, then recommend it or specify it as may include. (Conversely, don’t use should or may when you mean must.) If you write a procedure and it turns out not to work in practice, record this outcome (also known as a deviation) either according to company policy or as a study-specific note to file. If the same deviations happen over and over, arrange for a revision of the procedure as soon as possible. Not following a standard procedure may actually be viewed by an auditor as worse than not having a written procedure!", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Proving Compliance", "definition": "As many people in the field will say, “No SOP, no GCP” and “If it wasn’t docu- mented, it wasn’t done.” If a company has great SOPs but there is no consistent evidence that the procedures were followed, then for all intents and purposes, the procedure wasn’t followed. Sometimes this statement is taken a bit to the extreme because there is often intrinsic evidence that a procedure was followed and explicit evidence is not always required. Still, whenever a group is working on an SOP, they should ask themselves what the proof would be if an auditor came in and said, “Prove to me you do this consistently.” For SOPs guiding particularly important processes, such as serious adverse event reconciliation or locking a database, it is a good idea to have clear documentation that all required procedures were followed. The best evidence of compliance is something that is used in carrying out the pro- cedure specified in the SOP (an SOP tool), or something that is naturally produced 146 Practical Guide to Clinical Data Management, Third Edition as output in carrying out that procedure. The idea should be that whatever you file as evidence of compliance should also be helping you carry out the procedure. For example, when an SOP has many steps or steps that take place with a time lag (such as might be found in study lock procedures) consider using a checklist. That check- list not only provides great evidence that the steps were carried out but also helps to keep staff members from inadvertently missing a step. As an example of a natural output from a procedure, consider using the annotated CRF as evidence that the designer followed the procedures for SOP on database specification. That annotated CRF is hugely valuable to anyone working with the database and is not extra docu- mentation provided to show how a database was designed. (A printout of the data- base structures completes the picture and can provide a quality control [QC] step if compared to the annotated CRF.) Less useful, but generally acceptable, types of evidence are the common sign-off forms we find associated with many procedures in data management. These usually have a title such as “Study Lock Approvals” followed by a list of people, identified by position, who have to sign off to approve a process in advance or to attest to the fact that the procedure was carried out. These are really not tools, they are just evidence. In using them, we have to rely on the idea that the signers were diligent in making sure all the necessary steps were, in fact, carried out. HOW DATA MANAGEMENT SOPs ARE DIFFERENT FROM CLINICAL SOPs Embedded in the previous discussion is an important characteristic of data manage- ment SOPs that is not true of many other procedural SOPs such as the ones devel- oped mainly for clinical operations staff: CDM SOPs are typically system dependent. While it is possible to write a site monitoring SOP that could be used from one company to the next, it is not usually possible for CDM SOPs. Most CDM and EDC systems impact the order in which certain tasks are carried out and they use specific terminology that finds its way into the SOP. For example, we have seen one CDM software system that calls the temporary restriction on data changes imposed dur- ing data cleaning a freeze and the final database lock a lock; another system used by the same company uses the terms in reverse! Similarly, some systems may require a freeze-like activity prior to locking and others may make that activity optional. Even SOPs written at a fairly high level will have to have a step that says freeze the data after which some other activities take place before it says to lock the data. Finding the right balance between writing an SOP that has useful levels of detail, while keep- ing the system-specific items to a minimum, is a real challenge. The impact of the system on the process means that most typically there will need to be some SOPs specific to paper studies and some specific to EDC studies.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "SOPs ON SOPs", "definition": "Yes, there should be an SOP on SOPs! This procedure is usually developed at the corporate level and typically would contain references to the required sections of an Standard Operating Procedures (SOPs) 147 SOP or point to a company template. Ideally, the SOP on SOPs would also give some guidance as to what processes are documented in SOPs as opposed to in guidelines or work instructions. Many SOPs on SOPs do not include the process for approval and how to determine the needed signatures. This is unfortunate as it can result in inconsistencies on approvals for SOPs developed by different groups. There will always be cases where the process cannot or was not followed, so the SOP on SOPs must also include a process to follow for deviations and prospective waivers. Finally, the SOP should require review of each SOP within a period of time (typically on the order of two years) from the time it becomes active—which leads us to the next topic: work on any given SOP must always be considered to be ongoing.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "SOP WORK NEVER ENDS", "definition": "Let’s imagine that a data management group has all the SOPs in place that they feel are necessary. About that time, if not before, they are going to have to start at the beginning and review each of them. Procedures change, so the SOPs that govern them must be updated regularly. Systems change and SOPs have to be reviewed to see if the software has forced a change to the documented procedures. Regulations change and FDA guidance documents change; SOPs may need to be updated. Industry practices also change over time and it is worth asking if a pro- cedure perfectly acceptable five years ago is now falling out of the main stream of practice within the industry. Or, perhaps a new procedure has shown itself to be effective. SOPs are definitely not static. Most auditors expect SOPs to be reviewed at least every two years or so, and they may well be suspicious if a document has not changed in more than two years. Once SOPs are in place, data managers are understandably reluctant to open them up again during a review. Assessing the deviations requested for an SOP is a good place to start the review. Even when the procedure appears to be correct, the associ- ated tools, forms, or other evidence of SOP compliance can be revisited to see if a change can improve efficiency. In general, review should be a time to keep minds open and make the SOP work better for the data managers following it. 149 16 Training Just as standard operating procedures (SOPs) are required as infrastructure, so is training of staff—or perhaps more significantly, documentation of that train- ing. Training is listed explicitly in the regulations, as we see in the Good Clinical Practice (GCP) Section 2.8, which states: “Each individual involved in conducting a trail should be qualified by education, training, and experience to perform his or her respective task(s).” And 21 CFR (Code of Federal Regulations) Part 11 echoes this in Section 11.10, which says that the procedures and controls related to maintain- ing electronic records will include: “(i) Determination that persons who develop, maintain, or use electronic record/electronic signature systems have the education, training, and experience to perform their assigned tasks.” Perhaps even more interesting than the text of the actual regulations are the com- ments from the Food and Drug Administration (FDA) found in the introduction to 21 CFR Part 11. On pages 13450–13451 and 13464, the agency responds at length to comments sent in by the industry during the review phase of the rule. They use the responses to questions regarding training requirements to emphasize that the FDA believes training specific to the task is required, and they conclude by saying: The relevant education, training, and experience of each individual involved in devel- oping, maintaining, or using electronic records/submissions must be documented. However, no specific examinations or credentials for these individuals are required by the rule. (p. XX) Because data managers use clinical data management systems, they are creating and maintaining electronic records that will be used in submissions. They will have to document their training and experience. This chapter discusses ways to satisfy these training requirements, identifies some common problems in setting up train- ing, and touches on approaches to address those problems. WHO GETS TRAINED ON WHAT? The place to start with training is to decide who gets trained on what. Many com- panies have a matrix or spreadsheet of positions in the company and the SOPs on which people holding that position must be trained. While SOP training is important (see Chapter 15, “Standard Operating Procedures”), an SOP list is the bare minimum for identifying the training staff members must have. Training on an SOP will not provide training on the company’s data management system or specific workflow. In addition, the use of the title of an employee to select the SOPs often leads to over- training on SOPs for procedures some people will never perform and don’t need to know. A better approach is to define all the kinds of training available (beyond SOPs) and link that training to roles rather than titles. 150 Practical Guide to Clinical Data Management, Third Edition See Figure 16.1 for one example of this approach as applied to roles that work on paper-based studies. Note how the training table has columns not just for SOPs but also for clinical data management (CDM) system training, pertinent guidelines, and practice or tests. The SOP training applies if there is an SOP that covers work performed by the role in question. System training refers to any training required for using the CDM system or other software applications. The guidelines column lists all the guidelines that apply. Finally, the test column indicates whether there is a test, practice, or work review required before the person can perform the task on produc- tion or live data. There may be a practice data entry that is reviewed before a new entry operator can begin work, but for discrepancy management there may be no test if a reviewer just checks that person’s work for a specified period of time. Figure 16.2 shows a different approach—the SOPs, guidelines, and system courses are listed in the first column and the different roles are found in the headers of the other columns. When a role requires a specific training element, an “X” appears in the cell. This example assumes electronic data capture (EDC)-based studies. Both of the examples list guidelines as separate training components. A mistake made by many companies, including very large ones, is to create guidelines that must be followed and then just put them somewhere on a shared server. When new hires start, or when current employees take on new roles, they may or may not know about the guidelines that everyone is supposed to be following. As we saw in Chapter 15, auditors can hold a company responsible for all process documents that are supposed to be followed, whether or not they are called SOPs. If a CDM group has guidelines that must be followed (often called binding documents), then those guidelines must be trained on in the same way that SOPs are trained on. This is a critical step in embedding best and required practices in day-to-day work! A final note on roles: by training based on the role a person plays within data man- agement or even within a study, it should be clear that temporary workers and contrac- tors must receive the same training as permanent employees. Training for contractors is often neglected, but should not be. They do the work; they need the training. STUDY-SPECIFIC TRAINING While nearly all companies provide training on data management topics in general, they don’t all specifically require study-specific training. Study-specific training for clinical staff has been required for quite some time and attention is now moving to providing it for data management. There is growing recognition that studies involv- ing the same treatment, performed at the same company, using the same data man- agement system will still have differences worth pointing out. For companies with strong standards, the differences may be minimal, but in emerging companies, the differences between studies may be quite large and well worth spending an hour on. Study-specific training for data management would include a summary of the protocol and highlights of the study, any study-specific forms that may not be famil- iar from other studies, and any unusual or study-specific edit checks and listing reviews that are to be expected. For paper studies, data entry staff may be required to do some test entry followed by an opportunity for questions. Companies that use study-specific training do not grant access to work on the production study until the", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Role", "definition": "1. SOPs on: 2. CDM System 3. Guidelines Test Required?", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "First Pass Entry", "definition": "•\t CRF Workflow •\t Data Entry", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Entry Menu", "definition": "“Handling Pages with No Identifier” “Data Entry Guidelines”", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Second Pass Entry", "definition": "•\t CRF Workflow •\t Data Entry", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Verification Features", "definition": "“Handling Pages with No Identifier” “Data Entry Guidelines”", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Discrepancy Menu", "definition": "“Discrepancy Management” No, work review only", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "CRF Designer", "definition": "•\t Designing CRFs N/A “Managing CRF Files” No, work review", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "only", "definition": "FIGURE 16.1  An example of a training matrix for conducting paper-based studies. The roles identify the kinds of tasks being performed. The columns list the different kinds of training from three different areas. The final column indicates whether a formal test is required to qualify to do the work on an actual study. 152 Practical Guide to Clinical Data Management, Third Edition training has been completed. (How to document this training is discussed in a fol- lowing section.)", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "HOW TO TRAIN", "definition": "Only large companies have training groups, and even then those groups often focus on companywide training. They may train on corporate SOPs, good clinical practice (GCP) in general, and other topics that pertain to groups beyond data management. If a data management group is large enough and lucky enough, they may have a data management trainer or CDM training group knowledgeable in data management and the systems used, but it is extremely common for data management staff to be responsible for training its own staff on top of other duties and expectations. In the latter case, it may be necessary to make it a yearly goal for some data managers to provide such training support and to recognize those who do it well. If a data management group is relatively small with low turnover, one-on-one training may be the most efficient approach. While many small groups assign new staff members to a buddy or mentor for training, this has been found to lead to wide variability in the quality of training. Ideally, one person in the group who is both interested in and good at training will be the designated trainer. If the group is grow- ing, periodic formal training sessions may become worthwhile. In this case, the qual- ity of the training will probably improve and be more consistent, but there are always issues about holding the classes when they are needed. Computer-based training may", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "FIGURE 16.2  An example of the first part of a training matrix for EDC studies, with the list", "definition": "of all training components in the first column. Training components include courses (which may be computer-based or live), SOPs, and guidelines. The columns give the role, but in this case, the role is similar to a title but indicates what kinds of activities the employee is expected to perform. The full matrix would include all key data management roles and man- agers as well.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Training", "definition": "153 provide the consistency of training found lacking in mentor-based training, support part-time trainers, and make courses available as they are needed.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "TRAINING RECORDS", "definition": "One of the first things an auditor will ask for is training records for those staff who worked on the study that is the focus of the audit. Even at small companies, train- ing records should be available centrally—ideally, electronically with signed docu- ments scanned into PDF files. Records must be current. While larger companies will typically have a training management system, smaller companies must still maintain current records. If employees are responsible for updating their own files, then a quarterly reminder email or regular management review of training records will be needed. Whatever form the records take, the content needs to include items beyond those documenting SOP training. Because regulations require that each person be qualified by “education, train- ing, and experience,” training records need to include the education and the experi- ence portion. Current industry standard is to require a CV (curriculum vitae) for all employees and for that CV to be updated each year or at the change of position within the company. Many European companies also require a current job descrip- tion to be filed with the CV and that the job description be signed. Education is not only formal schooling. It also includes seminars, workshops, and other relevant courses that employees attend to increase their knowledge of the industry. These are valuable additions to an employee’s education and should be noted in the training files. With the current emphasis on training in the industry, nearly all organizers of external courses or seminars will print certificates of partici- pation for those who stayed through to the end of the course. Employees should be encouraged to keep a copy of these for their own files and file a copy (or original) in the training binder or folder. Even conferences add to a person’s experience and edu- cation and so add value to the training records. Conferences can be listed separately or added to the employee’s resume. Then we get to the company- and data management-specific training that cov- ers SOPs and guidelines. Many companies advocate the approach of filing only a training sign-off sheet and no materials actually related to the training. That is, SOPs themselves, guidelines, training slides, practice sheets, and so forth do not appear in the training binder. The idea here is that when given to an auditor, the additional material not only makes it more difficult to assess training, it also provides too much insight into details of processes. On the other hand, some com- panies do like to see more information about training, such as outlines or agendas, along with the sign-off form. At a minimum in both cases, the trainee, trainer, and date trained should be readily apparent and a true electronic signature or ink signature is required. Just as many companies are only now considering how to train on study-specific tasks, they are also considering how to file records for such training. There are good arguments for filing study-specific training in the data management study files, and there are also good arguments for filing that material in the training binders or folders. At this time, there does not yet seem to be a consensus or trend, so either is acceptable. 154 Practical Guide to Clinical Data Management, Third Edition The same effort that goes into training files for employees should go into training files for temporary workers and contractors. If an auditor requests a list of names of people who worked on a study, that auditor is then free to ask for training records for any of those people. Responding that one or another of those names belongs to a past contractor won’t be acceptable. Always maintain training records for everyone who worked on a study.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "SOPs ON TRAINING", "definition": "Not all companies have SOPs on training, but they do usually have very specific instructions on maintaining training documentation. If no company policy is in place, each data management group can set up a practice of having training plans for new employees. The training plan can cover the three areas of training: SOPs, department guidelines, and system training as recommended in this chapter. Study- specific training can be enforced through data management plans or requirements for the study files. ALLOTTING TIME FOR TRAINING Probably the biggest mistake smaller companies make is not allotting time for train- ing. They specifically hire experienced people and then expect each person to jump in and begin work. After all, that person has done this work before. That person may have done the work before, and they may even have used the same clinical data man- agement or EDC system, but they have not done the work at the company in question. As we saw in the earlier chapters of this book, there are many options for performing clinical data management and for using clinical data management systems. Each person needs to understand how the task is to be performed in each group’s unique combination of procedures and system configuration. This takes time and may mean the new hire sits around a bit while waiting for training or review of work. But it is well worth the investment for all involved. State up front to each new hire (and the group that person joins) that new staff members should not expect to do production work for the first week or two (or even more). When the expectation is clear, no one will feel the new person is wasting time. 155 17 Controlling Access", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "and Security", "definition": "The Food and Drug Administration (FDA) is very concerned, and rightly so, about the quality and integrity of data associated with clinical trials. In 21 CFR (Code of Federal Regulations) Part 11 and in guidance documents the agency frequently repeats the phrase “authenticity, integrity, and confidentiality of electronic records” to emphasize their interest. The regulation is clear; Section 11.10 requires controls and procedures including: “(d) Limiting system access to authorized individuals” and “(g) Use of authority checks to ensure that only authorized individuals can use the system, electronically sign a record, access the operation or computer system input or output device, alter a record, or perform the operation at hand.” Limiting access (who can get in) is achieved through proper account management. Authority checks (who can do what) are set up via access control or access rights. Account management deals with assigning and maintaining usernames and passwords—or bio- metric identifiers. Access control defines how those users are given access to particular features of the clinical data management system or electronic data capture (EDC) appli- cation and how and when that access is revoked. Good account management and access control has to be achieved through a combination of the features of the software being used and procedures to make sure the features are used properly and to fill in gaps in the systems’ abilities. While clinical data management (CDM) staff does not usually create accounts for sites in EDC systems, they are commonly responsible for assign- ing accounts in classic clinical data management systems and, in either case, need to understand good practices around assigning and managing accounts.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "ACCOUNT MANAGEMENT", "definition": "Accounts that are used to access clinical data management systems provide the user with varying degrees of power, or control, over data stored in the system. When the account permits the user to enter, modify, or delete data in electronic records, the username and password together constitute one kind of electronic signature. It is not the kind of signature that is the equivalent of a handwritten signature (such as the principal investigator signature for electronic case report forms (eCRF) in EDC) but rather is the kind of signature that makes the change to the data attributable to a par- ticular person. All data management systems automatically associate the person who is responsible for the entry or change with the data usually through the username. By thinking of the username and password as the way to make actions on the data attributable, it is easier to put procedures into place that govern the username and password in compliance with 21 CFR Part 11. The username must uniquely define a person and the combination of username and password constitute a signature. 156 Practical Guide to Clinical Data Management, Third Edition", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Usernames", "definition": "The statement “usernames must uniquely identify a person” sounds simple enough and most data management and EDC systems enforce unique account names, but there are some important, real-life cases to consider. For example, if the username associated with a common actual name (e.g., jsmith for Julia Smith) is removed when that person leaves the group or the company, then the possibility exists that that account name would be reused sometime in the future (e.g., when John Smith joins the group). In reviewing the data at a later time, it would then not be possible to immediately tell which person entered a particular record without referring to dates of employment and dates the record was created. It is best, therefore, to leave all account names in the system to prevent their reuse. When a person leaves, access permissions are removed but the account name remains to prevent reuse. Since most systems store not only the username but also the full name of the person using that account (thereby making the association of username and person), permanently retaining the account also keeps the connection to the actual person. If a system does not do this automatically, the connection must be retained through some paper method. In fact, many data management groups keep a paper account record of the person’s full name, signature, initials, and username when a new account is created. The need to keep the connection to a real name brings up one more common problem: having two people with the same name working at the same time. This is not the same case that we just considered. In that case, the username may be reused but the first person was Julia Smith and the second person coming along later was John Smith. In this case, we have people with the same name, two Julia Smiths, for example, working in the group at the same time. Because the system enforces unique user names, one of user names may be jsmith and the other may be jsmith2. Recording the owners of both of those names as Julia Smith would probably not be considered sufficient identification for a serious audit—after all, which Julia Smith was it? When tracking user names, either on paper or electronically, record either the middle initial or a birth date or some other information to differentiate the two. This same differentiation probably has to be carried over to training records, signatures on documents, initials on actions performed, and so forth. Rather a bother for the indi- viduals involved, but the idea of attribution is important in the eyes of the FDA.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Passwords", "definition": "The password in combination with the username is what makes the attribution work. It is equivalent to the signature of the person doing the work and says, “Yes, I per- formed this action.” Most companies in the industry are aware of good password procedures and implement them for corporate-level access to networks. Standard practices include the following: •\t Only the user knows the password, not the administrator. •\t The password has a minimum length (generally eight characters or more). •\t The password must be changed on a regular basis (definitely less than a year) and cannot be immediately reused. Controlling Access and Security 157 •\t Many companies also require at least one character in passwords that is not a letter and that the passwords not appear in a dictionary. •\t No one should do work while signed in as another person. That can be con- strued as falsification should the data come into question. These procedures comply with the FDA’s recommendations (found in the responses to comments in the preface to 21 CFR Part 11), which recommend enforc- ing procedures to make it less likely that a password could be compromised. While IT groups regularly enforce these standards for the company network, not all data management system administrators configure their applications the same way. A surprising number of companies, when audited, are found not to require regular password changes, nor do they enforce minimum lengths. A bit of digging has also turned up cases where employees change their passwords repeatedly when they expire to get back to their favorite password. In the same comments section of the regulation, the FDA says, “Although FDA agrees that employee honesty cannot be ensured by requiring it in a regulation, the presence of strong account- ability and responsibility policies is necessary to ensure that employees understand the importance of maintaining the integrity of electronic records and signatures.” Some groups try to make clear to their employees (temporary as well as perma- nent) the importance of good password control by making them sign a statement acknowledging that they understand the seriousness of not adhering to company account management policies, but many still do not see it and it is hard for manag- ers to detect poor password maintenance.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Account Timeouts", "definition": "Another often neglected situation is that of an employee walking away from their computer to get coffee. If they are logged on at that point, someone else could sit down and access the data. This is clearly not desirable as the comments section of the rule also states: “The agency’s concern here is the possibility that, if the person leaves the workstation, someone else could access the workstation (or other com- puter device used to execute the signing) and impersonate the legitimate signer by entering an identification code or password.” They go on to recommend an automatic disconnect or locking of the screen so that the user has to sign on again to continue. At some companies, this is a network setting; at others it is a setting of the data management application. However it is accomplished, such controls should be in place and the idle time setting, the number of minutes after which the systems locks, should not be set too high or it is of no value. New technology should help in the future as there are already devices that will lock the computer activity when the user, who is wearing or carrying a signal device in a badge, moves a given distance away from the computer.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "ACCESS CONTROL", "definition": "For the purposes of this chapter, access control is the combination of systems and procedures that define what access rights or permissions users have to the clinical 158 Practical Guide to Clinical Data Management, Third Edition data in a clinical data management system. (Access rights for EDC systems are a bit different but the information in this section can easily be extended to cover those as well.) Data management systems all have support for granting and revoking access to studies, but the systems available may not meet all the needs of a data manage- ment group. In that case, other procedures, with appropriate controls, should be put in place to fill the gaps. No matter how it is accomplished, systems and procedures should be able to answer these questions: •\t Who had access to a particular study? •\t When did they have access? •\t What were they allowed to do? This is recommended in the FDA guidance “Computer Systems Used in Clinical Investigations.”", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "How to Grant Access", "definition": "It is fairly standard in the industry at the time of this writing to grant access based on the concept of roles. A role is a database term that describes what a user will do in the software system. Will they enter data? Will they build a database? Will they man- age discrepancies? The roles are created first, and then the role is granted a specific set of access or permissions to studies. Often systems are set up so that the roles are granted access automatically at study creation based on a default specification. Users are added to the role or granted the role when they need to perform the activity. The advantages to this approach come mostly from easy maintenance. The sys- tem administrator sets up the roles and decides on the appropriate permissions just once. Then, as users are added, they are assigned whatever role(s) they need. When users leave or there is a change in the tasks they perform, they are removed from or added to a new role as appropriate. This works very well when roles are clearly defined and people who perform a role typically perform that role across studies. More classic CDM systems and most EDC systems also allow access to be granted both by role and by study. The industry best practice is to only grant access once a person has completed the training appropriate for that access. Chapter 16 discusses training matrices for roles and the question of study-specific training prior to doing actual work. Somehow the person granting access needs to have a communication that training is complete. Some companies enforce this by requiring a manager to request access; others pro- vide training reports to those responsible for granting access that they must consult before proceeding. Who Had Access? Auditors inspecting data management groups usually ask, “Who had access to this study?” or they may phrase it, “Who worked on this study?” They are either imply- ing or explicitly asking, “And what did they do?” They don’t often ask, “And during which time did these people have access?” but the question may arise should it be Controlling Access and Security 159 necessary to look at actual data and audit trails—something all of us hope will never happen at companies we work with! Nonetheless, it is worth asking ourselves how we can answer these questions because most systems, both classic and EDC, do not track it for us. Many data management groups already keep lists of people who worked on a particular study along with their roles. Of course, whenever a process is manual, it is prone to being forgotten or delayed, so it is much better if this information can come out of the data management system itself. The problem with data management systems as they are now is that they can easily produce current lists that answer the question, “Who has access right now,” but many don’t keep a history of access and access change. That is, systems can’t answer the question, “Who had access last January?” If a person is removed from a role, the information that that particular account did have access in the past is lost. This has led many data management groups to start tracking access on their own, independent of the data management system. This tracking may be on paper, in an Excel spreadsheet, or in a database application, but however it is accomplished, they do it to make sure they can always provide answers to the questions of who worked on a study and what that person did. Again, any process that is manual is prone to being forgotten, so not only do they have to set up tracking, they have to build in checks to make sure that the tracking information is kept—at least most of the time. SOPs AND GUIDELINES FOR ACCOUNTS Most companies have IT policies or standard operating procedures (SOPs) on account maintenance. A simple SOP or work instruction on account maintenance specifi- cally for clinical data management systems would probably suffice to cover security and access. For EDC systems, procedures specific to managing site passwords and accounts, and in particular the account and signature of the principal investigator, are critical for any group in charge of administering these. All SOPs should require that access be revoked immediately when an employee leaves the group or company, or has a change in their responsibilities. TAKING SECURITY SERIOUSLY The FDA takes secure access to the data very seriously; data management groups should do the same. Every data management group should be able to show that they take appropriate measures to control accounts, maintain passwords, and that they can identify who performed what actions on the data—even if the study locked years ago. That being said, it does fall on every person to maintain his or her account according to company polices and procedures. 161 18 Working with CROs Good clinical practice (GCP) defines a contract research organization (CRO) con- cisely as, “A person or an organization (commercial, academic, or other) contracted by the sponsor to perform one or more of a sponsor’s trial-related duties and func- tions (Section 1.2).” CROs can perform many or even all of the tasks associated with the development of a drug, including developing drug compounds, conducting toxicology studies, carrying out Phase I to IV clinical trials, producing a submis- sion, and many more. Larger CROs may have the resources to provide any of these services, but more typically, a CRO will specialize in a few particular areas of drug development. Data management groups will have the most contact with CROs work- ing on conducting clinical trials rather than those involved with drug development, for example. A biopharmaceutical or device manufacturer chooses to use a CRO for a variety of reasons. Small and emerging companies frequently do not have the resources and expertise in-house for all of the drug development tasks, so top management makes a decision as to which kinds of expertise will be hired directly by the company and which will be contracted out. They may have a plan that will bring that expertise in-house as the company grows. Larger companies turn to CROs to deal with changing capacity or for expertise in new areas of development. Even the best planning cannot possibly assure that there will be a steady and even flow of work in the drug development process, and CROs can help with the sudden need for extra capacity in a particular function. If a larger company is thinking of moving into a new area of research, man- agement may choose to start with a CRO for the same reasons small companies use them: they can provide expertise until a need for in-house staff is well established. Both the GCP and other FDA regulations discuss the transfer of obligations of the sponsor to a CRO. They both state explicitly that regulations apply to CROs as much as they do to sponsors. But the sponsor is still legally liable for the data. GCP says in Section 5.2.1: “A sponsor may transfer any or all of the sponsor’s trial-related duties and functions to a CRO, but the ultimate responsibility for the quality and integrity of the trial data always resides with the sponsor.” In this chapter, we will discuss how to work with CROs when they perform data management activities to assure that data is processed in compliance with regulations and results in datasets that meet the sponsor’s standards for its own data.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "THE CRO MYTH", "definition": "It is a myth that using a CRO means the sponsor offloads all of the work involved in the project or that portion of the project that is contracted out. Contracting with a CRO to carry out data management for a particular study does not mean that the sponsor’s data management group is freed from involvement with the study! It is only 162 Practical Guide to Clinical Data Management, Third Edition through close involvement from the time of study setup, throughout study conduct, and through database lock and final transfer that a sponsor can feel confident in the quality of the data associated with the study. It is by establishing a base knowledge of the CRO’s compliance with regula- tions and industry standards that the relationship gets underway. This baseline is established via an audit of the CRO. Then, for each project, both sides must clearly define their responsibilities so that no critical data management step is overlooked. To really understand the data and its quality, the sponsor liaison must stay closely involved in the project through ongoing review of materials, oversight of milestones, and constant discussions about the handling of problem data. After study closeout, the sponsor must ensure that the CRO transfers all data and items for the trial master file. To provide a sponsor contact and to keep closely involved with the study, a spon- sor’s data management group should designate a CRO liaison from data management who is an experienced data manager knowledgeable in all aspects of clinical data management (CDM).", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "AUDITING CROs", "definition": "A sponsor is ultimately responsible for the quality and integrity of the data coming from a CRO. It is generally accepted in the industry that a key component of taking on that responsibility is to audit the CRO before (or at least around the time of) begin- ning substantial work with that CRO. Sometimes a company will have the resources to maintain an audit group who will review a CRO’s procedures in all areas of inter- est to the sponsor. This should include someone with data management experience if the CRO will conduct some or all of the data management functions for a study. In smaller companies, or when special expertise is required, data management may be asked to designate an auditor (in-house or contracted) to look specifically at data management functions at that CRO. Auditing usually involves a review of written policies and procedures as well as interviews with CRO staff. Often, the auditor will work off a checklist or question list so that no key items are forgotten. By reviewing documents and talking with staff, the auditor gets an idea of whether the CRO performs up to industry standards and complies with regulations. Needless to say, if required to review data manage- ment practices in detail, the auditor must be very experienced in the field of data management and understand acceptable variations in practices. The auditor’s aim should be to ascertain if the CRO performs data management in an acceptable way, not that the CRO performs data management exactly as the sponsor does. After the audit, the auditor will write up an audit report and highlight any sig- nificant findings—both good and bad. The auditor must be careful to differentiate between noncompliance with regulations and variations in practices. In the first case, immediate action would be expected and the CRO should reply with a detailed reme- diation plan with timelines. In the latter case, the sponsor may have a different opin- ion about what is best practice in a particular area, but the CRO may still be using a fully acceptable approach. When this comes up, and the sponsor wants to continue to work with the CRO, the companies will usually work together to formulate a plan or compromise specific to the study.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Working with CROs", "definition": "167 company and may have limited knowledge of how a clinical trial really works and what is involved in clinical data management. CROs AS FUNCTIONAL SERVICE PROVIDERS The discussion of using CROs to this point has dealt with activities that are fully outsourced. That is, the CRO is using its systems, its standard operating procedures (SOPs), and its staff to perform the contracted activities. Larger companies use CROs to manage staffing of projects, and they have another option in addition to fully out- sourcing the CDM activities. They can contract-in CRO staff to work on the sponsor systems under sponsor oversight and sponsor SOPs. When a CRO is used in this way, it is often called a functional service provider. Essentially, the CRO is supply- ing staff and expertise but not systems. It is very much like hiring a local contractor who works onsite at the sponsor’s offices, but the CRO staff is typically located at the CRO facilities. Staff members working as functional service providers need the same kinds of training as any in-house staff or contractors performing the same activities. SOPs FOR WORKING WITH CROs Many companies will have a corporate-level SOP that lays out the bid process and specific requirements for contracting with a CRO. This would typically include the audit requirement mentioned previously. If that SOP does not exist, data manage- ment can still do the right thing at a department level and push for an audit and develop an appropriate responsibility matrix as part of the contracting process. Data management groups that work frequently (or even exclusively) with CROs can also develop a CRO manual or a CRO oversight SOP to lay out data manage- ment’s expectations explicitly. The document would, for example, require a data management plan from the CRO, along with all edit check specifications and data management self-evident corrections. It would also provide recommended workflows for coding, SAE reconciliation, listing review, and the issuing of manual discrepan- cies. Besides setting clear expectations for the CRO, a manual such as this provides consistency within data management when different data managers are working on projects with different CROs.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "CLOSING THE STUDY", "definition": "1.\tCloseout procedures plus any study-specific tasks 2.\tFor paper studies: database audit plan 3.\tApproval process needed to lock Associated document(s): Any documents required by the process, data- base audit results (paper), lock approval form 239 Appendix B: Clinical Data", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "EDC VENDORS AS CROs", "definition": "When small- or medium-sized data management groups make the leap to EDC, they often contract with an EDC vendor to build the study, manage accounts, and support the technical side of the study. In some ways, the EDC vendor then becomes a CRO whose responsibilities are limited to a portion of clinical data management activi- ties around database creation and support. The vendor should be treated like a CRO, and the suggestions for working well with CROs apply to the EDC vendor as well. In particular, expect to be heavily involved in the start-up activities. Keep in mind that EDC vendors are software companies, not drug development firms. While they will have hired staff familiar with clinical trials, they are not a biopharmaceutical", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "BENEFITING FROM CROs", "definition": "Getting the work done (and presumably done well) is the main benefit of working with a CRO. The experience the CRO gains with the study and the treatment is extremely valuable and can also be a benefit to the sponsor. When a sponsor provides adequate support staff to answer questions and meet regularly with the CRO, then the experience the CRO is gaining with study is shared with the sponsor with no additional effort. Small companies, in particular, do themselves a disservice by not staying closely in touch with a CRO during a trial and must overcome their reluc- tance to provide adequate support staff. After all, the data all there is to show for the investment in the treatment—and all there is to guide future studies. To benefit from it, the sponsoring company must provide someone to receive the knowledge. 168 Practical Guide to Clinical Data Management, Third Edition Many data management groups have rules of thumb to help determine the staffing level needed for a CRO study. Some say that one sponsor data manager can handle three or four active CRO projects at a time if the studies are in different points in the study lifecycle but fewer, perhaps only one or two, if the studies are large and complex. Looking at it the other way, in order to provide time for meetings, review of documents, data review, issuing manual discrepancies, and tracking the project, a sponsor’s data management liaison should allocate at least 5 hours per week for each fully outsourced study. (The hours don’t add up to 40 in this rule of thumb because some of an employee’s hours are taken up by the sponsor itself and studies will fluctuate in their needs, requiring more time during some weeks.) Larger companies generally have the resources to assign someone but may still make the mistake of not allotting enough of the person’s time. Small and emerging companies feel that they don’t have the staff to provide the internal management, and this may be the biggest mistake they make. There is too much leeway in the conduct of a study and in the interpretation of guidelines to expect a study to be run effectively without ongoing contact. With no one involved with the trial, it is almost guaranteed to be different from other trials being run in-house, at other CROs, or even at the same CRO. Finally, it is worth noting that the opportunity to benefit from a CRO is reduced if the relationship between the CRO and sponsor is adversarial. The sponsor com- pany is entrusting to the CRO important elements of its development program. The success of the CRO is a success for the sponsor. The relationship between the CRO staff and the sponsor staff should not be an us versus them relationship, but rather a partnership where the combined staffs act as a team. From the sponsor’s side, acting as a team means treating the CRO staff with respect, providing information, answering questions in a timely manner, and fulfilling the sponsor responsibilities using the best possible practices. The goal is to deal with the CRO staff as if they were part of the same organization but located elsewhere. From the CRO’s side, the staff must care about the details, make note of and track down possible problems, and keep the sponsor informed. In general, the goal of the CRO should be to treat the study as its own.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "CDM Systems", "definition": "All data managers work with computer software applications. In the chapters that follow, we will look at the characteristics of traditional clinical data management (CDM) systems and electronic data capture (EDC) systems and then explore the activities around those systems. Given the fast pace of software development and the rapid growth (and failure) of EDC vendors, many data managers are likely to be involved, at least peripherally, in vendor selection and validation of new systems. All data managers who design and/or build study databases need to be aware of change control requirements on study applications and all CDM software. A chapter on software used to code adverse events (AEs) and medications rounds out this section as background for those data managers responsible for the activity. Discussions of system selection, implementation, and validation could take up an entire book in their own right. The following chapters aim to provide an overview without going into exhaustive detail or extensive procedures. 171 19 Clinical Data", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Management Systems", "definition": "Except for companies that outsource all data management to contract research orga- nization (CROs) or electronic data capture (EDC) vendors, all data management groups use computer systems to carry out their data management tasks. These are either the large and specialized applications known as clinical data management (CDM) systems or they are the web-based EDC systems that are deployed directly to investigator sites. CDM systems were developed when the majority of trials were conducted on paper and continue to provide features to support those. As EDC for data collection has moved to the forefront, these CDM systems have integrated with or added on EDC front ends so that they can support both kinds of trials. In this chapter, we will focus on the characteristics of CDM systems generally. EDC front ends and stand-alone EDC applications are discussed in the next chapter. CDM SYSTEM CHARACTERISTICS CDM systems are designed specifically to support clinical data management groups in carrying out tasks over multiple and simultaneous studies. All of these systems have an underlying database that is used to store the data associated with a clinical trial. The database is usually a commercial product, such as Oracle® or Microsoft SQL Server. On top of the database is an application that takes user instructions and applies them to objects in the database. The user gives the instructions via simple screens, checklists, or forms, and only on a limited basis, as programming instruc- tions. The application translates the instructions from the user into actions on the tables and other objects defined in the underlying database. The idea that classical CDM applications support the full range of data manage- ment tasks is what differentiates them from most EDC systems (discussed in the next chapter) or other types of data collection tools and applications. At a minimum, CDM systems have features to support: •\t Database design •\t Entry screen creation •\t Data entry (through optical character recognition [OCR], single, or dou- ble entry) •\t Data cleaning through edit checks •\t Discrepancy management and query resolution •\t Locking of studies 172 Practical Guide to Clinical Data Management, Third Edition •\t Extraction of data for reporting and analysis •\t Account management and access Many of them also support: •\t Loading of external data including lab data •\t Coding against the Medical Dictionary for Regulatory Activities (MedDRA) and other dictionaries •\t EDC for data collection and discrepancy resolution All of the features of CDM systems are programmed to comply with the require- ments of 21 CFR (Code of Federal Regulations) Part 11, including time-stamped, automatic audit trials, account and access management, controls on the sequence of the activities, and attribution for the creation and modification of records. WHERE CDM SYSTEMS COME FROM Before 21 CFR Part 11, there were entire conference sessions devoted to the pros and cons of building a custom CDM system versus buying one from a vendor. We don’t see these tracks at conferences at all anymore. 21 CFR Part 11 had such an impact because meeting the regulations and the recommendations laid out in Food and Drug Administration (FDA) guidance documents require a professional approach to software development. Most larger biopharmaceutical and device companies have decided that their area of expertise is not in the area of software development but rather in development of new drugs, treatments, and devices. They have decided to leave the development of large system to vendors. That is not to say that these com- panies don’t do any development. In fact, all of the large and mid-sized companies that have the resources do add on to the systems they buy. They may extend the base system, integrate the system with other applications, or add specialized reports. Small companies nearing their first trial may be reluctant to make the finan- cial investment to install a CDM system and decide to build their own, often using common software packages like Microsoft Access. These systems must still con- form to 21 CFR Part 11 and must be validated per FDA requirements (see Chapter 23). Assuming good validation practices properly documented, this can be done. However, these applications are built and validated to support a single study. When the next study comes along, the entire build and validation process must be repeated. After one or two studies, this becomes impractical and the companies must then consider purchasing a system that supports multiple studies, or they can turn to EDC vendors to develop study applications and support the technical side of the study.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "CHOOSING A CDM SYSTEM", "definition": "We will look more closely at choosing a vendor product in Chapter 21, but it is worth making one point about vendor products here—they work. When used properly, the systems all collect data that reflect the protocol, store it, and clean it. They also allow for the extraction of that data for analysis. The tasks in the data management process Clinical Data Management Systems 173 that are easy in a given CDM system and the tasks that are awkward or require more resources do differ from product to product, of course, and that forms the basis for choosing among them. In the area of database design in particular, CDM systems do show significant differences. Some have underlying database structures that are table based, some are hypernormalized, and others are page based. These core concepts and the tools provided for database development have an impact on workflow downstream in the data management process. The underlying database structures also impact the way CDM systems provide a way of reusing database designs through data dictionaries or standard metadata. The people in data management groups evaluating CDM systems often have a strong inclination toward one structure and approach or the other when it comes to database design, and this can have an important impact when selecting a CDM system product. USING CDM SYSTEMS SUCCESSFULLY To use CDM systems successfully, data managers have to understand that they all have problems and they will never do everything you want exactly the way you want to do it. But, they do many things just fine. Because CDM systems come from ven- dors with professional developers and professional approaches to software develop- ment, we expect a high level of quality in the final product. That is, we expect no bugs and we expect a really smooth human interface. That is an unreasonable expec- tation. Even the best developers supported by high-quality software and great quality assurance groups cannot produce a system with no bugs. And, because these people are not data managers, they may not know enough about the real work to design a particular interface well. All of the CDM systems on the market have bugs and have problems—they just have different bugs and different problems. The key to success with any system comes from preparing for and dealing with bugs and “design flaws” when they arise. We also have to remember that even though CDM systems can be used “out of the box,” they are installed and configured differently by each company and they have to be validated by the end user. Chapter 22, “Implementing New Systems,” includes a discussion of configuration, and Chapter 23 discusses system validation in detail. In Chapter 23, in particular, we will see that one purpose of validation is to identify bugs and their workarounds, but another is to really understand the system in question and adjust business practices and workflow as needed to make the best use of that system. Sometimes it is necessary to add on a utility or report to make the system work better. Sometimes it is necessary to change the group’s approach or lower expectations.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "SOPs FOR CDM SYSTEMS", "definition": "The SOPs for CDM systems themselves are those that cover implementation, valida- tion, and change control. These SOPs are often written for an IT group. In smaller or newer companies, data management may have to create these SOPs themselves and the concepts needed are discussed in the chapters that follow. 174 Practical Guide to Clinical Data Management, Third Edition CDM SYSTEMS ARE FOR MORE THAN DATA ENTRY Just as data management is more than data entry, CDM systems are more than data collection tools or data entry applications. Even the smaller vendor products will support the key data management tasks for many studies at a time, with lots of data, while being 21 CFR Part 11 compliant. The larger, more complex (and expensive) systems add on more features for more tasks, greater flexibility, and further options for configuration. They will also be able to handle even larger volumes of data and studies. When looking at CDM systems, it is easy to focus on the screens and entry aspects because they are visible and concrete, but it is the complex functions that support the complex work of data management that should be the deciding factors. 175 20 EDC Systems Electronic data capture (EDC) systems deliver clinical trial data from the investiga- tor sites to the sponsor through electronic means rather than paper case report forms (CRFs). As in paper studies, site staff copies information from source records for most of the clinical trial information for a given subject, but they copy into electronic CRFs (eCRFs) rather than paper ones. In most current EDC systems, the site is online with a central computer and the data is stored only on a central computer. These systems work like, and feel like, the familiar websites we visit to shop for books or shoes. As soon as we make a selec- tion and provide payment information, the order is known to the vendor. Some EDC vendors do provide systems that also allow sites to work offline. These store the data locally until the site initiates a connection. This approach is much like the one used when we enter information on our smartphone and then synch it up with our personal computers later via special applications. There are pros and cons to both of these approaches that we will discuss later in this chapter. EDC systems are optimized for site activities during a clinical trial and typi- cally feature: •\t eCRFs for the entry of data •\t Support for single-field and cross-field checks on the data that generate", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "queries", "definition": "•\t Tools to allow sites to review and resolve queries •\t Ways for the sponsor to raise manual queries while reviewing data •\t True electronic signatures so the investigator can “sign” for the data •\t Record or subject locks on the data •\t Tools to assist monitoring •\t Reports about subjects for the sites and reports for the sponsor about sites •\t A portal that provides information about the study to the sites •\t A variety of ways to extract the data for review and analysis WHAT MAKES EDC SYSTEMS DIFFERENT? If EDC systems collect the data and manage discrepancies, why aren’t they consid- ered just another kind of clinical data management (CDM) system? The reasons are in fact a bit subtle and hinge on certain aspects of performing data management for clinical trials. Some of the key differences between the two kinds of systems are found in: •\t Managing multiple data streams •\t How coding is handled •\t Location of servers with trial data 176 Practical Guide to Clinical Data Management, Third Edition •\t Workflow for study setup •\t The need for data repositories", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Multiple Data Streams", "definition": "Today, all but the smallest trials have several streams or sources of data. These include interactive voice response system (IVRS) data, lab data from central labs, laboratory normal values, EKG readings, and even electronic subject diary data. This data must be cross-checked against the subject’s eCRF data during the conduct of the trial to identify discrepancies or unusual occurrences. EDC systems do not support multiple data streams very well. If the data is loaded into the EDC front end, then it must be hidden from the sites or protected so that the data cannot be changed. Adding edit checks to loaded data does not provide much value because any queries would not be directed to the sites but rather to the data source or vendor first. If the data is not loaded, but kept as SAS® datasets or stored in another database, then the only way to do reconciliation with the eCRF data is to first extract that data. Also, if the electronic data is kept separately, then special procedures must be in place to lock it at the time the EDC data is locked.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Coding", "definition": "Strong support for the coding process, including management of the coding diction- ary, is a typical feature of the larger CDM systems. Those systems support robust automatic coding and maintenance of synonyms and provide tools for making man- ual assignments. (See Chapter 11, “Collecting Adverse Event Data,” and Chapter 26, “Coding Dictionaries and Systems,” for more on coding.) Many current EDC systems may only support import of codes after terms have been coded externally; a few are starting to introduce better support for coding. Where the Servers Are—Hosting Another way that EDC systems differ from classic CDM systems is that the central server storing the data from the trail is typically not the sponsor’s server. EDC appli- cations are most commonly “hosted” by the vendor or by some other third party. There are two main reasons for this: 1.\tThere is still some uncertainty as to the interpretation of regulations regard- ing the requirement that a site own or have control of the subjects’ data. Many companies feel that having the data under the total control of the sponsor by having it on a sponsor server violates this regulation. Some oth- ers feel that a sponsor’s own IT department could be considered an appro- priate trusted third party. This discussion will certainly continue for a few more years, but for now, most data managers will see externally hosted EDC applications.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "EDC Systems", "definition": "181 a given data manager will participate in will change when a company begins to use EDC, but there is still a need to have a coordination of data storage and cleaning efforts. Data managers are also very likely to continue to play a role in design of the eCRF, database, and edit checks. They may also play more of a role in coordinating the various data streams from a study to assure quality and timeliness of data from labs, coding groups, and IVR systems, as well as the EDC host. At many companies, data managers will continue to play their important role as overseers of data quality as they review data listings, run aggregate checks, and perform simple analyses on the datasets. Some companies that use EDC systems widely have reported that the profile of their data management group changes. They go from having many lower-level staff members for data entry and discrepancy management and fewer senior data man- agers to define databases and oversee data collection to exactly the opposite. With EDC, data managers are more involved in study setup and more complex checking and there is less need for junior or less-experienced staff for entry or discrepancy management. They also require more technical expertise in their data managers than previously. This should be heartening to data managers as it shows a trend to more interesting, senior-level positions being available as we go forward with EDC.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "STUDY SETUP", "definition": "1.\tComputer system(s) to be used 2.\tProcess to design, build, and test the study database 3.\tProcedures for release for production 4.\tOther systems or integrations to be configured Associated document(s): Study database design document; other configu- ration documents, test plans and results, approval for production use", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "SOPs FOR EDC", "definition": "Because EDC study workflow is different enough from that for paper studies, stan- dard operating procedures (SOPs) specifically for EDC will be necessary for the usual topics in data management (see Chapter 15 and Appendix B). One SOP that is needed and is not common to paper studies is the procedure to manage the accounts and access for sites. Since groups other than data management, such as IT or even the EDC host, are responsible for accounts, they may be responsible for that SOP.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "MAKING EDC SUCCESSFUL", "definition": "To make EDC truly successful, we need to understand how it changes the way a study is conducted. The work is no longer as sequential and well separated among clinical, data management, and biostatistics. The work the groups are doing overlaps more and there is more room for duplication or, much worse, for a step in the process to be overlooked. In particular, if data management alone is given the task to go imple- ment EDC, the project is likely to fail or at least bring little value. It is when the three groups work together to decide what works given the company philosophy and avail- able resources that the project gets off to a good start. Continuing to work together and reevaluating the workflow during the initial studies will have a positive impact on the outcome of the early studies and on the company’s view of EDC as a whole. 183 21 Choosing Vendor", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Products", "definition": "Because software systems have a lifespan shorter than in the past and because new applications are constantly coming on the market, every five to six years data man- agement groups will find themselves evaluating and choosing among vendor prod- ucts. Evaluating and choosing among vendor products can be a simple process that takes a few weeks or a complex process that can take over a year. The steps are more or less the same, but the time and effort put into the process varies according to: •\t The size and complexity of the desired system •\t The number of vendor products available •\t Company requirements for approval of a system •\t Timeline for implementation •\t Availability of resources, especially people Even for the same complex system, such as full clinical data management pack- ages, one company may commit a year and another a month to the vendor selection period. So it is really the last two points in the preceding list—the timeline and avail- able resources—that ultimately decide how hard or long the process will be. Small companies with shorter timelines and fewer people with time to spare are often forced into a quick decision. Large companies, in contrast, often do take their time—gener- ally because they can—but this is not always a wise choice. At all companies, the pro- cess of selecting a product begins by determining what is needed and then proceeds through several selection phases, reducing eligible candidates and increasing the level of detail knowledge obtained at each phase, until a decision is reached. DEFINING BUSINESS NEEDS The evaluation process starts when a company decides what kind of product it needs. A document describing the needs the product must meet may be a one-page bul- leted list of tasks that must be supported or a fat document describing the details of each task. This business-needs document is not necessarily a requirements document; some of the items listed may be required, others desired, and yet others optional. While most of the items in the business-needs document may well be required (in that the system must support them in some way, shape, or form), it is worth identifying any needs that are actually desires or nice-to-have options. These can be extremely useful in making the final decision should more than one system meet all the requirements. When creating the list of business needs, companies should focus on the func- tionality they want to be supported (not necessarily saying how) to allow for 184 Practical Guide to Clinical Data Management, Third Edition different approaches in different products to be assessed. For example, a business- needs document can say that a clinical data management system must have an audit trail, but unless there really is a requirement for it, the document should not specify that the audit trail be implemented through copies of the entire record before the change or perhaps through change histories on each individual field. While some business needs will validly be specific as to a required implementation approach, the trick is balancing when to require specific implementations and when to specify only the feature. Note also that the business-needs document, or at least parts of it, will eventually become part of the validation package of the application chosen, as it provides mate- rial that will allow an assessment of the package’s suitability for the desired task. INITIAL DATA GATHERING Next, the evaluation team begins to gather a list of candidate vendor products. Friends, contacts at other companies, web searches, and visits to vendor exhibits during conferences are all good sources of leads for products. The evaluation team or group then gathers some basic information about each of these products, which may require making initial contact with the vendor. Because marketing materials are rarely complete enough to rule out (or in) a possible product, the group will likely arrange for short demonstrations. These early demonstrations should be kept short and to the point. It is usually valu- able to let the vendor go through at least a good portion of the canned demo before going into specific questions of interest to the group. This provides the group with a good overview of the product and allows the vendor to point out highlights of the product that the group may not even be aware existed. At some point, however, the dis- cussion should turn to desired features. While everyone in the evaluation team should ask questions, someone in the group should be responsible for trying to get at least a bit of information concerning the business needs. If the group will be seeing more than two or three vendors, a scribe should be taking notes at each demonstration. There is a good possibility that the group will see something new and interest- ing—and maybe even exciting—during the demos. Evaluation teams should be open to revising the business-needs document to include such features. (The business- needs document should not be frozen at this stage of the process and probably not until validation.) Once the business-needs document has been reviewed and the demos discussed, the group should be able to narrow down the list of candidate products and vendors. The next step is usually getting detailed information from the vendors on fea- tures and prices through a document often called a request for information (RFI) or request for proposal (RFP). If there are only one or two candidates, then the evalua- tion team may skip this step and go directly to in-depth demonstrations or pilots. REQUESTS FOR INFORMATION A request for information (RFI) or request for proposal (RFP) should aim to elicit detailed information from the vendor on how the product meets specific needs or Choosing Vendor Products 185 requirements. Because creation of the RFI document, which must be specific to the desired product and summarize the business needs, and the evaluation of the responses both require a large effort, RFIs don’t provide an efficient means of gath- ering general information when there are still a large number of candidates. They work better as tools targeted to a set of candidate products rather than as a method to identify the broad range of possible products. The vendor response to an RFI takes time also. Some vendors will decline to respond if they have not had previous contact with the company, if the timelines for a response are too tight, or if they feel that their responses will not be given appro- priate attention by the requestor. Companies get a better response from vendors if they have had contact with them first and have notified them well in advance that an RFI is coming. An internal contact person must be available who can provide back- ground information, clarify items in the RFI, and answer administrative questions from the vendors. Vendor staff will always (and should) respond to items in the RFI in such a way as to make their product look good. They will choose words carefully so that it will not necessarily be clear which business needs are fully and easily supported by the product and which are minimally supported or supported only through custom extensions. While it may seem contradictory, the longer and more detailed the RFI, the harder the vendor responses will be to evaluate.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "EVALUATING RESPONSES", "definition": "When the responses to the RFIs arrive, the evaluation team combines the new infor- mation with the initial assessments from the demos and tries to come to some kind of conclusion. This can be a surprisingly difficult task. Each software package will be strong in some areas and weak in others. It should not even be surprising if all the products under consideration end up being weak in an area that the company consid- ers very important. Deciding which of two needs, both initially labeled as “very important,” is going to be very difficult if the products don’t support those needs equally well. Some companies have tried complex systems of priorities and weighting. Each require- ment in the RFI is given priority, and the product responses are weighted as to how well they meet the requirement. The company then performs calculations or even statistical analyses on the outcomes in an attempt to come up with a clear numeric winner. These numbers help, but the final decision of which product to go with—or to decide not to go with any—will probably come down to a qualitative feel about the product and the vendor rather than a pure score based on features. Many people have found that a gut reaction to vendors based on a demo and an RFI results in the same outcome as a complex numerical analysis. EXTENDED DEMOS AND PILOTS If the goal of the vendor and product evaluation process is to learn as much as pos- sible about whether the product would be successful in the company’s environment, then the list of business needs and the evaluation of responses may not be enough. 186 Practical Guide to Clinical Data Management, Third Edition Many companies find that they need some amount of hands-on time with candidate products to really understand if it will work. If time is short in the evaluation period, an extended hands-on demo is a good option. If time and resources permit, a full pilot of the product (before purchase) may be possible. Neither demo nor pilot would normally be carried out with more than two candidate products—and frequently these tools are used as a final check only of the most probable choice.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Hands-On Demos", "definition": "A hands-on demo takes place either at the vendor or on-site at the company and typi- cally lasts from two to five days depending on the complexity of the system. Having a demo on-site allows more of the company staff to attend all or part of the session. On the other hand, it can be hard to corral all the group members of the evaluation team for the entire period and also keep them focused. Visits to the vendor may incur sig- nificant travel expenses, but they do keep the group more focused. They also provide the group access to more than just one or two of the vendor staff members. The idea behind the hands-on demo is to see if the product would work in the business environment of the company by using data or examples from actual studies. Another goal is to give the evaluation team a real sense of how the product would be used on a day-to-day basis. The evaluation team comes to the demo with sample data or studies to try out in the candidate system. The vendor can perform the more complex tasks with the evaluation team looking on, then turn over the keyboard as much as possible for other tasks. Turning the demonstration into a standard training session usually does not meet the goals of the evaluation team. The success of the hands-on demo will rely on the quality of the people sent by the vendor and on the data or examples chosen by the evaluation team. The examples should reasonably represent actual data or structures that would be used in the prod- uct after implementation. When appropriate, the evaluation team should provide the vendor staff with the data and examples before the demo so that the vendor can do some preparation or setup to keep the hands-on time focused. Note that for complex systems, it would be impossible to touch on all parts or features of the product in depth during the demo period, so the evaluation team should identify ahead of time which features they most want to see.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Pilots", "definition": "Larger companies may have the resources to spend more time with the most likely candidate product before they purchase a system, but even smaller companies may take this approach when they suspect that the product might not perform as expected. In both cases, the longer evaluation period, often known as a pilot, is performed on the one product the company is most likely to select. Pilots are more commonly con- ducted after a product is chosen, in which case the goal is more to identify appropri- ate use of the product in a particular environment than to see if it works. In a pilot, the evaluation team takes one or more typical examples and works through them using the candidate product. Unlike the hands-on demo, the evaluation team will perform most of the work. To do this, the product must be installed on-site Choosing Vendor Products 187 (or configured on a host server) and the staff trained appropriately—which is what makes pilot evaluations expensive. Pilots require significant investment in computer infrastructure and in staff resources. Pilots are also expensive for the vendor. The vendor will be supporting the com- pany staff with technical support, documentation, training, and general handhold- ing. None of this is inexpensive, so vendors usually charge for pilots through a combination of license fees prorated to the term of the pilot plus consulting fees for technical staff. The vendor has an interest in the success of the pilot and will usually provide good support if they feel the evaluation team is proceeding in a reasonable manner. There will be problems during a pilot. Users will find bugs. Users will get frus- trated. Users won’t be able to do things as well or as smoothly as they imagined or as easily as in the “old” system. This can generate inappropriate resentment against the candidate product. It is very important to set realistic expectations ahead of time. All team members should be warned about this likelihood and they should realize that other products would have other problems. Setting expectations ahead of time and outlining what must work well will help the team keep the process, and problems, in perspective. That is not to say that the user experiences should not be taken seriously. An evaluation period at the end of the pilot must allow the users to provide their input in the context of the pilot goals, business needs, and terms of success and failure. Vendors should be given an opportunity to respond to the pilot evaluation by clarify- ing points, proposing workarounds, and discussing fixes or extensions in problem areas. An important experience to note is that large and even some smaller compa- nies do decide not to proceed with a product after a pilot, despite large investments of time and money. ADDITIONAL CONSIDERATIONS Companies have found, to their dismay, that after they made a decision based on features, the success or failure of a product was based on nontechnical issues. As much as possible, a product evaluation should include consideration of these non- technical issues, which are valid regardless of the type of software system or appli- cation. They include: •\t Size of user community •\t Availability of contractors •\t Quality of the product •\t Stability of the vendor •\t Product development or enhancement plans •\t Vendor technical support The size of the user community can play an important role in the success of a product at a company. If the community is large, the system likely meets many com- mon needs and can get the work done. Current clients may have formed a user group that can be an invaluable source of information about the product, the vendor, and 188 Practical Guide to Clinical Data Management, Third Edition how to apply the product in production. There will be other companies to turn to for specific advice and help, and there also may be some movement between companies of staff experienced with the product. On the negative side, a large user community may also mean that the product is older and perhaps not as technically current as a new product might be. A small community may only mean that the product is new to the market; the product may be innovative and well worth the risk as long as the data management group is aware that they are more on their own. The availability of outside, independent contractors is often (but not necessar- ily) related to the number of companies using a product. Outside contractors in data management are used by nearly all firms during times of high workload or to assist in special projects. The need for contractors may be ongoing or just for a short time during implementation of the new system. For new or little-known products, contrac- tors and consultants who are not from the vendor may not be widely available. This may pose no difficulty if the vendor’s consulting and contracting staff are available and of high quality. The purchasing company is ultimately responsible for the quality of the product they choose to use. Every company should assure themselves, generally through a vendor audit, that the software was produced using good practices and was tested adequately before release. Having said this, it should be noted that audits by different companies of the very same vendor and same product do come up with very different results, so it helps if the auditors have a clear understanding of internal expectations and requirements for the product in question. Companies are often concerned about the stability of the vendor company. Many software products are built by small, young companies with no history, so that there is always a risk that the sales of the product will not be enough to support such a vendor. Even large companies fail, are taken over, or decide to give up on a product. There is no way of knowing what will happen to a given vendor even six months down the line from a purchase, but reasonable inquires may provide some assurance or perhaps a warning. Knowing the product plans for the system in question can make a considerable difference in a company’s decision. This is especially true if the timeline for imple- mentation and production use is tight. A company should ask these questions: •\t Which version of the software would we receive? •\t Is it a stable version or a completely new release? •\t On what operating systems, servers, and database applications is it dependent? •\t Is there a new version coming up in the near term that will require a migra- tion or a complicated upgrade from the version we will receive? Companies have delayed purchase and implementation of a product in order to avoid starting off on one version and then having to begin planning for a required upgrade or migration almost immediately. Finally, if the product has been on the market for a while, it should be possible to assess the quality of the vendor’s technical support. This support should include plans for new releases, bug fixes, and documentation updates, in addition to the usual telephone support and training classes. Many companies consider it essential for the Choosing Vendor Products 189 vendor to have technical consultants experienced in the industry available during the implementation and validation period. For new vendors or products, the quality of the support will be harder to assess, and there should be concern that the vendor’s staff will be learning about the product at the same rate as the new clients. Yet, more than any of the other business needs, the need for ongoing reliable support must be reasonably met for each and every product by every vendor. After all, following a list of features, isn’t support and maintenance one of the main reasons for going with a vendor product rather than building something custom? WHAT IS MISSING? After choosing a product and before moving ahead with a purchase and implementa- tion, companies should spend time to analyze what is missing in the product. This review is sometimes called gap analysis. The evaluation of the product information from the vendor and the hands-on demonstration would probably have identified any important feature or service that is missing; a pilot certainly would have. Some of the missing features may not be required immediately; others will require changes to business practices. Still, others will require custom programming, extensions, or support from other applications. Knowing what is not there, as well as knowing what is, is critical to successful implementation and can be used in negotiating the contract with the vendor. PREPARING FOR IMPLEMENTATION As the company prepares to move forward with implementation, the evaluation team can complete and gather the documentation from the evaluation. In particular, the validation process that follows will make use of the business-needs document, the results of the vendor audit, and the gap analysis. Also, because a company is respon- sible for the systems they choose to use, a short summary of the selection process along with the key reasons for the final selection may at some point prove valuable. 191 22 Implementing", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "New Systems", "definition": "Having chosen a software application for use in clinical data management, the plan- ning to implement the system, that is, putting it into production use, begins. Even the smallest, most contained of applications cannot be installed and released for use without some forethought and preparation. In some cases, a validation plan by itself will be sufficient. However, any new system that connects (integrates) with other systems or requires a migration of existing data would benefit from a separate imple- mentation plan. In this case, the validation plan (see Chapter 23) is just one aspect of the implementation process. Appendix D contains an example implementation plan that demonstrates both the elements commonly found in such a plan and also the complications in trying to put such a plan together. Because implementation projects frequently have many different kinds of complex tasks in them, each implementation team faces the ques- tion of how much detail to include in the implementation plan and how much to spin out into separate plans. One approach (demonstrated in Appendix D) is to use the implementation plan to document the overall tasks, providing details when the task falls directly under the control of the overall implementation project and providing references to external plans for especially complex tasks. The elements of the plan found in Appendix D are discussed in upcoming text, along with the risks to implementation timelines found in each task. OVERVIEW AND RELATED PLANS The first step in any implementation should be obtaining an overview of what will be involved in the implementation process. The overview should touch on what exactly will be installed and in what order. The “what exactly” must include details of the software application and underlying operating system, network packages, applica- tion builders, or database software required. It also should include all the hardware necessary, involving not only servers but also associated equipment such as scanners and printers that have specific requirements placed on them by the application. The evaluation process for vendor products (see Chapter 21, “Choosing Vendor Products”) involves a gap analysis that probably will have identified areas where integration with other systems is necessary to support a smooth workflow. That analysis will also have identified necessary (or desired) extensions to the system. (If the implementation team feels that the integration and extension points are not well defined, they may opt to require a pilot as the first step in implementation.) The overview of the implementation plan should identify whether these integration links 192 Practical Guide to Clinical Data Management, Third Edition or extensions will be developed as part of the implementation for this package or whether they fall under separate projects with their own plans. The implementation plan may have subplans or closely related plans that must be tracked and scheduled. The most common related plans are the following: •\t Project plan •\t Migration plan •\t Pilot plan •\t Validation plan The project plan (usually developed using Microsoft Project® or equivalent pack- age) lists all of the tasks in detail along with dependencies, delivery dates, resources, and schedule. When migration of legacy data is called for, and is not simple, a sepa- rate migration plan would guide the development of the tools, testing, and data veri- fication (see Chapter 27, “Migrating and Archiving Data”). If a pilot is necessary, the pilot plan focuses on that project’s purpose, method, and schedule. The most important of all of the subplans is the validation plan, which lists the steps and documentation needed to support validation of the application. Validation plans are discussed in Chapter 23 and Appendix E.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "ESSENTIAL PREPARATION", "definition": "The preparation needed before implementation of a system can begin involves get- ting all the necessary pieces in place. This means acquiring all of the hardware and software identified in the overview. It also means installing that hardware and soft- ware and configuring the software application being implemented. At a minimum, installed software should undergo an installation qualification to demonstrate that it has been properly installed. One common problem in the preparation phase is underestimating the time needed to acquire and install systems. In the case of hardware, implementation teams may be unaware that there could well be a wait time due to vendor lead times for deliv- ery of popular server configurations. Even if there is no delay expected in shipping, the process of ordering and purchasing hardware within companies has become so complex that it, by itself, introduces significant lead time. In the case of software, it may be immediately available, but contract negotiations may take quite a while— especially if special conditions, extensions, or future expectations are in discussion. The biggest risk in this preparation phase is forgetting the configuration task alto- gether. Most large software systems (and many small systems) allow some variation in the way the product can be used. Each installing company decides how to use it and configures the system appropriately. The configuration may take the form of assigning values to system and user parameters, or it may require more complex setup, such as these for clinical data management software: •\t Deciding on and setting workflow states •\t Adding company logos •\t Developing algorithms (as for autocoders) Implementing New Systems 193 •\t Loading large coding dictionaries •\t Providing company-specific information (e.g., addresses, protocols, com- pany drugs) The configuration tasks needed for a given software product are frequently dif- ficult to judge and difficult to perform because new users may not understand a product well enough to know what to configure and how. Implementation teams aware of the potential for problems in configuration can try to plan for it by working closely with the vendor to determine what needs to be done and roughly how long it should take. INTEGRATION AND EXTENSIONS The overview of the implementation plan in Appendix D has placeholders for all of the integration links and extensions that are considered integral parts of the system. Figure 22.1 lists some examples of integration points and extensions that might apply to a clinical data management system. An individual section for each integration point or extension allows the implementation team to deal with and track each one Type of Integration or Extension", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Purpose", "definition": "Connect to external AE and drug coding", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "applications", "definition": "An integration; terms to be coded are extracted into the coding application; results are", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "returned to the data", "definition": "Build CRF tracking applications for paper", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "studies", "definition": "An extension when the CDM system does not provide detailed tracking information Use external data-checking programs An integration when external programs such as SAS are used; data is extracted to SAS and discrepancies are loaded back into the system Connect imaging systems for paper CRFs An integration so that data is connected to the CRF image and query forms are connected to query records in the database Use Clinical Trial Systems with EDC An integration to allow site information to be loaded into the EDC system or allow visits to", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "trigger site payments", "definition": "Support SAE reconciliation An extension or integration that supports or simplifies SAE reconciliation by producing combined reports from the safety and CDM", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "systems", "definition": "Add CRA tools and reports for EDC Extensions to add custom reports to support CRA responsibilities during trial conduct FIGURE 22.1  For a clinical data management system (CDM) or electronic data capture (EDC) system, the integration points link the data to and from other systems. Extensions to the system fill in support for elements of the data management process not supported by the system. These are some examples of integrations and extensions that companies with pro- gramming resources may consider. 194 Practical Guide to Clinical Data Management, Third Edition separately. While integration points and extensions can be very well defined, they are often hard to schedule. Some of this is due to a need for detailed knowledge of the systems in question, but it is also because of the normal problems of scheduling delivery of software. This is especially true if vendors or outside contractors are responsible for the work. Delivery of integration links or extensions can cause serious problems with imple- mentation plans when the implementation team expects that the software will be “mostly” all right as delivered and expects to test, looking mainly for bugs. A com- mon occurrence is for the implementation team to receive the software and find that there has been a major misunderstanding. To avoid this kind of major glitch, imple- mentation teams should arrange for a delivery of an early version of the software integration or extension piece (or even a prototype) early in the development process and perform a quick test to look for general correctness of the application. MIGRATION OF LEGACY DATA When new systems or applications are to replace existing ones, the question arises as to what to do with the data stored in the existing system. Sometimes the old data (legacy data) can be archived and need not be moved to the new system. But if the legacy data is actively needed, it must be moved into the new system. This process is called migration or conversion and is discussed further in Chapter 27. Because migration is such a complex process, it is usually governed by a separate migration project plan that can have a significant impact on an implementation project. The biggest question the team must address is when to migrate the data in relation to the system or application going into production. In some cases, the migration has to take place before production use of the new system because the existing data must be available from the start. Serious adverse event systems and autocoders using existing synonym tables often fall into this category. In many other cases, the legacy data is not needed immediately and can be migrated after the system is released for produc- tion. Active or recent studies in clinical databases often fall into this category. Both approaches to timing of migration of data have risks. Migration before production may turn up significant problems or necessary changes at the very last moment before production. Dealing with these problems would certainly push out the release date. Migration performed after production work has begun means that those very same problems may turn up after production release, which might have an even more serious impact. Also, later migration or a migration spread out over time means that the old system must be kept running in parallel during that entire period. BENEFITING FROM PILOTS Some pilots are performed as part of a product selection process. These determine whether the system or application would meet the company’s needs, identify neces- sary extensions and integration links, and point out necessary changes to business practices (see Chapter 21, “Choosing Vendor Products”). Other pilots take place after a selection has been made as part of the implementation project, in which case the pilot’s goals tend to be some combination of the following: Implementing New Systems 195 •\t Determining how well the entire system works together •\t Testing (and/or identifying) new business practices •\t Updating or developing standard operating procedures (SOPs) •\t Confirming configuration choices •\t Identifying (and/or creating) necessary standard objects Pilots during implementation rarely result in the system being rejected but other- wise share many of the characteristics of pilots during the selection process. A pilot plan helps both the implementation team and those working on the pilot understand the goal and scope of the effort. The plan also provides the information the implementation team needs to schedule the pilot’s place in the overall implemen- tation plan. Besides stating the goals or purpose of the pilot as outlined previously, a pilot plan would likely include information on the following: •\t Data or examples to use •\t Staff resources and training plan •\t Functions, interfaces, and extensions to exercise •\t Expected outputs of the pilot One very important variable to specify in the pilot plan is whether the require- ment is to completely process the data selected for the pilot (e.g., code all the terms if the application is an autocoder or enter and clean all the data if the application is a data management system), to touch on a certain set of features or functions, or to work only until an end date. When resources permit, the data or examples for a pilot usually come from a closed study or from a study that will be conducted in parallel using the existing system. Because of lack of resources, many companies choose to use active studies for the pilot, that is, studies that will be conducted as a part of the pilot will contain production data. Great care must be taken in scheduling such a pilot in relation to validation. If the company decides to pilot on production data before validation, steps must be included to demonstrate the validity of the data. For a clinical data management (CDM) system and paper study, this could include, at a minimum, a 100% audit of clinical study data against case report forms (CRFs). For an autocoder, this might involve a complete recoding after the system has passed validation. The outputs of the pilot should specifically address the goals. If the goal is to determine how well the system works with its integration points and extensions or to test new business practices, the output may take the form of a report summarizing experiences of the pilot team. If the goal is to evaluate existing SOPs, the output may take the form of a list of existing SOPs, whether they require updates, and what new SOPs are needed. If the goal is to confirm configuration choices, the output can use the starting configuration values and approve them or recommend modifications. When a goal is to identify and create standard objects, a report of the work completed plus a summary of what additional work is required would make a good output. An evaluation meeting should be part of every pilot. At the meeting, the pilot team and the implementation team review the outputs of the pilot. They would also record user input and comments on how well the system worked. The implementation team 196 Practical Guide to Clinical Data Management, Third Edition should expect that the evaluation of the pilot experience will result in some changes to the system, be they minor or major. Until the pilot evaluation is completed, the schedule for the final phase of the implementation—the move to production—would have to be considered tentative.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "VALIDATION", "definition": "As we will see in Chapter 23 in greater detail, validation is not just testing prior to production use; it is a process with a series of steps that start even before installation of the software product. The implementation team needs to be sure that a validation plan is drafted early in the process and approved before the major milestones, such as installation of the software, take place to ensure that the requirements of the plan are actually met. Near the end of the validation process, testing will be completed and someone will write a testing or validation summary. This document will summarize the out- come of testing and highlight any existing bugs (with workarounds) or any special restrictions placed on the use of the product (e.g., perhaps a particular feature can- not be used). The start of production use cannot begin until this summary has been reviewed and signed and appropriate actions have been taken. However, preparation for production use can begin prior to the end of validation. PREPARATION FOR PRODUCTION As release for production nears, implementation teams will focus on high-profile tasks of setting up the production environment. However, teams frequently forget to include some preparation tasks that mean the system or application is not actually ready on the day it is released for production, causing much frustration among the users. These additional tasks include the following: •\t Updating and completing SOPs and guidelines •\t Setting up user accounts; granting permissions and access •\t Scheduling customized training and refresher courses •\t Satisfying any user acceptance requirements in the production area •\t Identifying which studies or data will use the system first (rollout plan) The move to production is a critical period for any system—even small applica- tions. The more people who have experience on the system that will be available to new users during this critical time, the better. For systems built in-house, the developers should be on call. For vendor systems, arrange for special coverage by a consultant or technical support. The implementation team and pilot team can also provide invaluable assistance and handholding. SUCCESSFUL IMPLEMENTATION Most companies these days are aware of the validation requirements for systems and software applications. They will dutifully create and carry out a validation plan Implementing New Systems 197 for a new piece of software but the implementation may still fail. That is, produc- tive use of the system may be delayed if no one takes a step back from validation and sees how the new (validated) software will fit into the bigger picture of data management in a company and all the little reports and connected systems that data management uses. 199 23 System Validation Despite the fact that the Food and Drug Administration (FDA) has required valida- tion of computer systems used for electronic trial data handling in clinical trials since 1996 (see ICH E6 GCP Section 5.5.3), there is still confusion as to what valida- tion is and when it is required. There are entire courses, seminars, and books devoted to this topic, as well as FDA guidance documents. Because it is such a large topic, this chapter will only present a very high-level introduction to the concepts of, and approaches to, validation. Since most data managers will be involved in validation of vendor products, the focus will be on validating purchased systems rather than validation for systems and software applications developed in-house. WHAT IS VALIDATION? We can find the FDA’s definition of validation in its “Glossary of Computerized System and Software Development Terminology.” There, validation is defined as: “Establishing documented evidence which provides a high degree of assurance that a specific process will consistently produce a product meeting its predetermined specifications and quality attributes.” And we can also look at the FDA’s guidance on validation, which states that the FDA considers software validation to be: “confirma- tion by examination and provision of objective evidence that software specifications conform to user needs and intended uses, and that the particular requirements imple- mented through software can be consistently fulfilled.” These definitions give us the keys to what is required in performing validation—it is necessary to define what the system purports to do, establish evidence that it is doing that, and then provide sup- port that it will continue to do that in the future. We can see from these definitions that validation, even of vendor products, is a whole process that involves far more than just testing. In fact, the FDA’s guid- ance document on “General Principles of Software Validation” (Section 4.2) states: “Software testing is a necessary activity. However, in most cases software test- ing by itself is not sufficient to establish confidence that the software is fit for its intended use.” The bold font for emphasis is the choice of the FDA; clearly they consider this a very important statement to make! The rest of the guidance provides very practical and specific principles that form the basis of a validation. The validation process starts at the very beginning of system development or implementation, when information is collected on the design and intent of the sys- tem. The validation process continues throughout development and implementation as details on how the system installation and configuration are recorded. Before it is released for use, the system is thoroughly tested to document its operation and problems. When it is in production use, information on how the application should be used (manuals, guidelines, standard operating procedures [SOPs]) further helps 200 Practical Guide to Clinical Data Management, Third Edition assure continued quality of the product of the application. Changes at any time affect validation status and trigger a revalidation, in full or in part, to show that the system is continuing to work properly. All of these tasks or elements of system validation are guided by and documented through a validation plan. VALIDATION PLANS OR PROTOCOLS A validation plan (sometimes call a validation protocol) guides the entire process. It lists all the steps in the validation process, how to perform those steps, and what documentation is required from all steps. The word plan implies an intended course of action. Plans are best written before the action starts, and this is also true of a validation plan. Many companies make the mistake of putting a validation task in a project or implementation plan immediately before releasing the system for produc- tion. Validation testing may take place at that point, but the creation or opening of the validation plan belongs at the start of the process to identify which documents must be produced along the way. Good validation plans can be used with all types of software systems and levels of validation. The variations are in the level of detail provided rather than in the requirements themselves. While validation plans typically cover the same elements, the names, organization, and grouping of the various requirements vary from com- pany to company (and consultant to consultant). There will definitely be differences between validation plans for custom systems, which involve extensive software development, and those for vendor-supplied systems. Appendix E shows an example validation plan outline for a vendor product. The meaning and intent of each section in that outline is described below to provide an idea of what is involved in carrying out a validation. Introduction and Scope The validation plan begins with a description of the system. The description helps readers (e.g., auditors) understand what kind of system is being validated and what pieces make up the system. Besides a descriptive summary of what the system is for, the text should include details such as the following: •\t Version of the software application in question •\t Names and versions of other underlying or required software packages •\t Customizations that were performed •\t Server types and operating systems •\t If appropriate, client types and related versions •\t Network support These details are particularly important for larger, vendor-supplied systems. An “options” section can be used to describe what parts of the software are or are not included in the installation, and so not involved in the validation process. It is often easier to state what is not included than what is. For example, certain major function groups or modules may not be used by a company at all and so will not be included", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "System Validation", "definition": "205 would be somewhat less and focus on customization and configuration. The effort for small user-written programs and application-built systems would likely be minimal. However, all validation efforts do need to cover the same points and to provide the same assurance of quality. They must include: •\t Requirements and specifications •\t A plan to carry out the validation •\t Test procedures to provide evidence of proper function •\t A summary stating that requirements were met •\t Change control to provide continued validated functioning Many may be surprised to hear that they need a validation plan for every study database. This is the case but there are shortcuts for such things. In the case of a data- base application built in a validated clinical data management system, the validation requirements can be met as follows: •\t An annotated CRF plus the protocol can act as the specification •\t An SOP on building and testing a study database acts as the validation plan •\t The entry and edit check testing is filed as the evidence •\t A ready for production form acts as the summary of testing •\t Change control should be in effect for study databases See Chapter 5, “Preparing to Receive Data,” for additional discussion of study- level validation and Chapter 12, “Creating Reports and Transferring Data,” for vali- dation of reports. It is worth finishing this section with a reminder to apply risk assessment appropri- ately. It really is not necessary to go through validation for little reports or programs that simply provide information. The developer will, of course, test these programs in the normal way but they need not go through formal validation. Reports, utilities, extensions, and customizations that might have impact on the data should be vali- dated, but at a level appropriate to the risk they have on the integrity and interpreta- tion of the data.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Assumptions and Risks", "definition": "The assumptions and risk assessment section may well be the most important section of the entire document. The FDA is heavily promoting an assessment of how critical the software is, and from that, judging the appropriate level of validation and testing required. Section 4.8 of the FDA guidance on “Principles of Software Validation” states: “The selection of validation activities, tasks, and work items should be com- mensurate with the complexity of the software design and the risk associated with the use of the software for the specified intended use.” In other words, work harder on validating the systems and features that are critical to the integrity of the data and less hard on features that are administrative or those that have built-in checks either through technical means or through process procedures. Business Requirements and Functional Specification It is not possible to verify that a system meets its requirements in a known manner without stating what those requirements to be met are. Most validation plans now have two sections: one for business needs or requirements and one for functional specifications. As we saw in Chapter 21, “Choosing Vendor Products,” the business- needs document or list of requirements may already be available from the vendor selection process. For vendor-supplied systems, it is common to refer to the user manual supplied with the system as the functional specification. The manual serves as a description of how the system is supposed to work. It may also be appropriate to include references to release notes, known bug lists, and other supplementary material provided by the vendor to describe the current state of the software.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Installation", "definition": "The installation section of a validation plan serves to document how a system is prepared for use. Planning and documenting the installation procedure has proven to be extremely useful to every company that has done it. Even for the smallest systems, a checklist of what must be done to make the system available can help a company avoid unpleas- ant lapses—assuming the checklist is written before the installation. Note that the installation is often carried out at least twice: once in the testing area or environment and once in the production area. The checklist or procedure also ensures that the installation is performed the same way both times. 202 Practical Guide to Clinical Data Management, Third Edition For larger systems that come with a detailed installation procedure or guide, the installer should document what choices were taken or where deviations from the standard installation were made. Those notes provide a reference of those choices for future installations. When independent consultants or vendor technical staff per- form the installation, companies should make clear that an installation checklist and installation notes are required as a deliverable. Many companies require an installation qualification (IQ) process and some also require an operational qualification (OQ) process. Very few require performance qual- ification (PQ). (There are some very interesting comments on IQ/OQ/PQ in the FDA guidance on validation.) The meanings for these terms are not universal, but the intent is to assure that the system is completely and properly in place and does seem to work. The qualification, installation or operational, usually requires some light level of test- ing or verification of system output. This can be performed using vendor-supplied or custom-built test scripts or using system test programs. If the installer encounters any discrepancies or problems, these must be documented. These discrepancies may be as simple as forgetting a step and having to go back or as serious as a complete inability to install. Very, very few installations take place just once, and these notes on problems will be invaluable at the next installation.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Testing Overview", "definition": "We now recognize that the testing portion of the validation process is only one step of many, but it is frequently the most time- and effort-consuming step of the entire validation process. Controlled testing, with expected results compared to actual out- put, will provide the evidence that shows that the system performs in a known way. For complex systems, the test procedures (test scripts) would be found in one or more separate documents. In that case, the testing section of the validation plan would typically provide a high-level overview of the testing approach and pointers to the other documents. (See Chapter 24 for further discussion of testing procedures.)", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Vendor Audit", "definition": "Since the ultimate responsibility for the validation of a system falls on the user, each company must consider whether or not they believe a vendor has provided a quality product when they choose to acquire rather than build a system. Most companies, large and small, conduct vendor audits or surveys to help document their decision to rely on the vendor’s software verification processes. Ideally, the vendor audit would take place before the product decision has been made—more typically, it takes place after the decision but before the system is put into production. The vendor audit should be performed by an auditor experienced with software development as well as the FDA’s guidance documents. This is not a GCP audit in the usual sense; this is an audit of the vendor’s development practices as viewed both from the general software industry practices and from the applicable regulations. The audit may be performed by in-house IT or regulatory staff. Or, the task may be contracted out to a consultant specializing in such audits. Companies must be", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Security Plan", "definition": "Not all validation plans include information on security approaches for new systems. Some companies cover this under general SOPs; others include it in implementation plans or user guidelines specific to the system. When a validation plan does include a security section, the intent is usually to document how security fits into the picture of assuring that the system will run correctly and to show how security will maintain integrity of the data in the new system as per 21 CFR (Code of Federal Regulations) Part 11. This is particularly important when the system in question can’t fulfill all the needs of tracking who had what kind of access and when, and the group must implement specific procedures to fill the gaps (see also Chapter 17, “Controlling Access and Security”).", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "SOPs and Guidelines", "definition": "In recognition of the fact that a system includes not just computers and software but also people and processes, some companies include a section on SOPs and guidelines in their validation plans. The goal of the section is to identify which SOPs or specific guidelines will apply to the process, including which need to be updated and reviewed. This is meant to provide evidence that the new system is used appropriately.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Completion Criteria", "definition": "When is the system considered validated? A section on completion criteria includ- ing what must be in place before a system is ready for production use is essential to determining if the requirements of the validation have been fulfilled. A final test report and/or validation summary must be part of the completion criteria. Associated with the final report should be an appropriate level of sign-off on the process. Large, important systems should require higher levels of sign-off than small systems used by a limited group of people. Maintaining Validation Plans It is fairly common for a validation plan to be changed after the initial sign-off and after the installation and testing has begun. Sometimes the environment is found to be different at installation. Sometimes there is a change to testing based on new information. There may also be a last minute change in scope. The validation plan should be updated to reflect these changes and normal document revision procedures 204 Practical Guide to Clinical Data Management, Third Edition should apply. Be sure to obtain appropriate sign-off for revisions—not just the origi- nal version. CHANGE CONTROL AND REVALIDATION Systems change and will require revalidation. Revalidation is the process of repeat- ing all or part of the validation process to provide assurance that the system is still running in known way after a change has taken place. The scope of revalidation is closely tied to the kind of change made to the system. Change control systems track changes to guide revalidation and to keep systems in a validated state. In the final guidance on validation entitled “General Principals of Software Validation,” the FDA explicitly requires change control procedures. Section 4.7 is entitled, “Software Validation after a Change.” That section includes the following statement: “Whenever software is changed, a validation analysis should be con- ducted not just for validation of the individual change, but also to determine the extent and impact of that change on the entire software system.” As in the quote earlier in this chapter, the bold font for emphasis comes from the FDA. There is no getting around this requirement. Complete retesting may be called for when a major new version of product is released or when there are changes to the underlying database application or oper- ating system. More limited retesting may be chosen when the changes are bug fixes to specific problems. In a few cases, the change is expected to have no impact, so a decision may be made to simply bring up the system to see if it runs. (An example of this latter case would be a change in a network protocol underlying a client– server application.) WHAT SYSTEMS TO VALIDATE A system can be a complex software application developed in-house; it can be a small vendor-supplied software package; it can be a small user-written SAS® program to clean data; it also can be a data management study database built using a software package. Any of these systems that have an impact on the safety and efficacy of clini- cal data are subject to validation. At the time of writing, nearly all companies validate large and mid-sized custom systems and all vendor systems with direct application to GCP data. Most also require validation of all user-written programs large and small, such as SAS programs that are used in analysis. More and more are requiring validation of applications, such as study databases and data entry applications that are built using software packages but without programming. This latter class of applications is software development of a sort, since the software packages build applications that directly affect the stor- age and extraction of the data and is the one where there is still confusion as to the level of testing and documentation required. The level of validation—that is, the extent of the effort and resources applied— would not be the same for all systems and applications. The effort for complex cus- tom systems would be very high indeed. The effort for widely used vendor systems", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "SOPs FOR VALIDATION", "definition": "Small companies with a single-vendor product or application can get away with hav- ing a really good validation plan or protocol and not having an SOP. Companies that have anything more than one or two applications that apply to GCP data or are subject to 21 CFR Part 11 should have an SOP on systems validation. The SOP should define what kinds of systems are subject to validation and then lay out the key components as previously described. A validation plan template or outline may be associated with the SOP or may be developed specific to the system using the required elements laid out in the SOP. There are many resources available that can help companies develop a risk-based approach to validation for covered systems. 206 Practical Guide to Clinical Data Management, Third Edition REQUIREMENTS AND BENEFITS Validation of computer systems and applications that are used to handle clinical data is required by the FDA. There are no exceptions for old, small, or vendor sys- tems. There are no exceptions based on the size of a company. That is, “we have no resources/staff” is not acceptable. One or two carefully constructed validation plan outlines can be written to guide the validation process for a wide variety of applica- tions. Small or low-risk systems can have low levels of detail or effort; large or high- risk systems can use the same outline with higher levels of detail and effort and more references to related documents. Until one has gone through the validation process several times, it is hard to see any value to it beyond meeting FDA requirements. There is, however, consider- able value! The whole process of validation helps everyone involved with a software application understand what the system does, how it was built, how it should be run, and what must be done to keep it running. Also, because all software systems have bugs, validation lets everyone know where there are flaws and how to work around them. All of this leads to more mindful and consistent use of software applications and a higher quality of the data stored in them. 207 24 Test Procedures As we saw in Chapter 23, one step in the system validation process is testing to provide evidence that the system behaves as expected. For larger systems, such as clinical data management systems, this is a huge job. First, the scripts have to be developed; this can take months even if people are dedicated to the task. Then after the tests are ready and the validation process has actually started, the scripts have to be run in the test environment. Because this is a critical point in the validation pro- cess, all eyes on the testers and the test outcomes. The pressure is on because every- one is watching the timelines to move the system into production, and stress levels rise every time a problem is encountered. From the practical point of view, no matter how much we talk about the validation process, the success and smooth implementa- tion of a software application or system rides on the testing step of the validation. This chapter aims to help data management groups get the most out of testing by setting up appropriate test procedures or scripts and carrying them out in such a way as to really provide documented evidence of how the system works. While the approaches discussed here apply most closely to larger system validation efforts, they also apply to testing of other software applications, including study database testing in the form of user acceptance testing.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "TRACEABILITY MATRIX", "definition": "We can’t even begin to write test scripts until we know what it is that will be tested during this validation and what it is supposed to do when we try it. The introduc- tion and scope sections of the validation plan tell us what is in and what is out of scope for testing. The user requirements and functional specifications list what the system can do or is supposed to do. Reconciling these two together provide us with a list of features to be tested. We add to this list requirements from 21 CFR (Code of Federal Regulations) Part 11, such as access control or automatic audit trail, which are not explicitly listed in the product’s features. The combined list of all items to test becomes the first column of a table or matrix known as the traceability matrix or testing matrix that will provide an index or overview to test scripts. Additional columns in this matrix indicate where the expected behavior for that feature or function can be found in the specifications. As the scripts are written or laid out, the applicable script and procedure identifiers are added to each row of the matrix. So, for example, if a data management group needs to be able to perform double data entry with third-party arbitration, a few rows of the test matrix make look like those found in Figure 24.1. For User Acceptance Testing, the matrix might include rows as shown in Figure 24.2. 208 Practical Guide to Clinical Data Management, Third Edition", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Each script or test procedure has a header. That header repeats information from the", "definition": "traceability matrix to identify what is being tested and what functional specification applies. The header may also include information on the applicable software version, the original script writer, and perhaps script version information. Scripts also need to identify the prerequisites. This should include what other scripts must be run first, what test data is required, and whether any external files are needed. By reading the header and prerequisites, the tester should know exactly what is needed before the script can be run. The different ways companies choose to specify the actual steps to follow in the script vary in their level of specificity. Some companies may choose to specify the actions in such detail that someone only lightly familiar with the systems can still carry them out. Other companies will describe the actions at a much higher level for knowledgeable system users. For example, an action described at a high level might be: “Enter the test data for the first 10 forms of the eCRF.” The same action with more detailed steps might be written as: 1.\tSelect the Enter function 2.\tFrom the Subject menu, select Register 3.\tRegister the test subject 4.\tSelect form 1, Demog 5.\tEnter the data as shown and click SUBMIT And so on. The level of detail describing the action is dependent not only on the expected tester but also on the level of detail required by the outcome of the test. If a Feature/Requirement", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Two-pass Entry", "definition": "Data Entry Manual, Chapter 2, pages 22–28 Test Script 2, Procedures 1 (first pass) and 2 (second pass)", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Arbitration", "definition": "Data Entry Manual, Chapter 3, pages 31–40 Test Script 2, Procedure 3 FIGURE 24.1  Two rows from a traceability matrix covering double data entry for a pur- chased clinical data management system. This example assumes that the correct storage and retrieval of the entered data is tested separately.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "eCRF", "definition": "Study Database Specification section 1", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "IVRS-01", "definition": "FIGURE 24.2  Several rows from a traceability matrix for user acceptance testing of an EDC study application. The broader categories are further refined in the actual test procedures.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Test Procedures", "definition": "211 through the scripts, the reviewer should read all the user outcomes and make sure they make sense—there is always a possibility that the tester misunderstood the step instructions and went astray without realizing it. The reviewer may also spot cases where the tester reported an outcome that seemed OK at the time but looks suspi- cious in the context of the results of test completion. This may be a discrepancy that should be added to the incident log for this script. After reviewing the results and output, the reviewer turns to any discrepancies or incidents associated with the script. The reviewer adds to the incident description any additional contextual information and begins to research the cause. Discrepancies are not necessarily bugs in the system. They may be due to user errors or script errors. They may also be surprising, but expected, behaviors. And of course, a dis- crepancy may be a real bug or flaw in the system. In addition to determining the cause of the discrepancy, the reviewer deter- mines the appropriate action. For user errors, the script may or may not have to be rerun. For script errors, the script may have to be revised and may, or may not, have to be rerun. For surprising behaviors or bugs, the reviewer may need to be in contact with the vendor and consider some way to document the behavior for future users. When the incident is a real bug, the resolution would be a bug report and a workaround if any (including training). On occasion, there may be no work- around and an entire feature may have to be declared off limits and business plans appropriately revised. Most rarely, a bug would be so serious as to prevent the system from being used. Clearly, the reviewer’s job is a critical one. A person assigned to the role of reviewer should be one of the people most experienced with the system being tested. That person should also have established a means of contacting the vendor (via hotline or email) before the testing begins. In some cases, companies may want to arrange special technical support from the vendor for the reviewer during testing.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "TRAINING FOR TESTERS", "definition": "Plan on training the testers on how to test, not just on how to use the system. Testers need to understand that finding problems is a good thing, not a bad thing to be avoided. The goal is to identify all possible issues to make sure there are no large problems hiding behind seemingly insignificant discrepancies in expected results. Train the testers to read the header of the test script first and then review all the steps before carrying them out. Before starting a test script, the tester must make sure that all the prerequisite steps have been met and that all test data and external files are present. The tester should also be sure he or she understands the steps to be carried out and the kinds of output the script requires. Testers also need to be instructed on how to fill out the test script results and label output documents. Explain to the testers that these are regulated documents. They should always use pen and sign and initial as required. They should use actual dates. Testers should never: •\t Use pencil or white-out •\t Transcribe their messy original results to a clean copy of the script •\t Rerun the script without checking with a reviewer All printed output from the testing must be labeled with enough information so that it can be linked to the step that was carried out to create it. Proper identifica- tion also applies to electronic (file) output, but in this case, the identification may be through the filename as the user should not modify the actual output file. All testers should know what to do if there is a discrepancy between the expected outcome of a step and the actual outcome. Training should emphasize that in report- ing the incident, the tester must provide enough information for a reviewer to under- stand what happened and what steps came right before the incident. Testers need to know that they should not continue after a system error message. Rather, they should stop and contact the responsible reviewer to see if that one test, or even all testing, must be halted.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "REVIEWING RESULTS", "definition": "Most validation testing does not need to be witnessed as it is conducted. That is, a reviewer typically does not need to be looking over the shoulder of the tester to confirm results on the spot. However, the idea of an observer or on-the-spot reviewer may have value at very high-risk points where the outcome would affect all other procedures. This situation tends to come up more during installation and operational testing phases than it does during user validation testing. For validation testing, a reviewer should go through all the scripts, associated out- put, and incident reports as soon after the script was run as is feasible. While going", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "TEST OUTCOME", "definition": "Testing is hugely useful. If the system is one that is new to the company, testing gives a wider audience a chance to put their training into use and become familiar with the behavior of the software. If the testing is for an upgrade, users may think the testing is a waste of time, but this is not true. Testing will provide confirmation that previously reported bugs have actually been fixed and that workarounds are no longer needed. It will also identify any new problems that need new workarounds. Understanding the limits and power of the system will improve the quality of the data stored in it, and reduce frustration of users. The result of testing will be lost over time if it is not documented. A test summary or a summary of testing as part of the validation summary is not only a good idea but is often considered a required part of validation. This testing summary is not the lengthy incident log that reviewers have been working with; it is a brief summary of the number of problems found and the typical causes. It should highlight actual bugs and focus on any limits to production use that those bugs place on the system. Any special or corrective actions taken should be very clear. 212 Practical Guide to Clinical Data Management, Third Edition RETAINING THE TEST MATERIALS Obviously, the script hardcopies used during testing and all the output and outcomes, along with the incident log and testing summary, must be retained as evidence of the validation. It is also vital to keep the test scripts in their electronic form. All systems will need to be revalidated in whole or in part when changes, such as patches, bug fixes, and upgrades, are made to the software product itself or to underlying base software. As we will see in the next chapter on change control, each change will be evaluated to see what level of testing is required. The most thorough kind of testing after a change is regression testing where the original scripts that were previously run are run again on the new version—with the results expected to be the same. Having access to the electronic versions of the scripts as well as to previous output is an essential part of regression testing. 213 25 Change Control In Chapter 23, “System Validation,” we learned that validation is commonly under- stood to mean the establishment of evidence that a computer system does what it purports to do and will continue to do so in the future. The process of validating a system involves defining what the system purports to do, establishing evidence that it is doing that, and then providing assurance that it will continue to work cor- rectly in the future. Modifying a system may change what it purports to do, which will leave it in a state of having no evidence that it is still doing what it is supposed to do. Change control provides the framework for ongoing documentation of what the system should do and providing evidence that it does do it. The change control (or change management, as it is sometimes called) process involves documenting needed changes, implementing the changes, and testing the impact of the change. Change control is required for validated systems. In the Food and Drug Administration’s (FDA’s) guidance on “Computerized Systems Used in Clinical Investigations,” Section IV.F.5, we learn: “The effects of any changes to the sys- tem should be evaluated and some should be validated depending on risk. Changes that exceed previously established operational limits or design specifications should be validated. Finally, all changes to the system should be documented” (author’s emphasis). This statement nicely summarizes what is required. This chapter will dis- cuss the requirements for controlling changes in a bit more detail by looking at what falls under change control and exactly what issues should be covered in evaluating the impact of each change. While change control for large systems is frequently the responsibility of IT groups, at smaller companies data managers may have responsibility for tracking changes to data management systems. Even at larger companies, data managers should understand the change control procedures to provide guidance to IT for sys- tems they use. Even more important, the responsibility for tracking and implement- ing changes to study databases falls squarely on data management at all companies. WHAT CHANGES SHOULD BE CONTROLLED? Any and all validated software applications or systems require change control. In the world of clinical data management, that applies to the clinical data management (CDM) or electronic data capture (EDC) systems, and also to the study databases that are built using those systems. In any change control process, the first thing that must be established is what constitutes a change. Changes to Software Systems For larger systems, such as CDM and EDC systems, it is important to understand that they are more than a single program, package, or application. Those systems 214 Practical Guide to Clinical Data Management, Third Edition incorporate hardware, computer operating systems, and associated applications such as databases and even compilers. Generally, a change to any of the underlying parts of an application must be viewed as a change to the system itself and be subject to change control and evaluation or risk. For software systems, applications, and programs, anything that does the follow- ing should be considered a change: •\t Introduces new versions of software at any level •\t Implements bug fixes in the program source code •\t Implements bug fixes in the form of patches •\t Affects system or site configuration files or parameters •\t Modifies system objects The first three bullet points cover changes to the system or application software directly and are easy to understand. The last two bullets are often overlooked because they don’t change the installed software. They do, however, impact how the system is used. Changes to Study Databases In the case of study databases built using a CDM or EDC system, the idea is to sub- ject to change control a wide variety of modifications with emphasis on anything that might possibly affect the storage, retrieval, or analysis of safety and efficacy data. This includes the following: •\t New fields or forms •\t Modification or removal of a database field, including changes in width •\t Changes to entry screens and forms of all types •\t New, deleted, or otherwise modified programs for calculated items •\t New, deleted, or otherwise modified edit checks It usually does not include: •\t Addition of new users or changes in permissions (as this is usually recorded elsewhere) •\t Any changes made prior to a study being released to production Some modifications, such as adding codes to an existing codelist, may or may not need to be tracked as the impact is system dependent and should be guided by whether or not there is an impact on existing data or studies. (See also Chapter 5, “Preparing to Receive Data,” for additional comments on change control for studies.) DOCUMENTING THE CHANGE In both the case of changes to system software and changes to study databases, data managers may cry: “But my change won’t impact the data!” That may be true, but that is what evaluation of the change is all about, and as we saw previously, the FDA", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Change Control", "definition": "217 or data management electronic folders. Providing a link to the change log (which has the overview and association to any protocol amendments) through a change number associated with all the documents makes retrieving evidence in case of an audit easy.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Assess the Impact", "definition": "For each change, a user, programmer, or IT staff member assesses the impact on the system or study. The key point is to assess the risks to existing data or data man- agement activities that the change will introduce. In making the assessment of the impact, include information on: •\t The amount of time the production system will be unavailable •\t Any required changes to documentation 216 Practical Guide to Clinical Data Management, Third Edition •\t What, if any, user training is required •\t Whether any SOPs are affected •\t All interfaces or reports that are affected •\t The level of testing or validation required Going through the questions of impact assessment may help clear up the question of whether opening a validation plan is warranted. The greater the impact, the higher the risk, the more systems affected, the more likely the need for a full revalidation. For small, localized system changes, the change control system may fill the need for documentation and testing, whereas it would not be sufficient for covering the infor- mation needed for a more widespread change.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Plan Testing", "definition": "Testing is essential for nearly every change—at an appropriate level. Specific bug fixes to the system can be tested against the conditions that caused the bug origi- nally and against the normal working conditions. System changes that have wider impact may require light testing across many areas or features. New releases of an entire system would typically require complete retesting of the system. For study databases, it is typical to require testing of any new or changed elements similar to that performed prior to production use. As with the assessment of impact, an assess- ment of the level of testing may help determine whether or not a validation plan is warranted. If a lot of testing is required across several areas, then a validation plan may be warranted. For some changes, defining appropriate testing may be surprisingly difficult because they are meant to fix bugs that only show up under rare or unusual circum- stances. In these cases, it may be sufficient to find some way to demonstrate that the change was properly and completely implemented and installed and then to confirm that the normal behavior is unaffected. Checking the implementation may include printing out new configuration parameters, displaying the changed source code, or showing that a patch is now recognized by the system via a special version number.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Document the Outcome", "definition": "When a validation plan is used as part of a system change, the plan will have test scripts associated with it. The validation plan, test scripts, test outcome, and valida- tion summary all need to be retained. It is very likely that systems will have more than one change and more than one set of testing associated with them before a com- plete revalidation. The change control log or system provides the overview, and the details are supported with other documentation. Making a link between the change and documentation is very helpful for systems and can be done by tagging any vali- dation plan or independent test results with a change number. A similar technique is called for when study database changes are made. Phase II and III studies frequently have multiple changes associated with them over the course of a trial. As previously noted, each change will have testing and associated documentation. These documents will go into the data management study binder", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "RELEASING CHANGES", "definition": "Whenever possible, the implementation and testing of changes should take place “on the side” so that they do not affect production use until after they have been tested and approved. This is not always possible, so testing may have to take place in the production environment. Clearly the system should not be in use at that time, and there must be a way to get back to the original state in case testing turns up a problem. When the testing is complete and all other requirements for documentation, train- ing, review, and so on are met, then the change can be released for production use. The release may involve duplicating the change in the production environment or making the production environment available for use. It is important to record the actual date and time it was released for production and to record who made (and reviewed) the final release. For most, but not all, changes to classic CDM systems, the change is applied to the software, or in the case of a study database change, the change is applied to the data in place. That is, the study data is not moved or copied. This is not true for all EDC systems, some of which require a copy or migration of the data. Some significant updates to the databases underlying CDM systems may also require a migration of the data for studies built with the system. Whenever data is transferred or copied, 21 CFR (Code of Federal Regulations) Part 11 comes into play. When data is migrated or copied, the data must be shown to be complete and unchanged. This is usually done with a comparison of a prechange snapshot of the data to a postchange snap- shot. See Chapter 27 for a further discussion of migrating data.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "PROBLEM LOGS", "definition": "Some auditors consider problem logs an essential part of system change control. A problem log is an ongoing record of problems reported by users. When a user detects a bug or notices a problem or inconsistency in the software, that person reports it to the responsible system’s person. That person will research the problem and attempt to identify the cause. This research is an essential part of problem log maintenance for problems that are a result of the following: •\t Due to user error—the researcher may recommend broader user training. •\t Misunderstood system designs—the researcher may explain to the user and perhaps also provide feedback to the developer or vendor that this is not a clean design. •\t Rare or not reproducible—the research records everything that is known in the hopes that the information will help the next time the problem shows up (if ever). 218 Practical Guide to Clinical Data Management, Third Edition •\t A true bug—the researcher reports the bug to the developer or vendor and attempts to identify any available workarounds or patches. If the bug is seri- ous and a patch is available, this may lead to an upgrade. CONSIDERING VERSION CONTROL Change control for software or applications written in-house often includes an ele- ment of release management. This means that each production version is archived and the differences between versions are understood in detail. (An archive copy is different from a backup in that the archived version will not be overwritten. It pro- vides a permanent record of what was released for production.) Implementing release management or version control can be as simple as keeping a copy of the program or application along with a description of what is in the version on a CD or in a specified directory. Or, version control may rely on specialized software. Software vendors routinely use release or version control programs both to track changes and to document releases. The more sophisticated version control programs can list differences between versions, identify which bugs were fixed in a new release, and otherwise allow for extensive annotation of each release. These version control programs can be used not only for actual code or executable versions but also for related files such as configuration or formatting files. Biopharmaceutical companies that produce large systems or significant numbers of applications might consider using such programs. THE VALUE OF CHANGE CONTROL The value of change control is to make people think through a change before mak- ing it. Before the requirements of 21 CFR Part 11 were commonly applied to study databases, data managers would often make a change without any thought to impact on existing data or processes. With change control, and especially if a policy of review of proposed changes is in place, the thought required to show that the change was properly and completely implemented and that other data or functions were not adversely impacted prevents many careless mistakes. When study databases were used by in-house staff only, mistakes might have been just an inconvenience, but now, when EDC is involved, changes immediately impact sites so the care taken prior to making a change prevents mistakes from having a significant impact on the conduct of the study. 219 26 Coding Dictionaries", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "and Systems", "definition": "Some kinds of data are collected as free text but must eventually be grouped together into like terms for review and analysis. The most common examples of this kind of data are adverse events, medications, and medical history. In all of these cases, the investigator reports a term with little or no guidance on terminology or wording. Yet, to make assessments of the safety of the drug, like terms must be counted or classi- fied together. For example, the adverse event terms headache, mild headache, and aching head should all be counted as the same kind of event. Similarly, the drugs reported as Tylenol and acetaminophen should be classified as the same drug. The process of classifying the reported terms by using a large list of possibilities is known as coding. The list of possibilities or standard terms is known as a coding diction- ary or thesaurus. The part of the coding process that is performed by specialized com- puter programs is known as autocoding, and the programs themselves are autocoders. Data management always has responsibility for collecting the terms to code in clinical trials because they arrive with the rest of the subject data. Data management may also have the responsibility for the coding process and dictionary maintenance, or these tasks may fall all, or in part, to a specialized coding group. In the case of serious adverse events, the company’s drug safety group may also play a role. Generally, data management takes on more of the coding tasks in smaller companies. In larger companies, more of the responsibility for the coding process and dictionary maintenance goes to specialized coding groups. Discrepancies do arise during the coding process. These are generally, but not always, registered and managed by data management staff, which also makes edits to the stored data for paper-based trials when the discrepancies are resolved. In this chapter we will look at dictionaries, autocoders, and the coding process, and then touch on some of the issues surrounding the maintenance of the dictionar- ies themselves. COMMON CODING DICTIONARIES The various dictionaries or thesauri used in coding have different structures but many similarities. There is usually a term, phrase, or name that identifies the adverse event, drug, or disease in question and a related code. The code is not always numeric—it may well be just another text version of the reported term. Associated with the reported term and code is a grouping term or a preferred term. In addition to terms and codes, dictionaries generally have auxiliary tables or columns with related information. For example, adverse reaction dictionaries have information on affected body systems. Drug dictionaries may have additional information on key ingredients or manufacturers. 220 Practical Guide to Clinical Data Management, Third Edition The Medical Dictionary for Regulatory Activities (MedDRA®; see next section) is the standard for coding adverse event terms, medical history, and similar reported terms. There is more choice for choosing dictionaries for coding medications, but the World Health Organization Drug Reference List (WHO Drug; see the section “WHO Drug” in this chapter) is the most common, but many small companies will start with a medication dictionary or list they develop on their own that includes those drugs most common to their area of research and development. As they grow, these companies will move to one of the more standard drug dictionaries.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "MedDRA", "definition": "MedDRA is a clinical validated medical terminology list used to classify adverse event information associated with biopharmaceuticals and medical devices. MedDRA was developed by the International Conference on Harmonisation (ICH) under the aus- pices of the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use. An international maintenance and support services organization (MSSO) maintains and updates MedDRA, pro- vides licensing arrangements, and distributes the dictionary. In March 2003, the FDA issued a proposed rule mandating that MedDRA be used for postmarketing submission of individual safety reports. The European Medicines Agency requires that all serious adverse event (SAE) reports and all periodic safety update reports (PSURs) be submitted using MedDRA codes. Japan and Canada also require or recommend the use of MedDRA coding. The sophisticated structure of this dictionary supports reported or low-level terms that map to single preferred terms. These preferred terms in turn have associations with higher-level groups and system organ classes.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "WHO Drug", "definition": "The WHO Drug dictionary for medical product information was created by the WHO Programme for International Drug Monitoring and is managed by the Uppsala Monitoring Centre. WHO Drug allows biopharmaceutical companies and regulatory agencies to consistently identify drug names, active ingredients, and therapeutic uses for drugs or compounds typically reported under concomitant or suspect medica- tions. The most recent versions are referred to as WHO Drug Dictionary Enhanced or WHO-DDE. The hierarchical structure of the dictionary includes trade or proprietary names and a preferred or nonproprietary (generic) name. Additional information associated with the drug name includes the manufacturer’s name and whether the drug has a single or multiple ingredients.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "USING AUTOCODERS", "definition": "Basic versions of autocoder programs take a reported term and look for an exact match in the lower-level terms column of a dictionary table. These work well with drug and disease dictionaries where the reported terms tend to have little variation. Coding Dictionaries and Systems 221 They do not have as high of a match rate for adverse event terms where the variation in the wording of reported terms is high. More sophisticated autocoders do simple text transformations on the reported term in an attempt to improve the likelihood of a match to the dictionary. The transformations may include removal of punctuation or removal of extraneous words (such as “patient complains of”). Even the most simple text transformations improve the match rate for adverse event terms. To understand how autocoders work at a specific company, we have to understand the details of the following: •\t How the term is collected (as this can impact the coding success) •\t The results the autocoder returns when it finds a match •\t The support, if any, the autocoder provides when no match is found", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Collecting the Term", "definition": "The first part of the coding process involves collecting and storing the reported term (which may be an adverse event term, a drug name, or a medical history diagnosis). The term is reported on a case report form (CRF) and entered into the database or through an electronic CRF (eCRF) form and is immediately in a database. When entered into an eCRF form, the term is typically “cleaner” than that found on a paper form because on paper, sites tend toward abbreviations and symbols. Misspellings, however, will be common in whatever way the term is reported. In paper studies, data management may find some terms that are hard to read, that contain abbreviations or symbols, and terms that are longer than the database fields. Company-specific guidelines specify how to handle these events in general, and data management should be aware how those guidelines specifically affect the ability of autocoders to code these terms. All variations in the reported term will make coding, especially autocoding, more difficult. Therefore, there is great temptation to make modifications to the term that will make it more standard. Those changes that can be clearly specified by entry guidelines may be allowed. For example, symbols may be left out or replaced with standard text (e.g., ↑ with increased). Some companies permit correction of obvious misspellings based on a study-specific, predefined list. Other companies feel that “obvious” is unclear to begin with and that staff without medical training may make incorrect assumptions. As a compromise, a few companies have a secondary internal field, which does not appear on the CRF, to collect a version of the reported term that is more standard and more likely to code. For both paper and EDC, the autocoder is not run immediately after a user sub- mits the data. Autocoders are typically run once a day for EDC systems, and once a day but only after second entry (or quality review) for paper studies. Some companies may batch coding work or out-source it to specialized coding firms or contractors, but in all cases, data management or coders should run coding regularly throughout the course of a study rather than at a few milestones or infrequent time points along the way. Coding problems need to be identified on an ongoing basis, just as other discrepancies are, in order to improve the quality and timeliness of resolutions from the site. 222 Practical Guide to Clinical Data Management, Third Edition", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Storing the Results", "definition": "When autocoders successfully find a match, they store the resulting code or pre- ferred term and other information found in the dictionary hierarchy in the database along with the rest of the subject’s data. At some smaller companies, the matches will be left in a coding dataset and joined to the subject data dynamically at the time of analysis. The result of the “match” may be nothing more than a code or text associated with the reported term since the additional dictionary information can always be found via the unique code or term. But, as previously noted, dictionaries have auxiliary information associated with a code and this information is typically stored in additional columns or tables linked by code or preferred term. Some com- panies find it convenient to add in that information to the subject or coded record itself. Examples of auxiliary information include body system for adverse events or generic name for drugs. Sophisticated autocoders that support a complex process and more sophisticated algorithms often support information called something like coding status. This sta- tus is associated with the reported term and code to identify how the term was coded. The status values might indicate that the code for a reported term falls into one of these categories: •\t Provided automatically by the autocoder from the main dictionary •\t Provided automatically by the autocoder from a synonyms table •\t Found via lexical transformation •\t Assigned by the autocoder but manually overridden •\t Manually coded All of these states are used by the autocoder itself to determine future process- ing (e.g., never recode a term that was manually coded or overridden) and by data management reports to identify what coding assignments may require review. For example, many data management groups require review by the medical monitor of all codes assigned manually and review by a specialist of all codes assigned via a lexical transformation.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Failure to Code", "definition": "When an autocoder fails to find a match, a person must review the term and deter- mine the appropriate association to a term in the dictionary, or decide that the term cannot be coded as it stands. Coding tools offered by the thesaurus/dictionary main- tenance organizations or tools provided as part of the autocoding software support manual assignments. In some cases, the manual coder will have access to the coding fields in the study database and will enter the information found by browsing the dic- tionaries. In other cases, the coder makes an assignment that is permanently stored in separate synonyms tables. In the first case, once the assignment is made, it does not improve autocoder matches in the future. When the same term comes in again for the same subject or some other subject, it must be manually coded again. Losing the association also means that the same term may be coded differently in the future. Coding Dictionaries and Systems 223 Autocoders provide major improvement to the coding process when they store the association of all new, nonmatching terms to their codes in a table called a synonyms table. The next time that same term is reported, the autocoder checks the diction- ary first and then the synonyms table, thereby reusing the association, reducing the manual coding effort, and assuring consistency of coding. The information in these synonyms tables has further value; it can be used as an audit trail of all manual assignments, making review and sign-off by a medical mon- itor easier. Also, reports or analyses periodically run over the associations can look for cases where similar terms, which really should be coded to the same code, actu- ally are, and that medically different terms were coded to different codes. Synonyms tables are also used as part of thesaurus upgrades to identify terms that did not code directly to the dictionary in the past version, which do now code. To aid the (still) manual process of making a code assignment to a new term, some autocoders have tools to present lists of potential matches to the coder. The list may come from a simple wildcard match, a “sounds-like” algorithm, or some other transformation of the reported text. Aggressive algorithms used to present possible matches to a human coder tend to be more acceptable than when the autocoder uses them to make automatic assignments without human intervention. Figures 26.1 and 26.2 show two kinds of coding processes. Figure 26.1 illustrates the workflow when a company does not use a synonyms table. In this case, the auto- coder is run, but if it fails to find a match, the assignment of codes to terms will take place through a manual step in which the coder reviews the data record and edits the one record to provide a code. Figure 26.2 illustrates a workflow that makes more use of an autocoder. In this case, if the autocoder fails to find a match, there is still a manual step to assign a code, but the coder updates the dictionary or associated tables rather than the data record. When the autocoder runs again, these new assign- ments of terms to codes are picked up and stored in the data records automatically", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Action", "definition": "1. During a visit as part of a clinical trial, a subject reports an adverse event term of “headache in front.” 2. The term is reported via an eCRF and is in a database immediately or on a CRF and is later entered into a database by data entry staff. 3. Sometime after entry into the database, an autcoder runs but does not find an exact match in MedDRA. 4. A coder manually associates the reported term with “HEADACHE FRONTAL” by updating a synonyms table with the association. 5. The next time the autocoder runs, it finds the assignment and fills in the fields associated with the reported terms. 6. Two months later, the subject reports “headache in front” again. 7. Sometime after entry, an autcoder runs and finds the match. FIGURE 26.2  An example of the steps in a coding process where the autocoder makes all updates to the data record and the company updates dictionary synonym tables when making a new assignment of a code to a term. Even though the number of steps is the same, this time a coder is involved only once, the first time the term is reported. Coding Dictionaries and Systems 225 Adverse events, especially serious adverse events (SAEs), may be collected directly by the safety group and entered into a separate safety system. Those reported terms must also be coded. As noted in Chapter 11, “Collecting Adverse Event Data,” the process of reconciling involves comparing SAEs in safety and those in clinical data management (CDM) systems to assure that serious events are present in both systems and match medically. If the data management group and the safety group are using different autocoding algorithms or even slightly different dictionaries, the chances increase that the same reported term will be coded to different codes. If they are compared at a preferred term level, this will even out differences. But even look- ing at grouping terms, there may be differences that must be ironed out by the medi- cal monitor. In an ideal world, the groups will share the same set of dictionary tables, and the autocoder will be the same, or will at least use the same algorithm for coding in order to minimize this problem—but that ideal case seems to be very rare. DICTIONARY MAINTENANCE New releases of the common dictionaries appear every frequently. In MedDRA’s case, for example, new versions are released twice a year—a full release in March and a maintenance release in September. Maintaining the dictionaries at a company is a challenging task—not because of technical issues, but because of the impact changes in the dictionary contents have. Release notes for new versions must be carefully studied and the impact of the changes understood before the new version is put into place. For example, it sometimes happens that in a new dictionary version an existing term is associated with a new code or text term. In this case, data man- agement and safety groups must consider what to do if that term in existing studies has been coded to the old code. Changing the assignment in existing data and locked studies might have impact on serious adverse events reports to the FDA, other inte- grated safety summaries, nondisclosure agreements (NDAs), and other analyses. Every company should have maintenance and upgrade plans for dictionaries in place. One plan is a technical plan for the loading of the data and the verification that it was loaded properly. (This is not a validation plan, because typically no soft- ware is being installed—only data is being loaded. Data loading for dictionaries must be verified.) A second plan deals with what to do with existing clinical study data. Presumably locked studies are not unlocked for recoding, but their terms may be recoded on the side for safety summary reports. Open studies may or may not be switched to the new dictionary as that implies recoding and this can impact not only outstanding queries but also any reports and interim analyses that have already taken place. Another challenge of dictionary maintenance is managing the impact of the new version on company-specific modifications or additions. If a company has added terms directly to the dictionary tables, these terms could be overwritten by a new release, or new terms may duplicate terms added by the company. If reported terms that are manually coded were stored in separate synonym tables, these now may code automatically in the released dictionary or there may be a new, more appropriate code in the new version than existed in previous versions of a dictionary. These cases all require evaluation of all synonyms in use. To allow for the most flexibility in support 226 Practical Guide to Clinical Data Management, Third Edition dictionary versions, data management groups should try to configure their dictionary tables and autocoders to support more than one version of a dictionary at any time. The following are a few more points regarding dictionary management: •\t All past dictionary tables should be maintained as archive data essential to the interpretation of previous studies. •\t Permission to update all dictionary tables should be restricted to those staff members trained in appropriate processes and procedures. •\t The dictionaries being used, along with their versions, should be recorded (and the information kept current) in the data management plan for each study or there should be reference to the upgrade schedule. QUALITY ASSURANCE AND QUALITY CONTROL FOR CODING Coding assignments provide an important piece of safety data, whether the term is an adverse event term, drug, or disease. Quality assurance and quality control for the cod- ing process have two different targets: the autocoder and the manual assignments. Because the autocoder program in effect creates data (the code), it must be held to the highest level of quality development, testing, and validation. Be aware that test data is rarely enough to turn up problems with the autocoder; it is only the variations in and volume of real data that exercise the program to the fullest. Therefore, even after putting a new autocoder into production use, companies may choose to review all autocoded assignments for a period of time or on an ongoing basis to assure cor- rect working and quality. For assignments made manually, periodic review can prevent and detect coding con- sistency problems. In simple systems, the review may be performed visually as quality control. In systems where new coding assignments are stored, a variety of reports or analysis can help pinpoint areas where similar terms have been coded differently or have been assigned inappropriate codes. Clearly, a process must be in place should an inappropriate assignment be found, keeping in mind that a change in coding assign- ment may impact previously coded data or data coded in other ongoing projects. SOPs FOR CODING AND DICTIONARIES While the coding process may be covered by lower-level documents and training, an SOP recognizes that coding creates clinical data and documents a company’s approach to quality coding. A coding SOP should document that an autocoding pro- cess is being used and provide guidelines as to required review of autocoded terms. If terms are extracted and coded externally, the SOP procedures should recognize the importance of 21 CFR (Code of Federal Regulations) Part 11 requirements for copied and transmitted data (see also Chapter 12). The coding SOP should discuss in detail the manual coding process and any steps to ensure quality and consistency, including review and approval or manually coded terms by the medical monitor. Other docu- ments associated with the SOP can address detailed coding guidelines and company policies, including the question of splitting terms and correcting spellings. Coding Dictionaries and Systems 227 Maintenance of the coding dictionaries can be covered in a separate SOP. That SOP should lay out the requirements for updating the dictionaries, but the more important content should cover company policy on the impact to open and closed studies when dictionary contents are upgraded.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "EFFECTIVE CODING", "definition": "The coding process is most effective when it integrates data management systems, practices, autocoders, data managers, and coding experts into one workflow. Good data management systems and practices will collect adverse event terms accurately and will identify many problem terms early. Autocoders run routinely as part of the data handling will also identify problems and discrepancies early. By giving coding experts access to the output of autocoder runs throughout the study (rather than only at critical points or near the end), they will have more time to devote to resolving problems and reviewing difficult coding decisions. A basic autocoder should be considered a requirement by every company. The more steps of the coding process the autocoder can support, the more effective the process will be. One of the most important features to improve effectiveness is the ability to store the association of new, nonmatching terms to the appropriate codes as synonyms. This both increases match rates over time and also helps ensure coding consistency. For highly varied adverse event terms, an autocoder may have to sup- port text modification algorithms to increase the match rate or to make the manual assignment process more effective. 229 27 Migrating and", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "ARCHIVING DATA", "definition": "Until fairly recently, companies tried to migrate all existing data through to new systems at every upgrade or implementation of a new application. At some point, this Migrating and Archiving Data 233 process becomes unwieldy because of the volume of studies and also because of the lack of understanding of the old data structures. When bringing along all studies has low value, a decision is made to archive the data. As the archive structure is designed, the first question that arises is whether the data will need to be readily accessible on short notice or whether it is permissible to require some level of effort to resurrect it. The second question then becomes what should be included in the archive. Level of Archive Access Studies that are several years old, but still part of an active product line, should prob- ably be available without much effort. For these studies, questions often arise as to adverse or serious adverse events and these questions may require a review of the study data. There may also be new questions about efficacy, impact on other safety variables, or groupings of patients brought up by new studies that warrant a look back at older studies. While the data for all submissions will be available in SAS form, there may be good reasons to go back to the original data as it was entered. The easiest way to make sure data is available is to archive the data to the same platform that the new application is on. For example, if the new system is built on top of Oracle, consider archiving the old studies in a compatible version of Oracle exactly as they were when they were locked—without any transformation. This plat- form provides ready access through standard interfaces and query tools even if the data cannot be viewed through the new application or system. The archived data is kept separate from production data and under tight security so that anyone who needs to can point to it and read it but no one can make changes. (It should be noted that data for old studies or studies in product lines that have been discontinued, need only be—and should only be—saved for the appropriate record retention period. That period should be spelled out explicitly in company policy and should conform to FDA regulations. Electronic data should be covered just as paper records are.) When data for studies that are older or for discontinued lines still needs to be retained but does not need to be readily accessible, other formats for data archive can be considered. XML, PDF, and SGML are all currently being discussed as ways of archiving data. These and other options for long-term storage are open to consid- eration. As the FDA says in the guidance document for 21 CFR Part 11, “Scope and Application,” Section C.5, “FDA does not intend to object if you decide to archive required records in electronic format to nonelectronic media. … As long as predicate rule requirements are fully satisfied and the content and meaning of the records are preserved and archived, you can delete the electronic version of the records” (by which they mean the original versions of the records).", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "COMPLEX MIGRATIONS", "definition": "When a migration is not a simple migration, there are usually still some options available with different levels of effort required. The least problematic approach sim- ply meets the requirements of the new system but otherwise leaves the structures of the old system unchanged. That is, data groupings, variable names, data types, codelist associations, and other data attributes are left unchanged. Data transforma- tions are performed only when there is an unavoidable conflict between the require- ments of the new system and the data in the old (if, for example, subject identifiers must be texts in the new system but were numeric in the old). This method is often a good choice when migrating data between clinical data management systems. Even if new data structure standards are being put in place in the new system, it is often not necessary (and may be undesirable) to change the structure of existing studies to meet the new standards. The most difficult kinds of migrations are those from two systems with fixed data structures, such as in serious adverse event (SAE) packages where most of the data structures are determined by the application, and there is little room for variability. When the data structures are determined by the applications, mapping of the fields from one system to the other usually uncovers the need for many transformations of the data. For example, numeric data in the legacy system may be text data in the new; date formats may be different, and codes for categorical fields will frequently differ. Unfortunately, these very complex migrations frequently turn out to be the ones where all the data must be migrated, and it all must be migrated before produc- tion use. Any system that has been in production for a few years and has significant amounts of data stored in it also has significant problems stored in it. There are mapping prob- lems such as those described in the previous text and there are also data problems. Data problems are those that arise because the values in a particular data structure (such as a field or column) are not internally consistent. Data problems, in particular, are made worse when the old system already has legacy data in it from some previ- ous system. An example of a data problem is the case of a field that was not coded in the past but now is. Old data will contain all kinds of values that may no longer be acceptable according to the new codes allowed. 232 Practical Guide to Clinical Data Management, Third Edition Mapping problems are really more like challenges in that they are systematic and logical and can usually be addressed or solved through programs run right before or during the migration process. Data problems are much harder to deal with because they deal with subsets of the data that cannot always be clearly specified. In some cases, data problems can be dealt with in the old system prior to migration (through deletion of test data or correction of truly erroneous data). Sometimes the problem data cannot be migrated. In this case, some migrations deal with the data by creating electronic files of records that could not be migrated. Hand entering those records is also an option.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Migration by Hand", "definition": "The more complex the migration and the more complex the data problems, the more complex the tools needed to migrate that data. Complex programs require time for development and validation by sophisticated and experienced programmers. When the job to migrate through tools and programs becomes too big or too expensive, companies should consider migrating by hand—that is, by reentering the data. Even with large volumes of data, experienced data entry staff may be able to reenter the data more efficiently than software tools can. Double entry in particular has a low error rate and even 100% verification can be reasonably cost efficient when carried out by administrative staff. This migration by hand should be an approach of last resort because information about the origin of the records (who originally entered them and when) is lost, as is any original audit trail. (For records submitted to the Food and Drug Administration [FDA], 21 CFR Part 11 requires that the audit trail be retained for at least as long as the electronic record itself.) Migrating Audit Trails For systems that have audit trails, the question arises as to whether the audit trail should be migrated along with the data. The FDA rule on electronic signatures and electronic records requires that the audit trail be accessible for the life of the data. However, this can prove difficult, if not impossible, because audit trails of some older systems are not in a structured form that would allow access and migration. Even accessible audit trail structures may contain less (or more) information than the new system requires. The migration team should review the audit trail structure and migrate it if the systems are compatible. If they are not, companies should consider putting the audit trail in some other archive format that is perhaps not as accessible as the data in the main application but could be reviewed if necessary during an audit of the data. This may mean putting it on the same platform or moving only key pieces of data. It is better, even, to move the audit trail to a different but accessible platform or applica- tion (even PDF files) than to lose the information entirely.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "What to Archive", "definition": "Just archiving the data is usually not enough; if that were the case, saving the SAS extract of the data would satisfy everyone. As we saw in the previous migration discussion, the audit trail should be retained for as long as the records are (see 21 CFR Part 11, Section, 11.10). So the audit trail, at a minimum, must be brought along into the archive. General industry consensus seems to be that as much information 234 Practical Guide to Clinical Data Management, Third Edition about the design and conduct of the study should be brought along as well. This would include database design, coding dictionaries, coding algorithms, derived and calculated values, and cleaning rules. For some systems, this information will be in a proprietary design and cannot be moved electronically. In this case, paper or PDF versions of the information should be obtained before the old system is retired. In other cases, this study information will be available electronically (in Oracle, for example) and can be archived along with or parallel to the data, if care is taken. MIGRATION AND ARCHIVE PLANS Like every other major undertaking we have looked at so far in this book, every migration or archive effort needs a plan. The plan should clearly document the approach being taken for all data and list all of the risks involved. In particular, the plan should specify all points at which verification that the data is a true copy will be provided and what methods will be used to provide that verification. For archiving data, the plan should explicitly list all the components of the original system that will be included in the archive and the formats being used for their storage.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "FUTURE DIRECTIONS", "definition": "The whole area of archiving is bound to mature over the next couple of years as companies try out various methods and improve upon standard format models. The FDA issued a guidance document in July of 2002 on the maintenance of electronic records. This guidance was withdrawn when the scope and application 21 CFR Part 11 was reevaluated. Presumably, the agency will issue new guidance on this topic eventually. In the meantime, every company will have to make its best effort, archiving as much as possible in as simple a format as possible, and documenting all decisions and approaches for future reference. 235 Appendix A: Data Management Plan Outline The outline for a data management plan shown in this appendix is just one example of the structure such a plan might take. The section headings that appear here come from the recommendations of the Society for Clinical Data Management (SCDM) combined with actual plans used by a wide variety of data management groups. The main headings identify the task to be described or the information to be provided. The subpoints provide a bit more detail on the kinds of information that might be included if appropriate. The subsections also list some of the documents that might be created or collected to fulfill requirements with the designation of Associated Documents. This basic outline can be easily adapted to studies carried out by a contract research organization (CRO) and studies that use electronic data capture (EDC) rather than paper case report forms (CRFs). As noted in Chapter 1, “The Data Management Plan,” the level of detail provided by a given group for a given study in such a data management plan could legitimately vary from little to lengthy. In particular, only a reference is needed when a task is fully described by standard operating procedures or guidelines. So, for example, if the process for reconciling serious adverse events (SAEs) is fully described in the company standard operating procedure (SOP) numbered DM-114, then the text under that bullet point would be something as simple as “As per DM-114. No study- specific instructions.”", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "DESCRIPTION", "definition": "1.\tProtocol title and study number 2.\tReference to protocol document RESPONSIBILITIES/SCOPE OF WORK 1.\tLead data manager 2.\tCRO contact information 3.\tOther relevant responsibilities (e.g., coding) CRF/ECRF DESIGN 1.\tWho is responsible for design 2.\tWho needs to sign off and when 3.\tHow revisions are made, approved, and filed Associated document(s): Approved design document 236 Appendix A: Data Management Plan Outline", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "CRF FLOW AND TRACKING", "definition": "1.\tFor paper studies: the CRF workflow 2.\tFor EDC studies: any paper elements being used (e.g., worksheets, backup SAE forms)", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Data Entry", "definition": "Perform Discrepancy Management and Query Resolution", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "loads", "definition": "LAB DATA ADMINISTRATION 1.\tNormal range handling 2.\tOther lab administration tasks", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "SAE RECONCILIATION", "definition": "1.\tProcess for reconciliation, including discrepancy handling 2.\tFrequency Associated document(s): Final approved SAE reconciliation records", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "CODING REPORTED TERMS", "definition": "1.\tAll dictionaries and specific versions being used for all items to be coded 2.\tAutocoding process, algorithms if relevant, software used 3.\tWorkflow for uncoded terms and review/approval requirements 4.\tCoding conventions specific to this protocol or project Associated document(s): Final approved coding", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "REPORTS", "definition": "1.\tList of standard reports (e.g., missing pages, outstanding discrepancies) 2.\tFrequency of reports TRANSFERS OR EXTRACTIONS 1.\tList of transfers expected or frequency of transfers, if any 2.\tProcess for transfers Associated document(s): Transfer specifications for outgoing transfers INTERIM ANALYSES/LOCKS 1.\tIf any, when in the course of the trial do they take place 2.\tProcess that will be followed at that time; signature requirements if any Associated document(s): Any documents required by the process", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "DATABASE CHANGES", "definition": "1.\tProcess that is followed for database changes Associated document(s): Database change log; any output from testing and", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "releasing updates", "definition": "238 Appendix A: Data Management Plan Outline", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Management SOPs", "definition": "The list of standard operating procedures (SOPs) in this appendix is derived from good clinical practices (GCP) and 21 CFR Part 11 requirements, Food and Drug Administration (FDA) guidance documents, the Society for Data Management’s “Good Clinical Data Management Practices” document, and a broad selection of SOPs from companies both large and small. The list is a superset of the topics from those references and sources and is meant to be a comprehensive list of topics to be covered by a standard procedure. Note that: •\t Not all of these SOPs are of the same priority. •\t Some of the topics could be combined into a single procedure document. •\t Some of the topics might be addressed by corporate SOPs or policies. Refer to the relevant chapters of this book for additional details on best practices and documentation. 240 Appendix B: Clinical Data Management SOPs TABLE B.1", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Both", "definition": "FDA guidance documents recommend SOPs on system backups and archiving. For larger companies, this is usually an IT or validation group SOP. 243 Appendix C: CRO-Sponsor", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Paper", "definition": "For paper-based studies there should be a database audit SOP, study-specific audit plan, or the DMP. Requirements may be included in the Study Lock SOP.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Comments", "definition": "Query Management: Issuing, Tracking, and", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "EDC", "definition": "For EDC studies, describes what kinds of queries and query responses must be reviewed by in-house staff and covers the responsibilities for review and closing queries. Receiving Electronic Data", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Continued", "definition": "242 Appendix B: Clinical Data Management SOPs TABLE B.1 (Continued )", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Responsibility Matrix", "definition": "Figure C.1 is an example of a responsibility matrix to be used in setting up a contract with a contract research organization (CRO) for a paper trial. Most of the key data management tasks are listed; some of them are broken down in detail. Note that this matrix should indicate not only who performs the work but also who is responsible for review (as shown in the first lines for the data management plan [DMP] and case report form [CRF] development.) It is also possible that the sponsor and CRO both do some of the work. The matrix should be customized for each study and sponsor– CRO combination as needed to clearly identify the work that is to be done. Figure C.2 is a similar matrix that might apply to an electronic data capture (EDC) study. Particular attention there should be paid to defining involvement during the testing phase of the study build. 244 Appendix C: CRO-Sponsor Responsibility Matrix", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "CRO", "definition": "Write and Maintain Data Management Plan", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Review", "definition": "Develop Help Text and Completion Guidelines Define Database Characteristics Specify Edit Checks (manual and automatic) Build the EDC Application (includes unit test)", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Build the Database", "definition": "•\t Write specifications •\t Design •\t Testing Program and Validate Edit Checks Image CRF Pages and Query Forms", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Track CRFs", "definition": "Generate Weekly Reports •\t Missing/expected pages •\t Discrepancy counts including time outstanding Perform Clinical Data Reviews Administer Central Lab Data •\t Define lab transfer agreement •\t Receive lab data •\t Manage lab normals •\t Check lab data (specify specific checks) •\t Contact lab to resolve data issues Receive and Manage Assay Data •\t Define transfer agreement •\t Receive data transfers •\t Check electronic data (specify specific checks) •\t Contact provider to resolve data issues Conduct Database QC (quality control) Audit •\t Frequency •\t Special milestones •\t Final audit Transfer Data to Sponsor Transfer Specification", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Code Reported Terms", "definition": "•\t AEs •\t Medications Perform SAE reconciliation", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Approve Study Lock", "definition": "FIGURE C.1  Data management responsibility: Paper-based study. Appendix C: CRO-Sponsor Responsibility Matrix 245", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Approve Online eCRF", "definition": "Create Validation/UAT (user acceptance testing) Plan Perform System Testing Perform Query Management •\t Review queries closed without data updates •\t Create manual queries as needed Generate Weekly Reports •\t Missing/expected pages •\t Discrepancy counts including time outstanding Perform Clinical Data Reviews Administer Central Lab Data •\t Define lab transfer agreement •\t Receive lab data •\t Manage lab normals •\t Check lab data (specify specific checks) •\t Contact lab to resolve data issues Receive and Manage Assay Data •\t Define transfer agreement •\t Receive data transfers •\t Check electronic data (specify specific checks) •\t Contact provider to resolve data issues Transfer Data to Sponsor Transfer Specification", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Create Site PDFs", "definition": "Create PDFs for Sponsor FIGURE C.2  Data Management Responsibilities: EDC study. 247 Appendix D: Implementation", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Plan Outline", "definition": "This example outline for a validation plan applies to a vendor-supplied software application. It includes elements common to most validation plans but the actual names, organization, and grouping of the various requirements would vary from company to company. This outline shows mostly high-level headings; the description of what might be included under each heading can be found in detail in Chapter 23. 1. Introduction and Scope 1.1", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Hardware", "definition": "1.2 Options 1.3 Project Plan/Timeline 2. Assumptions and Risk Assessment 3. Business Requirements 4. Functional Specification 5. Installation/Implementation 5.1 Installation Procedure 5.2 Installation/Implementation Notes 5.3 Installation Qualification 6. Testing Overview 7. Vendor Audit Summary 8. Security Plan 9. SOPs and Guidelines 10. Completion Criteria 11. Revision History 251 Appendix F: CDISC and HIPAA New data managers and others concerned about proper data management often ask about the CDISC and HIPAA and how they impact data collection. CDISC is a standards format and HIPAA is a privacy rule. This appendix provides a very brief explanation of each as well as links for more detailed information.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "CDISC", "definition": "CDISC stands for the Clinical Data Interchange Standards Consortium. The mission of this independent group is to develop and support global, platform-independent data standards that enable information system interoperability or interchange in medical research. Their goal is to develop standard models that still permit flex- ibility in research. The group has done most of its work in the study data tabulation model (SDTM), which is the standard for regulatory submission of case report form data tabulations from clinical trials. The Food and Drug Administration (FDA) already accepts data in SDTM and most companies are moving in this direction. There is a proposed rule being considered that will require data being submitted in electronic format conform to standardized data structures, terminology, and codes. Many sponsors also request that data sent to them from contract research organizations (CROs) or other vendors be consistent with this model. CDISC also has other standard models including an Operational Data Model (ODM) for the interchange and archive of data collected in clinical trials through a variety of sources. This would include not only the data but also the audit trail and metadata (structural and/or administrative data). The important point at this time is that the model is for writing and reading of files for interchange—it is not meant to replace standard clinical data management systems. However, electronic data cap- ture (EDC) systems could write ODM-compatible files and those files could be read into the clinical data management (CDM) system through an ODM reader utility. An initial version of a Clinical Data Acquisitions Standards Harmonization (CDASH) standard has also been released that connects to ODM. Data management groups have begun exploring these standards to see how they can facilitate building of stud- ies and transformation of the database to STDM for submission. More companies will begin to work with CDISC as more tools are available from vendors, but at this writing, it appears that it will be several years yet before the impact on data management will be clear. Data management groups would be wise to stay current with CDISC. The group has speakers that present sessions at many conferences and also has workshops on the standards. More information can be found at http://www.cdisc.org. 252 Appendix F: CDISC and HIPAA", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "HIPAA", "definition": "HIPAA is the Health Insurance Portability and Accountability Act of 1996. It is a federal law, and like 21 CFR (Code of Federal Regulations), it covers quite a range of topics. The sections that most apply to data management are those on privacy. (See 45 CFR Parts 160 and 164 for the actual rule.) The rules apply to covered entities, which are generally people (such as your private physician) or organizations (such as medical centers or research hospitals). Those entities may not transfer identifiable data to another organization without explicit permission from the patient for each transfer. Sponsors of clinical trials are able to receive datasets that identify each patient (albeit without an actual name) because the rule has provisions for research. Those provisions require the consent of the patient for the purpose of a single study. The informed consent document that patients in clinical trials sign, or a separate HIPAA waiver, serves this purpose. Data management groups that work with research institutions occasionally get calls from those sites asking that some data fields be removed from the CRF. Most often the field in question is the birth date. Some sites request that only the year be provided. Should the sponsor not be able to convince the institution that the data is permitted, the database design must handle partial birth dates. Patient initials are another kind of identifying field that may be called into question. Data managers can attend short introductions to HIPAA offered by a wide range of groups and private instructors. The National Institutes of Health (NIH) publishes use- ful material to explain the rule and its impact on clinical research. In particular, refer to its web publication “Clinical Research and the HIPAA Privacy Rule” and other useful materials found at http://privacyruleandresearch.nih.gov/clin_research.asp. 253", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Bibliography", "definition": "FDA, 21 CFR Part 11, Electronic Records; Electronic Signatures; Final Rule. Federal Register Vol. 62, No. 54, 13429, March 20, 1997. FDA, Guidance for Industry E6 Good Clinical Practice: Consolidated Guidance. FDA, Part 11, Electronic Records; Electronic Signatures—Scope and Application, 2003. FDA, General Principles of Software Validation; Guidance for Industry and FDA Staff, 2002. FDA, Guidance for Industry; Computerized Systems Used in Clinical Investigations, 2007. MedDRA MSSO 2011, MedDRA Term Selection: Points to Consider, Version 4.1. http://www. meddramsso.com/files_acrobat/ptc/9491-1400_TermSelPTC_R4_1_mar2011.pdf. Society for Clinical Data Management, Good Clinical Data Management Practices. http:// www.scdm.org/gcdmp/ (accessed July 2011). w w w . c r c p r e s s . c o m", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "Prac t ical Guide to", "definition": "CLINICAL DATA MANAGEMENT T h i r d E d i t i o n PHARMACEUTICAL TECHNOLOGY w w w. c rc p r e s s . c o m", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "an informa business", "definition": "6000 Broken Sound Parkway, NW Suite 300, Boca Raton, FL 33487 711 Third Avenue New York, NY 10017 2 Park Square, Milton Park Abingdon, Oxon OX14 4RN, UK The management of clinical data, from its collection during a trial to its extraction for analysis, has become a critical element in the steps to prepare a regulatory submission and to obtain approval to market a treatment. Groundbreaking on its initial publication nearly fourteen years ago, and evolving with the field in each iteration since then, the third edition of P actical Guide to Clinical Data Management includes important updates to all chapters to reflect the current industry approach to using electronic data capture (EDC) for most studies. See what’s new in the Third Edition: • A chapter on the clinical trial process that explains the high level flow of a clinical trial from creation of the protocol through the study lock and provides the context for the clinical data management activities that follow • Reorganized content reflects an industry trend that divides training and standard operating procedures for clinical data management into the categories of study startup, study conduct, and study closeout • Coverage of current industry and Food and Drug Administration (FDA) approaches and concerns The book provides a comprehensive overview of the tasks involved in clinical data management and the computer systems used to perform those tasks. It also details the context of regulations that guide how those systems are used and how those regulations are applied to their installation and maintenance. Keeping the coverage practical rather than academic, the author hones in on the most critical information that impacts clinical trial conduct, providing a full end-to-end overview or introduction for clinical data managers.", "sources": [ "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf" ], "file": "Susanne_Prokscha_Practical_Guide_to_Clinical_Data_Management_Third_Edition-CRC_Press_2011.pdf", "type": "pdf" }, { "term": "AAB", "definition": "American Association of BioAnalysts A national professional association whose members are clinical laboratory directors, owners, supervisors, managers, medical technologists, medical laboratory technicians, physician office laboratory technicians and phlebotomists. AAB is committed to the pursuit of excellence in clinical laboratory services by enhancing the professional skills of each of its members; promoting more efficient and productive operations; offering external quality control programs; collaborating with other professional associations and government agencies; promoting safe laboratory practices; and educating legislators, regulators, and the general public about clinical laboratory tests and procedures. http://www.aab.org/aab/default.asp", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "AABB", "definition": "(formerly American Association of Blood Banks), now just known as AABB International non-profit association representing individuals and institutions involved in the field of transfusion medicine and cellular therapies, specifically ones based on hematopoietic stem cells . Virtually all major blood banks in the United States are voluntarily accredited by the AABB with more than 80 percent of hospital transfusion services and similar facilities in the US being members. In 2005 the organization changed its name to AABB to reflect the changes in scope and operations. http://www.aabb.org/Pages/default.", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Chemistry", "definition": "An international society comprised of medical professionals with an interest in clinical chemistry, clinical laboratory science, and laboratory medicine. http://www.aacc.org/Pages/default.", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "AAFS", "definition": "American Academy of Forensic Sciences A multi-disciplinary professional organization that provides leadership to advance science and its application to the legal system with the objectives to promote professionalism, integrity, competency, education, foster research, improve practice, and encourage collaboration in the forensic sciences. http://www.aafs.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "ABP", "definition": "American Board of Pathology The American Board of Pathology is the certifying board for physicans seeking or maintaining a specialty certificate as a Pathologist. http://www.abpath.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Affordable Care Act", "definition": "The Patient Protection and Affordable Care Act (PPACA), also known as the federal health care law, is a 2010 US federal statute to decrease the number of uninsured Americans and reduce the overall cost of health care. https://www.healthcare.gov/law/feat ures/index.html", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "ACLA", "definition": "American Clinical Laboratory Association A not-for-profit organization created in 1971 which offers members the benefits of representation, education, information and research, in order to facilitate advocacy for laws and regulations recognizing the essential role that laboratory services play in delivering cost-effective health care; encourage the highest standards of quality, service and ethical conduct among its members; and promote public awareness about the value of laboratory services in preventing illness, diagnosing disease, and monitoring medical treatment. http://www.acla.com/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "ACLPS", "definition": "Academy of Clinical Laboratory Physicians and Scientists An organization dedicated to the advancement of teaching and scholarship in laboratory medicine. http://www.aclps.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "ACO", "definition": "Accountable Care Organization Coordinated care systems in which providers are incentivized on the basis of outcomes rather than the number of services. http://www.gpo.gov/fdsys/pkg/FR- 2011-11-02/pdf/2011-27461.pdf", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "ADASP", "definition": "Association of Directors of Anatomic and", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Surgical Pathology", "definition": "An organization comprised of directors of anatomic and/or surgical pathology from academic institutions, that promotes expertise and education of pathologists and other healthcare professionals in the field of anatomic pathology and related disciplines. http://www.adasp.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "AFIP", "definition": "Armed Forces Institute of Pathology Founded in 1862 as the Army Medical Museum in Washington, D.C., AFIPs primary purpose was to provide a second opinion diagnostic consultation on pathologic specimens such as biopsies from military, veteran, and civilian medical, dental, and veterinary sources. AFIP was closed in September, 2011. http://www.nlm.nih.gov/hmd/medto ur/afip.html", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "AJCC", "definition": "American Joint Committee on Cancer An organization that provides leadership in the development, promotion and maintenance of evidence-based systems for the classification and management of cancer. https://cancerstaging.org/Pages/def ault.aspx CAP Glossary of Clinical Informatics Terms 1 of 14", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Link", "definition": "CAP Glossary of Clinical Informatics Terms", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Association", "definition": "An international association of over 1,800 clinical laboratory professionals that provides support, resources and advocacy in the clinical laboratory industry to enhance the image and increase the visibility of the laboratory management profession. http://www.clma.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Immunologists", "definition": "A society of medical and scientific professionals, including veterinary and dental, enjoined to improve the practice and study of medical laboratory immunology. http://www.amli.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "AMP", "definition": "Association for Molecular Pathology An organization comprised of molecular pathologists, clinical laboratorians, clinical or basic research scientists, reagent/ instrument manufacturers, bioinformaticists, teachers, mentors, students and public servants, who promote the highest quality of molecular diagnostics to improve patient care. AMP is a community that fosters and advances excellence in innovation, translational research, education, training, and clinical practice. http://www.amp.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "ANSI", "definition": "American National Standards Institute A private non-profit organization that oversees the development of voluntary consensus standards for products, services, processes, systems, and personnel in the United States for consumer and environmental safety, while strenghtening the U.S. marketplace position globally by coordinating U.S. standards with international standards so that American products can be used worldwide. http://www.ansi.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "APHA", "definition": "American Public Health Association A public healthcare professional organization aimed at protecting all Americans and their communities from preventable, serious health threats and ensuring that preventative health services are universally accessible", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "APHL", "definition": "Association of Public Health Laboratories A U.S. membership organization comprised of public health laboratories for the promotion of policies that support healthy communities. http://www.aphl.org/Pages/default.a", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "API", "definition": "Association for Pathology Informatics A non-profit organization whose mission is to promote the field of pathology informatics as an academic and a clinical subspecialty of pathology by supporting advances in the field of Pathology Informatics through research, education, scientific meetings, and through electronic and printed communications. http://www.amp.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Reporting", "definition": "A profile within IHE AP that provides templates for building surgical pathology reports (cancers, benign neoplasms as well as non-neoplastic conditions). http://www.ihe.net/Technical_Fram eworks/#anatomic", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Act of 2009", "definition": "An economic stimulus package enacted by the 111th United States Congress in February 2009 and signed into law on February 17, 2009, by President Barack Obama. The ARRA created the HITECH Act for healthcare meaningful use. Also referred to referred to as the Stimulus or The Recovery Act http://en.wikipedia.org/wiki/America n_Recovery_and_Reinvestment_A ct_of_2009", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "ASC", "definition": "American Society of Cytopathology A national professional society of physicians, cytotechnologists and scientists dedicated to improving patient care in regards to the cytologic method of diagnostic pathology. http://www.cytopathology.org/ ASCLD/LAB PRC American Society of Crime Laboratory Directors/Laboratory Accreditation Board Proficiency Review Committee Proficiency Review Committees (PRC) are established for each forensic discipline accredited by ASCLD/LAB. The PRCs are tasked with reviewing the proficiency test results of accredited laboratories in the corresponding discipline. http://www.ascld-lab.org/proficiency- review-committees/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "ASCLS", "definition": "American Society for Clinical Laboratory", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Science", "definition": "Canada's national certifying body for medical laboratory technologists and medical laboratory assistants. http://www.csmls.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "ASCP", "definition": "American Society for Clinical Pathology ASCP is a professional membership organization for pathologists and laboratory professionals, whose mission is to provide excellence in education, certification and advocacy on behalf of patients, pathologists and laboratory professionals across the globe. http://www.ascp.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "and Immunogenetics", "definition": "An international non-profit association of professionals dedicated to advancing the science, education, and application of immunogenetics and transplant immunology. http://www.ashi-hla.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "ASIP Sante", "definition": "French Agency of Shared Healthcare", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Information Systems", "definition": "An agency of the French government and one of three sponsors of IHE Lab, whose mission is to strengthen public ownership of the information systems being developed in the healthcare. http://esante.gouv.fr/en 2 of 14", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "ATA", "definition": "American Telemedicine Association A non-profit organization whose goal is to promote access to medical care for consumers and health professionals via telecommunications technology. http://www.americantelemed.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Bioinformatics", "definition": "An interdisciplinary scientific field combining computer science, statistics, mathematics and engineering, for the purpose of developing methods and software tools that are used for storing, retrieving, organizing and analyzing biological data, especially as applied in molecular genetics and genomics. http://en.wikipedia.org/wiki/Bioinfor", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "CAH", "definition": "Critical Access Hospital Small, rural hospitals that are structured differently than acute care hospitals. Some of the requirements for CAH certification include having no more than 25 inpatient beds; maintaining an annual average length of stay of no more than 96 hours for acute inpatient care; offering 24-hour, 7-day-a-week emergency care; and being located in a rural area, at least 35 miles drive away from any other hospital or CAH (fewer in some circumstances). http://www.cms.gov/Outreach-and- Education/Medicare-Learning-", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "CCD", "definition": "Continuity of Care Document XML based document standard developed by the HL7 organization and specifies the encoding, structure, and semantics of a patient summary clinical document for exchange. 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Its main goal is to protect America from health, safety and security threats, both foreign and in the U.S., through the control and prevention of disease, injury, and disability. http://www.cdc.gov/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Consortium", "definition": "A multidisciplinary, neutral, non-profit standards developing organization that develops and supports global, platform-independent data standards that enable information system interoperability to improve medical research and related areas of healthcare. http://www.cdisc.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Technology", "definition": "Founded in 1901 and prior to 1989 was known as the National Bureau of Standards (NBS), NIST is one of the nation's oldest physical science laboratories, providing measurement standards in the U.S. NIST measurements support the smallest of technologies to the largest and most complex of human-made creations. http://www.nist.gov/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "CFR", "definition": "Code of Federal Regulations The codification of the general and permanent rules published in the Federal Register by the departments and agencies of the Federal Government. It is divided into 50 titles that represent broad areas subject to Federal regulation. http://www.gpo.gov/fdsys/browse/c ollectionCfr.action?collectionCode=", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Product List", "definition": "The authoritative, comprehensive listing of certified Complete Electronic Health Records (EHRs) and EHR modules, published by the ONC. http://www.healthit.gov/policy- researchers-implementers/certified- health-it-product-list-chpl", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "CISC", "definition": "Clinical Informatics Steering Committee CAP committee that reports to the Council on Scientific Affairs (CSA) and presides over the Informatics Committee and PERT committee. http://capnet/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "CISH", "definition": "Chromogenic in situ hybridization A practical, cost-effective, and valid alternative to fluorescent in situ hybridization (FISH) testing for gene alteration, in that it does not need expensive fluorescence microscopy instrumentation / expertise, and the chromogenic agents used in most CISH methods are chemically stable and do not fade over time, allowing easy storage and repeated re-examination of samples. http://www.cytotest.com/cish.asp", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "CLC", "definition": "Clinical Laboratory Coalition An organization committed to ensuring access to high quality laboratory services. http://www.aab.org/NewsBot.asp?M ODE=VIEW&ID=209 3 of 14", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Amendments", "definition": "Federal regulatory standards that apply to all clinical laboratory testing performed on humans in the United States, except clinical trials and basic research. Clinical laboratories must be certificated by their state as well as the Center for Medicare and Medicaid Services (CMS) before they can accept human samples for diagnostic testing. Laboratories can obtain multiple types of CLIA certificates, based on the kinds of diagnostic tests they conduct. http://wwwn.cdc.gov/CLIA/Default.a", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Advisory Committee", "definition": "A diverse committee managed by the Centers for Disease Control and Prevention (CDC), that provides scientific and technical advice and guidance to the Department of Health and Human Services (HHS), pertaining to general issues related to improvement in clinical laboratory quality and laboratory medicine practice, as well as possible revision of the CLIA standards. http://wwwn.cdc.gov/CLIAC/default.", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Clinical Informatics", "definition": "The application of information management in healthcare to promote safe, efficient, effective, personalized, and responsive care. 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Additionally, CMS administers standards from the Health Insurance Portability and Accountability Act of 1996 (HIPAA), ensures quality in long-term care facilities by survey and certification processes, oversees clinical laboratory quality standards under CLIA, and maintains HealthCare.gov. http://cms.hhs.gov/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "COLA", "definition": "Commission on Laboratory Accreditation Formerly known as the Commission on Office Laboratory Accreditation, COLA is a clinical laboratory education, consultation, and accreditation organization. http://www.cola.org/ 4 of 14", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Change Proposal", "definition": "Method employed by IHE by which stable, published technical documents can be proposed to be modified by knoweldgeable suggestion by users, vendors or Technical Committee members. These proposals are reviewed by the Technical Committee on a regular bases and then either approved or rejected. http://wiki.ihe.net/index.php?title=C ategory:CPs", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "CPOE", "definition": "Computerized Physician Order Entry (Computerized Provider Order Entry) The electronic entry of medical practitioner instructions for the treatment of patients which are communicated over a computer network to the medical staff or to the departments (pharmacy, laboratory, or radiology) responsible for fulfilling the order. CPOE has many benefits including reducing delays in order completion, reduceing transcription errors, and provideing error-checking for duplicate or incorrect doses or tests. http://searchhealthit.techtarget.com /definition/computerized-physician-", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "CQM", "definition": "Clinical Quality Measure Tools that utilize date in order to measure and track the quality of health care services provided by eligible professionals, eligible hospitals and critical access hospitals, such as treatment, experience, and patient outcomes. CQMs are required as part of meaningful use requirements for the Medicare and Medicaid Electronic Health Record (EHR) Incentive Programs. http://www.cms.gov/Regulations-", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "CRC", "definition": "Chemistry Resource Committee A CAP committee working to improve patient care by promoting the overall quality of laboratory results, evaluating emerging trends, and advising the Laboratory Accreditation Program on issues related to clinical chemistry. http://www.cap.org/apps//cap.portal ?_nfpb=true&cntvwrPtlt_actionOver ride=%2Fportlets%2FcontentViewe r%2Fshow&_windowLabel=cntvwrP tlt&cntvwrPtlt%7BactionForm.conte ntReference%7D=committees%2F chemistry_description.html&_state= maximized&_pageLabel=cntvwr", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "CSMLS", "definition": "Canadian Society for Medical Laboratory", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "DICOM", "definition": "Digital Imaging and Communications in", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Medicine", "definition": "A special interest group of IHTSDO focused on Pathology and Laboratory Medicine as it relates to SNOMED CT. http://www.ihtsdo.org/search/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "DIHIT", "definition": "Diagnostic Intelligence and Health Information Technology See Informatics Committee (ICE) www.cap.org", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Direct Project", "definition": "The Direct Project specifies a simple, secure, scalable, and standards-based way for participants to send authenticated, encrypted health information directly to known, trusted recipients over the Internet. http://wiki.directproject.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "DPA", "definition": "Digital Pathology Association A not-for-profit organization comprised of pathologists, scientists, technologists and industry representatives that are focused on facilitating education and awareness of digital pathology applications in healthcare and life sciences. https://digitalpathologyassociation.o rg/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "DPS", "definition": "Digital Pathology System An image-based computer system that enables the acquisition, management and interpretation of pathology information generated from a digitized glass slide in order to achieve better, faster, and less costly diagnosis, prognosis, and prediction of diseases. https://digitalpathologyassociation.o rg/_data/files/Archival_and_Retriev al_in_Digital_Pathology_Systems.p", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "DRG", "definition": "Diagnosis Related Group A system to classify hospital cases into one of 467 groups, with the last group being \"Ungroupable\", with the intent to identify the \"products\" that a hospital provides. DRGs, used in the US since 1982, determine how much Medicare reimburses the hospital for each \"product\", and are also standard practice for establishing payment for other Medicare related reimbursements. http://library.ahima.org/xpedio/grou ps/public/documents/ahima/bok1_0 47260.hcsp?dDocName=bok1_047 260", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "eCC", "definition": "Electronic Cancer Checklist An electronic version of the more than 70 CAP Cancer Checklists in XML format, that aid pathologists in management of information and in promotion of interoperability, as it offers a standardized way to report cancer data electronically. The CAP Cancer Committee develops these guidelines in collaboration with cancer experts, such as oncologists, radiologists, surgeons, cancer registrars, and other health care professionals. http://www.cap.org/apps//cap.portal ?_nfpb=true&cntvwrPtlt_actionOver ride=%2Fportlets%2FcontentViewe r%2Fshow&_windowLabel=cntvwrP tlt&cntvwrPtlt%7BactionForm.conte ntReference%7D=reference%2Fec c.html&_state=maximized&_pageL abel=cntvwr", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "eDOS", "definition": "Electronic Directory of Service An initiative of ONC Standards and Interoperability (S&I) Framework Laboratory Initiatives Pilots that aims to provide an electronic interchange of a laboratory’s directory of services in an structured format based on HL7 2.5.1. http://wiki.siframework.org/LOI+- +eDOS", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Eligible Hospital", "definition": "Acute Care Hospitals (including Cancer and Critical Access Hospitals) where the Average Length of Stay (ALOS) is 25 days or less. Under the Medicaid EHR Incentive Programs, eligible hospitals can qualify for incentive payments if they adopt, implement, upgrade or demonstrate meaningful use of certified EHR technology during the first participation year or successfully demonstrate meaningful use of certified EHR technology in subsequent participation years. http://www.cms.gov/Regulations-", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "EHR", "definition": "Electronic Health Record The aggregate electronic record of health-related information on an individual that is created, gathered and shared cumulatively across multiple facilities or healthcare organizations and is managed and consulted by licensed clinicians and staff involved in the individual's health and care. Sometimes used interchangeably with EMR (Electronic Medical Record). http://www.healthit.gov/providers- professionals/learn-ehr-basics", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "ELR", "definition": "Electronic Laboratory Reporting The automated transmission of laboratory-related data from commercial, public health, hospital, and other labs to state and local public health departments through an electronic health records (EHR) system or a Laboratory Information System (LIS). ELR is a requirement of Meaningful Use Stage 2. http://wwwn.cdc.gov/nndss/script/m u_elr.aspx", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "EMR", "definition": "Electronic Medical Record A digital version of the traditional paper-based medical record for an individual. The EMR represents a medical record within a single facility, such as a doctor's office, clinic, or hospital, and it is the source of data for the EHR. Sometimes used interchangeably with EHR (Electronic Health Record). http://www.himssanalytics.org/docs/ WP_EMR_EHR.pdf", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "EP", "definition": "Eligible Professional (also known as Eligible Provider) An eligible professional is a healthcare provider who has demonstrated their understanding of electronic medical records (EMRs) by implementing criteria based on EMR patient updates and Meaningful Use laws. Eligible professionals include Nurses, Physician assistants, Physicians, and Social workers. http://www.healthit.gov/policy-", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "ePUB", "definition": "Electronic Publication A free and open ebook standard by the International Digital Publishing Forum. http://idpf.org/epub", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "FHIR", "definition": "Fast Healthcare Interoperability Resources (HL7 group) (pronounced 'FIRE') A next generation standards framework created by HL7 that is suitable for use in a wide variety of contexts. http://www.hl7.org/implement/stand ards/fhir/summary.html", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "FISH", "definition": "Fluorescence in situ hybridization Test method that provides researchers with a way to visualize and map the genetic material in an individual's cells, including specifc genes or portions of genes. Because a FISH test can detect genetic abnormalities associated with cancer, it's useful for diagnosing some types of the disease. In some cases when the type of cancer has previously been diagnosed, a FISH test also can provide additional information to help predict a patient's outcome and whether he or she is likely to respond to chemotherapy drugs. http://www.genome.gov/10000206 6 of 14", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "FNA", "definition": "Fine Needle Aspiration A diagnostic, minor surgical procedure used to investigate superficial lumps or masses, utilizing a thin, hollow needle inserted into the mass for sampling of cells that, after being stained, are examined microscopically. http://www.ncbi.nlm.nih.gov/pmc/art icles/PMC498011/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "FTP", "definition": "File transfer protocol A standard network protocol used to transfer files from one host to another host over a Transmission Controlled Protocol (TCP)-based network, such as the Internet. FTPs promote sharing of files (computer programs and/or data) and transfer data reliably and efficiently. http://tools.ietf.org/html/rfc959", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Information Set", "definition": "A widely used tool consisting of 81 measures across 5 domains of care in the managed care industry for measuring performance of care and service, developed and maintained by the National Committee for Quality Assurance (NCQA). http://www.ncqa.org/HEDISQuality Measurement/WhatisHEDIS.aspx", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "HHS", "definition": "Health and Human Services (Department of) The U.S. governments main agency for protecting the health and providing essential human services of all Americans. http://www.hhs.gov/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "HIE", "definition": "Health Information Exchange Created when health care information is electronically collected across organizations within a region, community or health system. Grants to support some of these exchanges were legislated into the HITECH part of ARRA. Sometimes called Regional Health Information Organizations (RHIOs). http://healthit.hhs.gov/portal/server. pt?open=512&objID=1488&parentn ame=CommunityPage&parentid=5 8&mode=2&in_hi_userid=11113&c ached=true", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Systems Society", "definition": "A not-for-profit global organization, founded in 1961, dedicated to imporving healthcare through the optimal use of information technology and management systems. http://www.himss.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Accountability Act", "definition": "A U.S. federal act aimed to improve portability and continuity of health insurance coverage, to combat waste, fraud, and abuse in health insurance and health care delivery, to promote the use of medical savings accounts, to improve access and coverage to long-term care services, to simplify the administration of health insurance, and to ensure an individuals right to medical privacy. http://www.hhs.gov/ocr/privacy/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "HIT", "definition": "Health Information Technology The comprehensive management and exchange of health information across computerized systems. http://en.wikipedia.org/wiki/Health_i nformation_technology", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "HITECH", "definition": "Health Information Technology for Economic and Clinical Health Act Enacted as part of the American Recovery and Reinvestment Act of 2009, signed into law on February 17, 2009, promotes the adoption and meaningful use of health information technology. http://www.hhs.gov/ocr/privacy/hipa a/administrative/enforcementrule/hit echenforcementifr.html", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "HITPC", "definition": "Health Information Technology (HIT)Policy Committee The American Recovery and Reinvestment Act of 2009 (ARRA) provideed that the HIT Policy Committee be created under the Federal Advisory Committee Act (FACA) and charged with making recommendations to the National Coordinator for Health IT on a policy framework for the development and adoption of a nationwide health information infrastructure, including standards, implementation specifications, and certification criteria, for the exchange of patient medical information http://www.healthit.gov/policy- researchers-implementers/health-it-", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Standards Committee", "definition": "A federal committee created by the American Recovery and Reinvestment Act of 2009 (ARRA) that advises the Office of the National Coordinator for Health IT (ONC) on matters of standards, certification criteria and other issues surrounding electronic health records (EHRs) and meaningful use. This committee focuses mainly on the policies developed by the health information technology (HIT) policy committee, and also provides for testing of the same by the National Institute for Standards and Technology (NIST). http://www.healthit.gov/facas/health- it-standards-committee", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Standards Panel", "definition": "An organization comprised of healthcare experts from both public and private sectors, created to promote standardization and broad scale interoperability among healthcare applications and information systems. HITSP sponsors numerous specifications that define standards for interoperability and information sharing. http://www.hitsp.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "HL7", "definition": "Health Level Seven, Inc. An international standards organization that develops and publishes voluntary consensus technical standards for interoperability of health information technology. http://www.hl7.org/ 7 of 14", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "ICE", "definition": "Informatics Committee (formerly DIHIT) A CAP committee whose mission is to establish the role of pathologists on the health care team as recognized stewards of clinical and diagnostic data integration and utilization, and reports to the Clinical Informatics Steering Committee (CISC). www.cap.org", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "IDR", "definition": "Integrated Disease Reporting A CAP DIHIT project formed with the intent to develop use cases for next generation reports and the benefits for primary care and mid-level providers. The project will also define \"structured data\" and \"what is a report\" as well as the pathologist role and liability when changing the structure and user interface to meet customer expectations. The output of the efforts will be a White Paper with detailed definitions and requirements for next generation reporting that support interoperability, Meaningful Use, and reuse of data. http://www.pathologyinformatics.org /content/api-webinar-integrated- disease-reporting-order-almost-", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "IHE", "definition": "Integrating the Healthcare Enterprise An initiative by healthcare professionals and industry to improve the way computer systems in healthcare share information to achieve optimal patient care. There are 12 domains within IHE, including laboratory and anatomic pathology. http://www.ihe.net/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Anatomic Pathology", "definition": "IHE Anatomic Pathology Domain addresses information sharing, workflow and patient care in anatomic pathology, including surgical pathology, cytopathology, autopsy, electron microscopy, and molecular pathology. http://wiki.ihe.net/index.php?title=A natomic_Pathology", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Laboratory", "definition": "IHE Laboratory Domain addresses information sharing and workflow related to in vitro diagnostic testing in both clinical laboratories as well as point of care testing. The IHE Laboratory Domain, established in 2003, manages the Laboratory Profiles and the Laboratory Technical Framework. http://wiki.ihe.net/index.php?title=La", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "boratory", "definition": "IHTSDO® International Health Terminology Standards Development Organisation® An international non-profit standards development organisation whose mission is to develop, maintain, promote and deliver medical terminology products in order to improve health in a global scale through the development and application of appropriately standardized clinical terminologies. IHTSDO owns, maintains, and administers the rights to SNOMED CT and related terminology standards. http://www.ihtsdo.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "IICC", "definition": "IVD Industry Connectivity Consortium A global nonprofit organization dedicated to creating and encouraging adoption of a unified connectivity standard to reduce the cost and variability of data exchange between IVD devices and healthcare informatics with the goal to improve healthcare efficiency and patient care. http://ivdconnectivity.org/cms/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "ILW", "definition": "Inter Laboratory Workflow An IHE profile that covers the workflow of orders and results beween a requesting lab (providing the specimens and the orders) and a subcontracting lab (performing the tests and reporting the results to the former one). This workflow is able to carry the payors information along with the order. http://wiki.ihe.net/index.php?title=Int er_Laboratory_Workflow", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Informatics", "definition": "The study and application of information management. http://en.wikipedia.org/wiki/Informati", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Interface engines", "definition": "An HL7 interface engine is an interface or integration engine built specifically for the healthcare industry. It connects legacy systems by using a standard messaging protocol. http://www.hl7.com/interface- engine.html", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Standardization", "definition": "An international standard-setting body composed of representatives from various national standards organizations that promotes worldwide proprietary, industrial and commercial standards. http://www.iso.org/iso/home.html", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "In Vitro Diagnostics", "definition": "Tests that can detect diseases, conditions, or infections and may be used in a laboratory or other health professional settings or by consumers' use at home. http://www.edma- ivd.be/index.php?page=About-In-", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "JAHIS", "definition": "Japanese Association of Healthcare Information Systems Industry One of three sponsors of IHE Lab, JAHIS is a Japanese organization that strives to improve healthcare information systems, ensuring quality and safety, and promoting standardization in order to contribute to the sound development of the healthcare information systems industry, as well as the improvement of public health, medical and welfare services. http://www.jahis.jp/english/greeting/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "LAW", "definition": "Laboratory Analytical Workflow Profile An IHE Lab profile that addresses the exchange of information related to both patient and QC test orders and results between IVD testing systems and health informatics systems to improve interoperability. http://wiki.ihe.net/index.php?title=La boratory_Analytical_Workflow_Profi", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "LBL", "definition": "Laboratory Barcode Labeling An IHE Lab profile that supports the robotization of specimen container identification and delivery at blood sample collection time, in the context of laboratory test requests. http://wiki.ihe.net/index.php?title=La boratory_Barcode_Labeling", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "LCC", "definition": "Laboratory Clinical Communications An IHE Lab profile that will enable rapid, standardized, and automated capture of electronic communication between the clinician and the laboratory related to orders and questions regarding results. This could be utilized when the laboratory wishes to propose a more appropriate test order to the clinician or the clinician wishes to obtain a pathologist's interpretation of patient results. This information will then be logged, tracked, and included in QA studies and process improvement projects. http://wiki.ihe.net/index.php?title=L CC_Long_Proposal_-_wiki", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "LCSD", "definition": "Laboratory Code Sets Distribution An IHE Lab profile that electronically establishes and maintains a common vocabulary between systems involved in laboratories workflows. http://wiki.ihe.net/index.php?title=La boratory_Code_Sets_Distribution", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "LDA", "definition": "Laboratory Device Automation IHE Lab profile that covers the exchanges between an Automation Manager (Actor played by a Laboratory Information System or by a Laboratory Automation System) and a set of automated Laboratory Devices to process a Work Order, perform the tests on the related specimens and retrieve their results. This processing includes the pre-analytical process of the specimen (sorting, centrifugation, aliquoting, transportation, decapping) the analytical process itself (run of the ordered tests on the specimen) and the post-analytical process (recapping, transportation, rerun, storage and retrieval). This profile is restricted to operations performed in a clinical laboratory setting and does not include point of care testing. http://wiki.ihe.net/index.php?title=La boratory_Device_Automation", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "LDT", "definition": "Laboratory Developed Tests An vitro diagnostic test that is manufactured by and used within a single laboratory (i.e. a laboratory with a single CLIA certificate). LDTs are also sometimes called in-house developed tests, or “home brew” tests. LDTs are considered “devices,” as defined by the FFDCA, and are therefore subject to regulatory oversight by FDA. When a laboratory develops an LDT in-house without receiving FDA clearance or approval, CLIA prohibits the release of any test results prior to the laboratory establishing certain performance characteristics relating to analytical validity for the use of that test system in the laboratory’s own environment. https://www.cms.gov/Regulations-", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "LOI", "definition": "Laboratory Orders Interface An S&I Framework iniative that is focused on the development of a Laboratory Orders IG for ambulatory settings that aligns with the LRI IG and ELINCS Laboratory Orders IG, will incorporate support for the adoption and use of the Test Compendium Framework, i.e., eDOS, and develop validation tools to facilitate the evaluation and adoption of the LOI IG. http://wiki.siframework.org/Laborato ry+Orders+Interface+Charter+Page", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Implementation Guide", "definition": "U.S. based Laboratory Results Interface Implementation Guide developed by the S&I Framework LRI Iniative, that provides guidance for ambulatory laboratory to ambulatory provider interfaces for laboratory results. http://www.hl7.org/implement/stand ards/product_brief.cfm?product_id= 279", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "and Codes", "definition": "A database hosted by Regenstrief Institute that provides a universal code system for reporting laboratory and other clinical observations which is available in multiple languages. In addition to laboratory tests, LOINC also includes clinical measures, imaging tests, and document architecture. http://www.regenstrief.org/loinc/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "LRI", "definition": "Laboratory Results Interface An S&I Framework iniative whose ultimate goal is to develop an Implementation Guide that will define the core data elements required for ambulatory care clinical laboratory test results in the U.S. Realm. The clinical laboratory test results will use standardized structured data so that they can be incorporated into a certified EHR. http://wiki.siframework.org/Laborato ry+Results+Interface+Quickstart+P", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "LTW", "definition": "Laboratory Testing Workflow An IHE Lab profile that integrates the ordering, scheduling, processing, and result reporting activities associated with in vitro diagnostic tests performed by clinical laboratories in healthcare institutions, and supports all laboratory specialties with the exception of anatomic pathology. http://wiki.ihe.net/index.php?title=La boratory_Testing_Workflow", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Middleware", "definition": "Software that mediates between an application program and a network. It manages the interaction between disparate applications across the heterogeneous computing platforms. http://foldoc.org/middleware", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Meaningful Use", "definition": "The American Recovery and Reinvestment Act of 2009 specifies three main components of Meaningful Use: 1. The use of a certified EHR in a meaningful manner, such as e-prescribing. 2. The use of certified EHR technology for electronic exchange of health information to improve quality of health care. 3. The use of certified EHR technology to submit clinical quality and other measures. Simply put, “meaningful use” means providers need to show they're using certified EHR technology in ways that can be measured significantly in quality and in quantity. http://www.cms.gov/Regulations-", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "NAACLS", "definition": "National Accrediting Agency for Clinical", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Laboratory Sciences", "definition": "An agency that accredits and approves education programs in the clinical laboratory sciences and related health care professions. NAACLS provides services including program accreditation, program approval, consultation, and continuing education, for educational programs, students, employers, and health care consumers. http://www.naacls.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Biochemistry", "definition": "NACB is the Academy of the AACC comprised of doctoral level clinical scientists and whose mission is to further the practice of clinical biochemistry for the benefit of all. http://www.aacc.org/members/nacb /pages/default.aspx#", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "NCCLS", "definition": "National Committee for Clinical Laboratory Standards (Now CLSI) A volunteer-driven, membership-supported, not-for-profit, standards development organizationthat promotes the development and use of voluntary laboratory consensus standards and guidelines within the health care community. (see CLSI) http://clsi.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "NCI", "definition": "National Cancer Institute Part of the National Institutes of Health (NIH), which coordinates the U.S. National Cancer Program and conducts and supports research, training, health information dissemination, and other activities related to the causes, prevention, diagnosis, and treatment of cancer; the supportive care of cancer patients and families; and cancer survivorship. http://www.cancer.gov/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Assurance", "definition": "A private, not-for-profit organization founded in 1990, dedicated to improving health care quality, through the administration and utilization of evidence-based standards, measures, programs, and accreditation. http://www.ncqa.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "NCRA", "definition": "National Cancer Registrars Association Established in 1974, the National Cancer Registrars Association is a non-profit organization that represents more than 5,000 cancer registry professionals and Certified Tumor Registrars (CTR®). Its mission is to serve as an education, credentialing, and advocacy resource for cancer data professionals. Cancer Registrars capture a complete summary of the history, diagnosis, treatment & disease status for every cancer patient, which leads to better information used in the management of cancer, and ultimately, cures. http://www.ncra- usa.org/i4a/pages/index.cfm?pagei d=1", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "NIH", "definition": "National Institutes of Health The NIH, part of the U.S. Department of Health and Human Services, is the nation’s medical research agency—making important discoveries that improve health and save lives. It is the largest source of funding for medical research in the world. The NIH both conducts its own scientific research through its Intramural Research Program (IRP) and provides major biomedical research funding to non-NIH research facilities through its Extramural Research Program. http://www.nih.gov/ 10 of 14", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "NIST", "definition": "National Institute of Standards and", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "NLM", "definition": "National Library of Medicine The NLM is operated by the U.S. federal government, and is the world's largest medical library, with over 7 million scientific and medical works. The NLM freely distributes SNOMED CT within the U.S., and also hosts PubMed, which contains over 23 million citations for biomedical literature. http://www.nlm.nih.gov/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "ONC", "definition": "Office of the National Coordinator for Health Information Technology (under HHS) A position within the US Department of Health & Human Services (HHS) created by Executive Order in 2004 and written into legislation by the HITECH Act. Its purpose is to promote a national health Information Technology infrastructure and oversee its development. http://healthit.hhs.gov/portal/server. pt?open=512&objID=1200&mode= 2 O&O Orders and Observations An HL7 special committee whose intent is to define information exchange capabilities to support the order/scheduling and clinical event management/reporting requirements between the stakeholders in the healthcare organization regarding patients, non-patients, other species, or inanimate objects. These information exchanges may cross organizational boundaries, and may involve messages, documents, services, and other HL7 constructs. O&O supports the ongoing development of HL7 Version 2.x and ensures that equivalent functionality is present in HL7 Version 3.0 and FHIR. http://www.hl7.org/Special/committ ees/orders/index.cfm", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "PathLex", "definition": "Anatomic Pathology Lexicon The PathLex project is an interface terminology launched by IHE and HL7 Anatomic Pathology, with the collaboration of other Anatomic Pathology organizations. The Pathlex project has been “designed to satisfy the needs of Anatomic Pathology information system vendors and users by adopting the best features of existing terminology systems, while producing new terms to fill critical gaps\". PathLex unifies and supplements other terminology systems, such as SNOMED-CT, CIM-O or various vocablary tables defined by DICOM and HL7. http://www.google.com/url?url=http: //www.hl7.org/documentcenter/publi c/wg/anatomicpath/20100831_Path Lex.docx&rct=j&frm=1&q=&esrc=s &sa=U&ei=R7y1U_z2AYidyASX2Y CABA&ved=0CCEQFjAC&usg=AF QjCNFRKyevb740dn5uW98hXpraL", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "PDF", "definition": "Portable Document Format A file format used to represent documents in a manner independent of application software, hardware, and operating systems. http://en.wikipedia.org/wiki/Portable _Document_Format", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Committee", "definition": "The Pathology Electronic Reporting Committee of the CAP was formed in 2007 to help guide the development and maintenance of the electronic Cancer Checklists (eCC). PERT serves to both oversee eCC informatics issue reconciliation as well as to interact with the CAP Cancer Committee regarding eCC content issues and paper/ electronic checklist release coordination, and reports to the Clinical Informatics Steering Committee (CISC). www.cap.org", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "PHI", "definition": "Protected Health Information Any information about health status, provision of health care, or payment for health care that can be linked to a specific individual. This may include any part of a patient's medical record or payment history. http://www.hipaa.com/2009/09/hipa a-protected-health-information-what- does-phi-include/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "PHIN VADS", "definition": "Public Health Information Network Vocabulary Access and Distribution", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "System", "definition": "A web-based enterprise vocabulary system for accessing, searching, and distributing vocabularies used", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "PHR", "definition": "Personal Health Record A health-related documentation maintained by the individual. http://www.ncbi.nlm.nih.gov/pmc/art icles/PMC1447551/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "PHSA", "definition": "Public Health Service Act A U.S. federal law enacted in 1944 to consolidate and revise the laws relating to the Public Health Service. http://www.fda.gov/regulatoryinform ation/legislation/ucm148717.htm", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "PPO", "definition": "Preferred Provider Organization A type of health plan that contracts with medical providers, such as hospitals and doctors, to create a network of participating providers. The insured pay a discounted rate when using providers that belong to the plan’s network and a higher rate for providers outside the network. http://www.ehealthinsurance.com/h ealth-plans/ppo/ 11 of 14", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "RCMT", "definition": "Reportable Condition Mapping Table Mapping tool available through the CDC to provide mappings between reportable conditions and their associated LOINC laboratory tests and SNOMED CT results https://phinvads.cdc.gov/vads/Sear chVocab.action Regenstrief Institute Regenstrief Institute An internationally respected non-profit medical research organization, which developed and now maintains", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "RELMA", "definition": "Regenstrief LOINC Mapping Assistant A mapping program to assist the mapping of local test codes to LOINC codes and to facilitate browsing of the LOINC results. http://www.regenstrief.org/loinc/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Research Use Only", "definition": "RUOs are products that have not cleared by the FDA for diagnostic use and should only be used in the research environment. According to the FDA, RUO products are described as products “in the laboratory research phase of development and not represented as an effective in vitro diagnostic product.” http://www.fda.gov/medicaldevices/ deviceregulationandguidance/guida ncedocuments/ucm253307.htm", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "SAFER Guides", "definition": "Safety Assurance Factors for EHR", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Resilience Guides", "definition": "Nine self-assessment guides for healthcare organizations to use to optimize the safety and safe use of EHR systems. http://www.healthit.gov/safer/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "SAFMLS", "definition": "Society of Armed Forces Medical", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Laboratory Scientists", "definition": "A non-profit organization, officially established in 1971, dedicated exclusively for charitable, educational and scientific purposes in relationship to the laboratory sciences. The primary objective is to maintain and enhanc high professional standards through improved laboratory policies and technology in support of the health care delivery systems of the Armed Forces, Public Health Services and Veterans Administration. http://www.safmls.org/ S & I Framework Standards and Interoperability", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Framework", "definition": "The S&I Framework, adopted by ONC's Office of Standards & Interoperability, is a collaborative community of participants from the public and private sectors who are focused on providing the tools, services and guidance to facilitate the functional exchange of health information. The S&I Framework has many different areas of involvement, such as aLOINC Order Code Initiative, Laboratory Results Interface Initiative (LRI), Structured Data Capture (SDC), and many others. http://www.siframework.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "SGR", "definition": "Sustainable Growth Rate A formula currently used by the Centers for Medicare and Medicaid Services to control spending by Medicare on physician services. Generally, this is a method to ensure that the yearly increase in the expense per Medicare beneficiary does not exceed the growth in GDP (gross domestic product). The formula limits growth in spending for physicians’ services by linking updates to target rates of spending growth. https://www.cms.gov/Medicare/Med icare-Fee-for-Service- Payment/SustainableGRatesConFa ct/Downloads/sgr2015p.pdf", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "SIG", "definition": "Special Interest Group A community within a larger organization that has a shared interest in advancing a specific area of knowledge, learning or technology. SIG members cooperate to affect or to produce solutions within their particular field. http://en.wikipedia.org/wiki/Special_ Interest_Group", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "SNOMED", "definition": "Systematized Nomenclature of Medicine older version of SNOMED CT. a systematic, computer-processable collection of medical terms, both human and veterinary, to provide codes, terms, synonyms and definitions which cover anatomy, diseases, findings, procedures, microorganisms, substances, etc. SNOMED was started in the U.S. by the College of American Pathologists in 1973 and revised into the 1990s. In 2002, SNOMED was merged and expanded with the UK National Health Service to become SNOMED CT. http://www.nlm.nih.gov/research/um ls/sourcereleasedocs/current/SNM/ SNOMED CT® SNOMED Clinical Terms® A systematically organized computer processable collection of medical terms (over 311,000 concepts) providing codes, terms, synonyms and definitions used in clinical documentation and reporting, originally creatred by the College of American Pathologists. SNOMED CT provides the core general terminology for electronic health records, and is used to encode qualitative laboratory results and microorganism identifications, as well as many other medical terms. SNOMED CT is one of two current terminologies required for Meaningful Use, along with LOINC, for the encoding of laboratory results. http://www.ihtsdo.org/snomed- ct/snomed-ct0/ 12 of 14", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Protocol", "definition": "The networking model and group of communications protocols used for the Internet and similar networks utilizing the Transmission Control Protocol (TCP) and the Internet Protocol (IP), which were the first networking protocols defined in this standard. TCP/IP provides end-to-end connectivity specifying how data should be formatted, addressed, transmitted, routed and received at the destination. http://www.protocols.com/pbook/tcp ip1.htm", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Telemedicine", "definition": "The use of medical information exchanged from one site to another via electronic communications to improve a patient’s clinical health status. Telemedicine includes a growing variety of applications and services using two-way video, email, smart phones, wireless tools and other forms of telecommunications technology. http://www.medicaid.gov/Medicaid- CHIP-Program-Information/By- Topics/Delivery- Systems/Telemedicine.html", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Telepathology", "definition": "A form of communication between medical professionals that includes the transmission of pathology images and associated clinical information for the purpose of various clinical applications including, but not limited to, primary diagnoses, rapid cytology interpretation, intraoperative and second opinion consultations, ancillary study review, archiving, and quality activities. http://www.medterms.com/script/m ain/art.asp?articlekey=33621", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "UCUM", "definition": "Unified Codes for Units of Measure A code system of standardized codes for measurement units used in medicine and pharmacy, based on 7 standard units. This industry standard, in international use since 1999, is published in English by the Regenstrief Institute. Medical documentation IT applications use UCUM for the unambiguous communication of measurements based on standard SI units. Application areas include the representation of laboratory tests, clinical examinations, and pharmaceutical data. http://unitsofmeasure.org/trac/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "UICC", "definition": "Union for International Cancer Control (Union Internationale Contre le Cancer) Previously known as the International Union Against Cancer, the UICC is a membership organisation that exists to help the global health community accelerate the fight against cancer. UICC was founded in 1933 and is based in Geneva, Switzerland, with a membership of over 800 organisations across 155 countries. http://www.uicc.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "USCAP", "definition": "United States and Canadian Academy of", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Pathology", "definition": "An organization with more than 10,000 members whose mission is to provide pathologists with high quality information at the investigative and applied practice level to reinforce and update their knowledge of pathology in their unique area(s) of interest, from anatomic to molecular pathology. http://www.uscap.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "VA", "definition": "U.S. Department of Veterans Affairs A U.S. government-run military veteran benefit system that provides patient care and federal benefits to veterans and their dependents. The Department has three main subdivisions, known as Administrations: Veterans Health Administration (VHA), Veterans Benefits Administration (VBA), and National Cemetery Administration. http://www.va.gov/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "VPN", "definition": "Virtual Private Network A private network that interconnects remote (and often geographically separate) networks through primarily public communication infrastructures such as the Internet. VPNs provide security through tunneling protocols and security procedures [1] such as encryption. For example, a VPN could be used to securely connect the branch offices of an organization to a head office network through the public Internet. http://en.wikipedia.org/wiki/Virtual_p rivate_network", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "WASPaLM", "definition": "World Association of Societies of Pathology and Laboratory Medicine An international organization to improve health throughout the world by promoting the teaching and practice of all aspects of Pathology and Laboratory Medicine, by promoting education, research, and international quality standards, through the Committees and Secretariats of WASPaLM and the World Pathology Foundation. http://www.waspalm.org/", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "WHO", "definition": "World Health Organization A specialized agency of the United Nations (UN) established in 1948 that is concerned with international public health. It is responsible for providing leadership on global health matters, shaping the health research agenda, setting norms and standards, articulating evidence-based policy options, providing technical support to countries, and monitoring and assessing health trends. http://www.who.int/en/ 13 of 14", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Whole Slide Imaging", "definition": "Whole slide digital imaging uses computerized technology to scan and convert pathology specimen glass slides into digital images which are then accessible for viewing using a computer monitor and viewing software. This is known as virtual microscopy because the images are viewed without the use of a microscope or slides. http://www.jpathinformatics.org/artic le.asp?issn=2153- 3539;year=2011;volume=2;issue=1 ;spage=36;epage=36;aulast=Panta", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "XML", "definition": "Extensible Markup Language A markup language that defines a set of rules for encoding documents in a format that is both human- readable and machine-readable http://en.wikipedia.org/wiki/Xml 14 of 14", "sources": [ "clinical-informatics-acronym-glossary.pdf" ], "file": "clinical-informatics-acronym-glossary.pdf", "type": "pdf" }, { "term": "Siebel", "definition": "Clinical Trial Management System Guide", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "September 2025", "definition": "Part Number: F84295-06 Copyright © 1994, 2025, Oracle and/or its affiliates. Authors: Siebel Information Development Team This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display in any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, the following notice is applicable: U.S. GOVERNMENT END USERS: Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs) and Oracle computer documentation or other Oracle data delivered to or accessed by U.S. Government end users are \"commercial computer software\" or “commercial computer software documentation” pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, the use, reproduction, duplication, release, display, disclosure, modification, preparation of derivative works, and/or adaptation of i) Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs), ii) Oracle computer documentation and/or iii) other Oracle data, is subject to the rights and limitations specified in the license contained in the applicable contract. The terms governing the U.S. Government’s use of Oracle cloud services are defined by the applicable contract for such services. No other rights are granted to the U.S. Government. This software or hardware is developed for general use in a variety of information management applications. It is not developed or intended for use in any inherently dangerous applications, including applications that may create a risk of personal injury. If you use this software or hardware in dangerous applications, then you shall be responsible to take all appropriate fail-safe, backup, redundancy, and other measures to ensure its safe use. Oracle Corporation and its affiliates disclaim any liability for any damages caused by use of this software or hardware in dangerous applications. Oracle and Java are registered trademarks of Oracle Corporation and/or its affiliates. Other names may be trademarks of their respective owners. Intel and Intel Xeon are trademarks or registered trademarks of Intel Corporation. All SPARC trademarks are used under license and are trademarks or registered trademarks of SPARC International, Inc. AMD, Opteron, the AMD logo, and the AMD Opteron logo are trademarks or registered trademarks of Advanced Micro Devices. UNIX is a registered trademark of The Open Group. This software or hardware and documentation may provide access to or information about content, products, and services from third parties. Oracle Corporation and its affiliates are not responsible for and expressly disclaim all warranties of any kind with respect to third-party content, products, and services unless otherwise set forth in an applicable agreement between you and Oracle. Oracle Corporation and its affiliates will not be responsible for any loss, costs, or damages incurred due to your access to or use of third-party content, products, or services, except as set forth in an applicable agreement between you and Oracle. The business names used in this documentation are fictitious, and are not intended to identify any real companies currently or previously in existence.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Preface .................................................................................................................................. i", "definition": "1 What’s New in This Release 1 What’s New in This Release ......................................................................................................................................................... 1 2 Overview of Siebel Clinical Trial Management System 7 Overview of Siebel Clinical Trial Management System .......................................................................................................... 7 About Siebel Clinical Trial Management System ..................................................................................................................... 7 Features of Siebel Clinical Trial Management System ........................................................................................................... 7 Product Modules and Options for Siebel Clinical Trial System ............................................................................................ 8 3 Setting Up Siebel Clinical 11 Setting Up Siebel Clinical ............................................................................................................................................................. 11 About Setting Up Siebel Clinical ................................................................................................................................................ 11 Configuring Properties for Siebel Clinical in Siebel Tools .................................................................................................... 12 Enabling or Disabling Siebel Open UI for Siebel Clinical ...................................................................................................... 12 Enabling Siebel Server Component Groups for Siebel Clinical ........................................................................................... 12 Activating Workflow Policies for Siebel Clinical ..................................................................................................................... 12 Configuring Web Services for Siebel Clinical .......................................................................................................................... 14 Administrative Setup Tasks for Siebel Clinical ....................................................................................................................... 14 About the My Team’s Filter ........................................................................................................................................................ 16 Using Siebel Assignment Manager in Siebel Clinical ............................................................................................................ 16 Setting Up Mobile Web Clients for Position Rollup ............................................................................................................... 19 4 Setting Up Clinical Trials 21 Setting Up Clinical Trials ............................................................................................................................................................. 21 About Setting Up Clinical Trials ................................................................................................................................................. 21 Scenario for Clinical Trials .......................................................................................................................................................... 22 Process of Managing Clinical Trials ......................................................................................................................................... 23 Creating Clinical Programs ......................................................................................................................................................... 24 Setting Up Clinical Protocols ..................................................................................................................................................... 25", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Integration", "definition": "This task describes how to configure the Web services that are required for clinical data management system integration. For more information about configuring Web services, see Integration Platform Technologies: Siebel Enterprise Application Integration . Note: It is recommended that you use HTTPS authentication. For information about configuring Secure Sockets Layer (SSL) for HTTPS authentication, see Siebel Security Guide . This task is a step in Process of Setting Up Clinical Data Management System Integration. To configure Web services for clinical data management system integration 1. Navigate to the Administration - Web Services screen, then the Inbound Web Services view. 2. Query for each of the following Web services, and update the language and address variables: ◦ ClinicalSubject Inbound Web service. ◦ LS Clinical Protocol Site Interface Service 3. Click Clear Cache on the Inbound Web Services applet. Integrating Data for Subject Visits with Data for Activities Subject visit templates allow you to set up a template schedule. The template schedule is based on the protocol document. You use the template schedule to generate screening, rescreening, and enrollment schedules for each subject, according to the subject’s screening, rescreening, and enrollment dates. The Clinical Item Integration field in the subject visit template is used for integrating visit data with activity data between Siebel Clinical and Oracle Health Sciences InForm. To integrate data for subject visits with data for activities 1. Navigate to the Administration - Clinical screen, then the Visit Templates view. 2. In the Subject Visit Templates list, create a new record and complete the necessary fields. The Clinical Item fields in the Visits and Activities applets are automatically populated. 223", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Siebel Clinical", "definition": "The tasks that a user of the Siebel Mobile application for Siebel Clinical can execute in online and offline mode include the following: • Getting Started with Siebel Mobile Disconnected Applications (general tasks) • Switching to Offline Mode and Synchronizing Data • Managing My Site Visits for Siebel Clinical • Managing My Sites for Siebel Clinical Note: You must complete the relevant setup tasks detailed in this guide before using the Siebel Mobile disconnected application for Siebel Clinical. Getting Started with Siebel Mobile Disconnected Applications For information about how to get started with Siebel Mobile disconnected applications and about the common procedures that you can execute in online (connected) and offline (disconnected) mode in all applications, see the Getting Started chapter in Siebel Mobile Guide: Disconnected which includes information about the following: • Logging in to and out of Siebel Mobile • Navigating the Siebel Mobile user interface • Managing records in Siebel Mobile • Reviewing notification messages in Siebel Mobile • Configuring application settings for Siebel Mobile • Displaying location details in Siebel Mobile • Running predefined queries in Siebel Mobile • Using Attachments in Siebel Mobile • Printing from Siebel Mobile • Process of using Siebel Mobile disconnected applications in offline mode 231", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Preface", "definition": "This preface introduces information sources that can help you use the application and this guide. Using Oracle Applications To find guides for Oracle Applications, go to the Oracle Help Center at https://docs.oracle.com/. Documentation Accessibility For information about Oracle's commitment to accessibility, visit the Oracle Accessibility Program website.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Contacting Oracle", "definition": "Access to Oracle Support Oracle customers that have purchased support have access to electronic support through My Oracle Support. For information, visit My Oracle Support or visit Accessible Oracle Support if you are hearing impaired. Comments and Suggestions Please give us feedback about Oracle Applications Help and guides! You can send an email to: oracle_fusion_applications_help_ww_grp@oracle.com.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "What’s New in This Release", "definition": "What’s New in Siebel Clinical Trial Management System Guide, Siebel CRM 25.4 Update The following table lists the changes in this revision of the documentation to support this release of the software.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Description", "definition": "LSClinicalTripReportFileTransferQuery Queries the trip report details based on the given trip report Id or Visit name. For more information, see Request Message for TripReportFileTransferQuery. LSClinicalTripReportFileTransferQueryPage Queries the trip report details in pages based on the given filter criteria. For more information, see Request Message for ClinicalTripReportFileTransferQueryPage. LSClinicalTripReportFileTransferUpsert Either updates the trip report and its child components or inserts new child items to the trip report. For more information, see Request Message for TripReportFileTransferUpsert. Request Message for TripReportFileTransferQuery The following table describes the request message for TripReportFileTransferQuery. Note the following about using the Query method: • If no input is provided for any tags in the Query method, then the Web service may return an error – prompting you to refine the search to limit the output result set. • Use the required tags in the Query method to query the data. For example, to query site visits for a particular", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Charge Sheet Library", "definition": "2. A new attribute, ‘Charge Sheet Version’ is added to: a. Master Subject Visit Template level: - You can select from Active’ Charge Sheet Versions only. - Charge Sheet version set at this level will be the default Charge Sheet version applied to a new", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "PII Detection in CTMS", "definition": "PII Service Setup and Configuration If requires a Siebel Administrator can configure the PII detection service. By default, the system is set to ‘N’. As an admin in the CTMS system: 1. Navigate to Administration – Application > System Preferences and search for EnableOCIAILanguage. 2. Set the ‘System Preference Value’ to Y to use the PII service. Once the System Preference is set, you can configure the fields within CTMS that you want to validate for the PII information. Note: A server restart is mandatory for changes to take effect. 3. In Siebel Web Tools, Search for the component that PII information is required to be validated. For example, if the fields under Trip Report section in CTMS are required to be validated: a. Navigate to the Business Component section. b. Search for Clinical Trip Report. Search for Business Component User Props that contain the word Batch. 4. To configure the fields that you would like to validate for the PII detection: a. Go to Business Component User Properties. b. Search for Batch and set the ‘text:FIELD’ property with field names that are required to be validated.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Flow", "definition": "Modified topic. Explains how CTMS provides a outbound API to support pushing protocol/subject/SVT details from CTMS to PowerTrials. 1", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Using CTMS Dashboards", "definition": "Status RollUp Fields:Protocol 7: \"# CustomStatus\", \"# CustomStatus\" Status RollUp Fields:Region 7: \"# CustomStatus\", \"# CustomStatus\" BC: Clinical Region Status RollUp Fields 7: \"# CustomStatus\", \"# CustomStatus\" 5. Map this new field on Applet. 6. Navigate to Applet > Search for LS Clinical Site Subjects Infolet Applet. Add this new field in control and web template items. Note: You may need to inactivate any existing Web Template item since the Dashboard can display only 6 items at a time. System Preferences for Dashboards You need to set these System Preferences for DB and Milestone to function properly: System Preference Name System Preference Value CL - Dashboard TR Target CL - Site Status Dashboard Planned,Initiated,Enrolling,Terminated,Not Initiated,Closed 299", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Using CTMS Milestones", "definition": "You can create, track, and complete the milestones required for the Clinical study success. You can also create a milestone library at a customer level, add and modify Study milestone templates, apply a template, and create milestones. This chapter contains the following topics: • System Preferences for Milestones • Configuring Email Alerts for Upcoming Milestones and Missed Milestones System Preferences for Milestones You need to set these System Preferences for DB and Milestone to function properly: System Preference Name System Preference Value CL - Milestone Alert Days Configuring Email Alerts for Upcoming Milestones and", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "PowerTrials", "definition": "To set up integration between CTMS and PowerTrials system (for ENU customers), perform the following tasks: 1. Enabling Integration Between CTMS and PowerTrials Update the domain address as shown below in Outbound Web Services. Note: This step is a prerequisite for enabling the integration between the CTMS and PowerTrials system. 2. Importing the PowerTrials Certificate to the CTMS Application 3.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Questions", "definition": "(Read-only) Displays the assessment question when you save the assessment template record. Administrators set up questions when they set up the template. 48", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Applications", "definition": "Modified topic. Information on supporting operation systems for disconnected mobile experience. What’s New in Siebel Clinical Trial Management System Guide, Siebel CRM 23.3 Update The following table lists the changes in this revision of the documentation to support this release of the software.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "in Siebel Clinical", "definition": "Modified topic. Added the following user properties for Clinical Trip Report: • BIP Report Template Name • Enable FollowUp Activities for Approved TR • Enable FollowUp Activities with Status Not Equal To • Enable Question Number • Enable SmartScript Page Save •", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Enable Unblinding", "definition": "What’s New in Siebel Clinical Trial Management System Guide, Siebel CRM 23.1 Update The following table lists the changes in this revision of the documentation to support this release of the software.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Oracle Clinical One", "definition": "Integrating CTMS with Oracle Clinical One involves: • Setting up the User Access for REST APIs • Creating a Communication Profile 248", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Clinical", "definition": "The following table describes the business component user properties that you can use to enable and configure functionality for Siebel Clinical.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "About Site Visit Name", "definition": "The site visit Name is user generated by default when you manually type in the name of the site visit during site visit creation. However, you can enable the site visit Name to be automatically generated. The following procedure shows how to enable the automatic generation of site visit Name by configuring the Calculate Site Visit Name user property in Siebel Tools. Once enabled, the site visit Name is automatically generated when you create a new site visit. To enable the automatic generation of site visit Name 1. Log in to Siebel Tools. 94", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Grouping Site Visits", "definition": "You can group site visits together according to the following categories: All Visits, Pending Visits, Pending Approval, and Closed Visits. • The All Visits category groups all site visits together irrespective of the Visit or Trip Report status. • All other categories group site visits according to various combinations of Visit and Trip Report status.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Trip Reports", "definition": "The Approvals view provides a summary audit trail of the changes to the trip report status, including the dates and times of review and approval operations, and the details applicable to the users who complete those operations. To use audit trail for reviews and approvals of a clinical trip report 1. Navigate to the Site Visits screen, then the Clinical Site Visits List view. 2. In the Clinical Site Visits list, drill down on the Visit Start field of the site visit for the required trip report. The Trip Report form for the selected site visit appears. 3. Navigate to the Approvals view. Some fields are described in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "eTMF", "definition": "To set up integration between CTMS and eTMF, perform the following tasks: 1. Enabling Integration Between CTMS and eTMF Integrating Siebel Clinical with Oracle Business Intelligence Publisher (BI Publisher) to generate reports is a prerequisite to this step. 2. Configuring Email Recipients 3. Configuring the LS Clinical Trip Report File Transfer Web Service 4. (Optional) Manually Generating Trip Report Files 242", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "for Clinical Trials", "definition": "Supporting Blinded and Unblinded Users for Clinical", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Overview of Siebel Clinical Trial Management System", "definition": "This guide documents Siebel Life Sciences with the optional modules installed. In addition, the Sample database includes data for optional modules. If your installation does not include some of these modules, then your software interface differs from that described in some sections of this guide. The exact configuration of Siebel Life Sciences screens and views depends on your company’s configuration of Siebel Life Sciences. For more information about Siebel Life Sciences, see Siebel Life Sciences Guide . For introductory information about using the Siebel Life Sciences interface, see Siebel Fundamentals . Note: The Siebel Bookshelf is available on Oracle Technology Network (http://www.oracle.com/technetwork/ indexes/documentation/index.html) and Oracle Software Delivery Cloud. It might also be installed locally on your intranet or on a network location. 9", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Management System", "definition": "Overview of Siebel Clinical Trial Management System This chapter provides an overview of Oracle’s Siebel Clinical Trial Management System. It includes the following topics: • About Siebel Clinical Trial Management System • Features of Siebel Clinical Trial Management System • Product Modules and Options for Siebel Clinical Trial System About Siebel Clinical Trial Management System Siebel Clinical Trial Managements System allows biotechnology companies, pharmaceutical companies, and CROs (clinical research organizations) to better manage the clinical trial process, maintain quality of clinical trials, and manage investigator relationships. It provides a comprehensive set of tools for CRAs (clinical research associates), clinical investigators, and site coordinators, and includes a personalized Internet portal to conduct study activities more efficiently. The following products are supported: • Siebel Clinical Trial Management System • Siebel Clinical Trial Management System Cloud Service Features of Siebel Clinical Trial Management System Siebel Clinical supports the following functionality: • Support for full clinical trial hierarchies of Subject-Site-Region-Protocol-Program • Support for global trials running in multiple countries, multiple languages, and multiple currencies • Support for randomized trials • Support for multi-arm, epoch, and adaptive trials • Site management tools for CRAs (clinical research associates), including a site calendar, trip reports, document tracking, and payment generation • Personalized Internet portal to help site coordinators, clinical investigators, and CRAs better manage clinical", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "trials over the Web", "definition": "• Project and resource management • A flexible audit trail engine 7", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "System", "definition": "N/A N/A Viewing Universal Inbox Notifications for Action Items of", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Setting Up Siebel Clinical", "definition": "5. In the Integration Object Map list, query for Clinical*Position*. 20", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Siebel Life Sciences", "definition": "• Activating workflows for accounts", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Contacts", "definition": "Use this view to maintain a list of contacts associated with the project. Enter names of employees in subcontracting or partner organizations.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "accounts contacts", "definition": "• Generating column maps for accounts", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "contacts list", "definition": "• Creating product data to appear in accounts contacts list Siebel Life Sciences Guide Creating a clinical program •", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Revising protocols", "definition": "• (Optional) Setting up regions • Defining a subject visit template Setting Up Clinical Trials Administering Clinical Subjects and Clinical", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Visits", "definition": "Site visit data integration SWI LS Clinical Subject Inbound - Activity Clinical data management system integration 280", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Managing sites", "definition": "• Creating protocol site templates • Creating assessment templates for", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "contacts and accounts", "definition": "• Maintaining contact and account", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "information", "definition": "• Setting up contracts for sites Managing Sites and Contacts for Clinical", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Trials", "definition": "This chapter describes blinded and unblinded support in Siebel Clinical, how to control access to data for blinded and unblinded users, and how to administer blinded and unblinded users for clinical trials. It includes the following topics: • What is a Blinded and Unblinded Clinical Trial? • Blinded and Unblinded Support in Siebel Clinical • Controlling Access to Data for Blinded and Unblinded Users • Site Management for Blinded and Unblinded Users • Inheritance Hierarchy for Blinded and Unblinded Users • Blinded and Unblinded Support in Siebel Mobile Disconnected Applications • Blinded and Unblinded Customization Support Note: This feature is available in Siebel CRM 20.7 Update and later releases. What is a Blinded and Unblinded Clinical Trial? A blinded clinical trial is one where participants do not know which treatment or medical intervention they have been allocated. In a blind clinical trial, certain information which may influence the participants in the trial (including subjects, CRAs, and evaluators) is withheld or hidden (blinded) until after the trial is complete. Good blinding can reduce or eliminate experimental biases that arise from participant expectation, observer bias, confirmation bias, and so on. An unblinded clinical trial is one where information is not withheld from trial participants and, in such cases, both participants and researchers know which treatment is being administered. During the course of a clinical trial, a participant becomes unblinded if they obtain information that has been withheld or hidden (blinded) from them. However, if this occurs unintentionally before the end of the trial, then this can be a source of experimental error. Blinded and Unblinded Support in Siebel Clinical Siebel Clinical provides the option to have and the ability to manage both blinded and unblinded users for clinical trials in Siebel CRM 20.7 Update and later releases. 203", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "projects", "definition": "• Creating activity templates for projects Managing Clinical Projects", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Importing data", "definition": "• Importing data with Siebel Enterprise", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Integration Manager", "definition": "• Importing, extracting, and routing", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "syndicated data", "definition": "• Charting denormalized syndicated data Siebel Life Sciences Guide Configuring Siebel Clinical • Configuring user properties for business", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "components", "definition": "• Configuring user properties for business", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "services", "definition": "• Configuring applet properties • Configuring field properties •", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Configuring workflows", "definition": "Developer’s Reference for Siebel Clinical 15", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "For More Information", "definition": "• Customizing Web services About the My Team’s Filter The visibility filter appears on many screens. It provides a list of filters, such as My Contacts, My Team’s Contacts, and All Contacts. These filters determine the records that appear in the view. The behavior of the My Team’s filter varies from screen to screen. In some screens, this filter displays those records where the primary member of the team reports to the user. In other screens, this filter displays records where any of the team members report to the user. The Manager List Mode user property in the business component determines this behavior. If the Manager List Mode user property is active and set to Team, then the My Team’s filter displays all records where the user’s subordinate is on the team but is not necessarily the primary member. The following information lists the default setting of the Manager List Mode user property for some Siebel Clinical screens and business components.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Active", "definition": "• Cannot delete Sheet version • New charge sheet items can be added • Restricted update to following fields: ◦", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Account", "definition": "• Initiation completed date •", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Account Brick", "definition": "The source table for Account Brick is changed to S_CON_ADDR, and the source column for Account Brick is changed to BRICK_ID.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Contact", "definition": "• Contact Medical Specialty Code •", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Contact Zip Code", "definition": "• Contact City State Country •", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Position", "definition": "Contact Assignments in Siebel Clinical In most Siebel Business Applications, contact assignment is based on the primary address. This process is different for Siebel Life Sciences. A contact in Siebel Life Sciences can have multiple addresses, and each representative on the 17", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "File", "definition": "Complete the procedure in this topic to export DTE data maps from the server database to an XML file. To export DTE data maps from the server database to an XML file 1. In Siebel Clinical, connect to the server database. 2. Navigate to the Administration - Integration screen, then the Data Maps view. 3. In the Integration Object Map list, query for Clinical*. The query returns the following records: Clinical Region Position to Protocol Position Map, Clinical Site Position to Account Position Map, Clinical Site Position to Protocol Position Map, and Clinical Site Position to Region Position Map. 4. Click Menu (the cogwheel icon), and select Export Data Map. 5. In the dialog box, check Export All Rows in Current Query and click Export. 6. In the dialog box, select Save to Disk, select a location, and save the data maps as PositionRollupDataMap.xml. Importing DTE Data Maps to a Local Client From an XML File Complete the procedure in this topic to import DTE data maps to a local client from an XML file. To import DTE data maps to a local client from an XML file 1. In Siebel Clinical, connect to the local client. 2. Navigate to the Administration - Integration screen, then the Data Maps view. 3. In the Integration Object Map list, click Menu (the cogwheel icon), and select Import Data Map. 4. In the dialog box, select Browse and find PositionRollupDataMap.xml. For information about creating this file, see Exporting DTE Data Maps From the Server Database to an XML File. 19", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Setting Up Clinical Trials", "definition": "3. In the Assessment Templates list, create a new record and complete the necessary fields. Some fields are shown in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "End-User Procedures", "definition": "The following list shows the tasks end users typically perform to manage clinical projects: • Creating Clinical Projects. You associate protocols with projects. • Associating People and Accounts with Clinical Projects. Give employees access to the project; add contacts and accounts to the project team workbook. • Creating Activities and Tasks for Clinical Projects. Standalone activities are not associated with tasks. • Monitoring Costs for Clinical Projects. View costs for clinical projects. • Managing Risk for Clinical Projects. Document project risks and resolution activities. Creating Activity Templates for Clinical Projects You can create activities in the Projects screen. If the study managers primarily enter activities in the Projects screen, then creating activity templates for projects is advantageous. To create an activity template for projects, you create an activity template with a Project type. The Protocol Type field is optional because you can apply the activity template to any project, regardless of the protocol associated with the project. In the Activity Template Details list, create records to describe activities and milestones for the project. For information about how to create activity templates, see Siebel Applications Administration Guide . This task is a step in Process of Managing Clinical Projects. Setting Up Employee Profiles for Clinical Projects End users can use Siebel Assignment Manager to automatically search the employee database for the available employees whose skills best fit the needs of the project. Siebel Assignment Manager requires that you set up profiles of skills and competencies for employees. For information about using Siebel Assignment Manager, see Siebel Assignment Manager Administration Guide . Use of Siebel Assignment Manager is not required. End users can assign team members directly into the Team Workbook view, without using Siebel Assignment Manager. This task is a step in Process of Managing Clinical Projects. Setting Up Position Types and Rate Lists for Billing If project team members bill their time to the project through the Team Workbook view, then set position types and rate lists. 167", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Program", "definition": "Select the name of the program for the clinical trial.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Mechanism", "definition": "Select the partners associated with the clinical program.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Application", "definition": "Select a record containing details of the application for the clinical program. If necessary, create an application record. This record contains values for the following fields: Number. The number assigned to the application when it is submitted to the regulatory agency, for example the (A)NDA or IND number. Type. The type of application, such as CTN, IND, or CTX. Sub-Type. The application filer, for example, a company or an investigator. Filed. Whether the application is filed with the specified regulatory agency. Product. The applicable product for the application. You must complete this field before you can create a protocol for the program. Indication. The clinical indication for the application. 3. (Optional) Drill down on the Program field of the new record and associate files with the clinical program. 24", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Favorite", "definition": "Select this field and then select the star icon that appears to indicate that this site is a favorite. Deselect the star icon to remove the star icon from the Favorite field. A star icon appears in the Favorite field of each favorite site. Favorite sites appear first in the Protocol Site List.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Title", "definition": "Type a descriptive title for the protocol. 4. If required, click Pause at any time to pause the task. The task moves to your Inbox where you must go when you want to resume work on the task. 5. If required, click Cancel at any time to cancel the task. 6. Click Submit to submit the task - that is, create the new protocol. Creating Protocols and Regions The following procedure shows how to create a protocol and region using Siebel Clinical task-based UI. To create a protocol and region 1. Click the Tasks icon on the application taskbar to open the Task Pane applet for Siebel Clinical. 2. Click Create Protocol in the Task Pane applet. 3. Enter protocol data on the page that appears: ◦ Fields to complete are described in Creating Protocols. ◦ Select the Regions Required check box. 4. Click Submit. 5. Click Yes when prompted with the following question: Do you want to create Region now? 6. Click Submit. 192", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Status", "definition": "About Integrating Data for Activity Completion Oracle Health Sciences InForm controls the integration of activity completion data between Siebel Clinical and Oracle Health Sciences InForm. In Oracle Health Sciences InForm, when patient data is entered that complies with the criteria for the clinical item value for a visit or activity, Siebel Clinical receives a message containing a completion date. The visit or activity is updated with the status of Complete, and the completion date is populated. If the message from Oracle Health Sciences InForm does not contain a completion date, and the visit or activity in Siebel Clinical already has a status of Complete, then no change is made to the completion date or status in Siebel Clinical. Oracle Health Sciences InForm integrates activity completion data with Siebel Clinical as follows: • Siebel Clinical searches for the subject using the unique subject identifier (row ID). When the subject is found, it searches for the activity as follows: ◦ Siebel Clinical searches for the activity using the clinical item for the visit and the clinical item for the visit activity as follows: - If the clinical item in the update corresponds to a subject visit, then the completed date for that visit is updated. - If the clinical item in the update corresponds to an activity for a subject visit, then the completed date for that activity is updated. ◦ If the clinical item sent from Oracle Health Sciences InForm cannot be mapped to an activity completion item in Siebel Clinical, then an error is generated to indicate that the update failed. 219", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Product", "definition": "Select the product for the clinical trial. You can select only the products that are associated with the clinical program. # Planned Sites Type the number of planned sites for the protocol. 191", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Phase", "definition": "Select the phase of clinical trial, such as, Phase I, II, or III.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Objective", "definition": "Type the objective for the clinical trial.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Sponsor", "definition": "Select the clinical trial sponsor.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Design", "definition": "Select the type of study. 25", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Regions Required", "definition": "Select this check box only if the sites for the protocol must belong to a region (and see Creating Protocols and Regions for more information). When you select this check box, you cannot create sites directly under protocols. You must create regions first, and then create sites that are associated with regions. Deselect this check box to indicate that the sites for this protocol must not belong to a region. Protocol # Type an identifying number for the protocol.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Type", "definition": "Select the purpose of the protocol.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Team", "definition": "Read-only field. The team for the satellite site, inherited from the parent site. For more information, see Creating Sites for Clinical Trials (Step 3.). 6. Drill down on the Satellite Site Number field to complete more fields as necessary. Some data is inherited from the parent site and cannot be changed. Some fields are shown in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Approval Date", "definition": "Select the date that the regulatory authority approves the protocol.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Currency Code", "definition": "Select the currency that will be used to display the payments, costs, and budgets for the region. The default value is USD (US dollars).", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Planned Start Date", "definition": "Select the planned start date for the study.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Exchange Date", "definition": "Select the date that determines the exchange rate of the currency. By default, the exchange date for the region is the date that you create the region.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Planned End Date", "definition": "Select the planned end date for the study.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Withholding Amount", "definition": "Type the amount to withhold from each of the payments to the investigators until the trial completes. Withholding Percent (%) Type the percentage to withhold from each of the payments to investigators until the trial is complete.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Actual Start Date", "definition": "Select the date that the study begins. # Planned Sites Type the number of planned sites for the protocol. # Planned Subjects Type the number of planned subjects for the protocol. Withholding % Type the percentage to withhold from each of the payments to the investigators until the trial is complete. You can overwrite this value at the region and site levels.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Actual End Date", "definition": "Select the date that the study concludes. 26", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Comments", "definition": "◦ Active flag (on Charge Sheet Items)", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Central Lab", "definition": "Select the name of the laboratory associated with the study. You create this name in the Accounts screen.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "CRO", "definition": "Select the Account name to assign the associated CRO. # Planned Sites Type the number of planned sites for the region. # Planned Subjects Type the number of planned subjects for the region.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Role", "definition": "The role of the contact.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Start Date", "definition": "The date when the contact record is active.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "End Date", "definition": "Displays the date that the contact record is archived. Adding Address Types for Sites Users can add a specific type of addresses for each site. This task is a step in Process of Managing Sites and Contacts for Clinical Trials. To add an address type for a site 1. Navigate to the Site Management screen, then the Protocol Site List view. 2. In the Protocol Site list, drill down on the site number field of the site for which you want to add an address type. 3. Navigate to the Addresses view. 99", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Lock Record", "definition": "Select this field to make the record read-only and to make the End Date field a required field. Automatically Assigning Team Members to a Protocol Using the Position Rolldown Button When you add a team member to a protocol, click the Position Rolldown button to add the member to all regions and all sites under the protocol. You can add a member to the team only once. When you click the Position Rolldown button to add a member to the team of a protocol, a record is created, where applicable, in each of the Team History views for all regions and all sites belonging to the protocol. The Position Rolldown mechanism automates the addition of team members to the Team History view for sites and the Team History view for regions as if they are manually added. About Removing Team Members From the Team of a Protocol When you remove a team member from the protocol, the team member is removed from either the protocol, or from all protocols, regions and sites belonging to the protocol. When you remove a member from the team of a protocol (either manually or through Position Rollup or Position Rolldown), the End Date field of the team member’s record, if present, is updated with the system date. However, if 28", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Original Version", "definition": "Select this field if this version is the first version of the protocol. If this field is checked, then the Amendment Version field is read-only.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Amendment Version", "definition": "Select the version number of the protocol version, for example, Version 1, Version 2, and so on.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Date", "definition": "Displays the timestamp of the change.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Central IRB", "definition": "Select this field to indicate that all sites use a central institutional review board.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Regional CRO", "definition": "Select this field to indicate that a clinical research organization provides services to all sites in the clinical region. Creating Accounts and Contacts for Clinical Trials An account is the institution from which you manage clinical trials. Typically, it is the facility where the investigators conduct the trials. You can track as accounts IRBs (institutional review boards), central laboratories, CROs (clinical research organizations), and other subcontractors. You can associate multiple sites with an account, and an account can carry out multiple protocols. A contact is a person working at a clinical site. Contacts include investigators, typically medical professionals who are also researchers, and site coordinators, who might be the practicing nurses administering the treatment plan according to the clinical protocol. 33", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Region", "definition": "The geographical region to which the site belongs. Principal Investigator The principal investigator for the site (contact associated with the site).", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Protocol Region", "definition": "Displays the name of the region. This field is automatically populated with the protocol number and region name.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "No Site Info", "definition": "Select this option to indicate that no site information will be available under the region. In such cases, only summary information about site enrolment will be available for the region. Deselect this option to indicate that site information will be available under the region. 8. If required, click Pause at any time to pause the task. The task moves to your Inbox where you must go when you want to resume work on the task. 9. If required, click Cancel at any time to cancel the task. 10. Click Submit to submit the task - that is, create the new protocol and region. 193", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "and Rolldown Buttons", "definition": "When you click the Position Rolldown and Position Rollup button, a record is created, where applicable, in each of the Team History views for the protocol and all sites belonging to the region. The Position Rolldown mechanism automates the addition of team members to the Team History view for sites and the Team History view for protocols as if they are manually added. To remove a team member from the protocol, see About Removing Team Members From the Team of a Protocol. Creating Assignment Team History for Regions The Team History view allows you to administer and track team members who work in the region. It also provides details about the roles as well as the start and end dates. To create assignment team history for a region 1. Navigate to the Administration - Clinical screen, then the Region List view. 2. In the Region list, drill down on the Region field of the region for which you want to create a new team assignment history. 3. Navigate to the Team History view. 4. In the History list, create a new record and complete the necessary fields. 32", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Creating Accounts", "definition": "Complete the procedure in this topic to create an account.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "To create an account", "definition": "1. Navigate to the Accounts screen, then the Accounts List view. 2. In the Accounts list, create a new record and complete the necessary fields. Some fields are shown in the following table. To access more fields, click the show more button in the account form.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Site", "definition": "The site associated with the trip report file.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Account Type", "definition": "Select the type of account, such as Hospital, Clinic, IRB, and so on.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Account Team", "definition": "Select the members assigned to the account team. The team member who creates the account record is the primary team member.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Address", "definition": "The address for the contact.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Synonyms", "definition": "Select the synonyms for the account. This field allows you to refer to accounts in the way that you prefer. For example, an account named A/B Products, Inc., might have the following synonyms: AB, A/B, and AB Products. When you search for an account or enter an account in another part of your Siebel Business Application, you can use a synonym instead of the actual name.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Territories", "definition": "Select the territories that are associated with the account. 34", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "My Address", "definition": "Select the addresses for the contact. A contact can have more than one address. You must specify one address as the primary address. Each CRA (clinical research associate) assigned to the contact can specify a different address as the primary address. For example, one CRA might specify a private office as the primary address, while another CRA might specify a hospital department as the primary address.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Satellite Site", "definition": "Read-Only field. A star in this field indicates that this is a satellite site. Site # Type the number to assign to the site. This field is not required when the Status field for the site is Planned or Not Initiated. This field becomes required after a site is initiated. Protocol # Select the protocol from the list of existing protocols in the Pick Protocol dialog box.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Parent Site", "definition": "Read-only field. The parent site for the satellite site.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "PI Last Name", "definition": "Select the last name of the principal investigator for the site.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "No Subject Info", "definition": "Select this field to indicate that no subject information is available for a site. Only summary information about subject enrollment is available for such a site. 36", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Versions", "definition": "Read-only field.The version of the subject visit template, which is inherited from the parent site. Only one version can be active at a time. The active template is used when activities are generated for a subject. For more information about protocol versions, see Tracking and Revising Team Assignment History", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Contract Amount", "definition": "Type the amount of money that the contract is worth. If you enter multiple contracts, then the total value of all contract amounts equals the total contract amount for the site. This total appears in the Contract Amount field on the site form.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Paid To Date", "definition": "Displays the amount of money that you paid to date to the investigators.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Earned To Date", "definition": "The amount of money that investigators earned to date. Activate for Synchronization Select this field to activate the site for synchronization. This field is required for integration with Oracle Health Sciences InForm. When this field is checked, a new integration object for the protocol site is sent to Oracle Health Sciences InForm. The integration object creates the site in Oracle Health Sciences InForm, or updates the site, if it already exists. This field is read-only until the following conditions are met: ◦ The Synchronize Active Study Sites field of the protocol is set to true. ◦ The Primary Site Address field is populated.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Primary Site Address", "definition": "Select the primary address for the site. This field sets the primary location of the site for the study in Siebel Clinical. The Addresses dialog box displays all addresses for the site. This field is required for integration with Oracle Health Sciences InForm, and populates the site address when the site is created in Oracle Health Sciences InForm. 42", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Satellite Site Number", "definition": "Type the number to assign to the satellite site.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "PI First Name", "definition": "The first name of the principal investigator for the site (read-only field). # Planned Subjects Type the number of planned subjects for the protocol.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Site Initiated", "definition": "The date the satellite site was created. This field is blank by default.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Site Terminated", "definition": "The date the satellite site was terminated. This field is blank by default. 40", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Satellite Site Count", "definition": "Displays the number of satellite sites associated with this site. 8. Assign subject visits to the satellite site as follows: a. Navigate to the Subjects view. b. Create a new Subject record and complete the necessary fields. c. Drill down on the Screening # field. d. Create a new Visit Plan record and complete the necessary fields. Some fields are shown in the following table. For more information about subjects visits, see Defining Subject Visits.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Assigned To", "definition": "Select the users assigned to the trip report. The team member who creates the trip report is the primary owner.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Sequence", "definition": "Type the sequence number of the visit. Typically, the first visit has a sequence number of 1.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Name", "definition": "Type the name of the version of the training plan.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Clinical Region", "definition": "Clinical Remove Position From Site", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Assessment", "definition": "Type in the name of the template.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Clinical Protocol", "definition": "SWI LS Clinical Create Site Visit Geo", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Order", "definition": "(Read-only) Displays the order number for the question when you save the assessment template record. Administrators set up the order number for each question when they set up the template.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Category", "definition": "Select the type of training applicable to the training topic.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Technology", "definition": "◦ Data Collection, CRF source ◦", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Endpoints", "definition": "◦ Organization Experience ◦ Investigational Product/Study Medication ◦ IP Logistics/Supply Chain ◦", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Weight", "definition": "(Read-only) Displays the weight for the attribute when you save the assessment template record. Administrators set up the weight for each attribute when they set up the template. This field is essentially ranks the importance of the category. If all categories have a default value of 1.0, then all categories are of equal importance.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Considerations", "definition": "Type in any additional information relevant to the assessment question that should also be considered. Any information that you enter in this field automatically appears on screen (for example, as a tool tip) when the user places the mouse over the respective question during an assessment. 4. In the Question Values list, create records to further describe the assessment questions you created in the previous step, and complete the necessary fields as shown in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Value", "definition": "Select the value that applies to the option that you select in the Type field. For Impact and Probability, the values available are: ◦ High (3) ◦ Medium (2) ◦ Low (1) For Detectability, the values available are: ◦ Difficult to detect 3) ◦ Medium to detect (2) ◦ Easy to detect (1)", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Score", "definition": "Displays the sum of the Weight field multiplied by the Score field for the assessment attributes that are associated with the template. This field is populated after you assign values to assessment attributes.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Template Name", "definition": "Select a template that includes the appropriate attributes to assess the program, protocol, region, or site.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Updated", "definition": "Displays the date and time that the field was modified. 160", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Percent", "definition": "Displays as a percentage the result of the Score field for the template divided by the Maximum Score field for the template. This field is populated after you assign values to assessment attributes.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Maximum Score", "definition": "Displays the highest score possible for the template that you select. For each assessment attribute in the template, the Weight field is multiplied by the highest value possible in the Score field. The sum of these results is the maximum score for the template. 4. In the Assessment Questions list, enter a value for each question to assess the program, protocol, region, or site in the clinical trial. Some fields are described in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Impact", "definition": "Select an impact value for the question, which can be one of the following: ◦ High (3) ◦ Medium (2) ◦ Low (1) This value determines the impact of the individual risk on the trial.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Probability", "definition": "Select a probability value for the question, which can be one of the following: ◦ High (3) ◦ Medium (2) ◦ Low (1) This value determines the probability of occurrence of the individual risk.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Detectability", "definition": "Select a detectability value for the question, which can be one of the following: ◦ Difficult to detect 3) ◦ Medium to detect (2) ◦ Easy to detect (1) The higher the detectability of individual risk, the lower the overall risk to the trial", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Risk Score", "definition": "(Read-only) Displays the risk assessment score for the individual question. Individual risk scores are calculated automatically from the values in the following fields: Impact, Probability, Detectability, Weight. The default formula for calculating individual risk score is product of Impact, Probability, Detectability and Weight.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Risk Level", "definition": "Select a value that specifies the type of risk assessment template, which can be one of the following: ◦", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Rationale", "definition": "If required, type in any explanatory information to capture the rationale for category risk level assessment. 49", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Functional Impact", "definition": "Select a functional impact value for the attribute, which can be one of the following: ◦ Medical Monitoring Plan ◦", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Data Plan", "definition": "◦ Statistical Analysis Plan ◦", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Communication Plan", "definition": "This field highlights the specific functional plans that might be impacted by this assessment.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Mitigation", "definition": "If required, type in the mitigation actions or plans for categories with the highest category risk score. 50", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Administering Clinical Subjects and Clinical Visits", "definition": "To monitor status accruals for clinical subjects by visit type 1. Navigate to the Protocols, Regions, or Site Management screen. 2. In the list, select the record for which you want to create the charts. 3. Navigate to the Charts view. 4. Select values from the drop-down lists as follows: a. From the first drop-down list, select Subject Status Analysis. b. From the second drop-down list, select Subject Accruals. c. From the third drop-down list, select the time frame. d. From the fourth drop-down list, select the display type, such as bar chart or pie chart. 5. Click Go. Using Audit Trail for Changes to Subject Status The Status Audit Trail view provides a detailed history of the changes to Subject Status records, including the dates and times of the changes and details of the users who make the changes. To use the audit trail for changes to subject status 1. Navigate to the Subjects screen, then the Subject List view. 2. In the Subject list, drill down on the screening number field of the subject. 3. Navigate to the Status Audit Trail view. Some fields are described in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Version", "definition": "The version number of the trip report.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Obsolete", "definition": "When you create a new version of the subject visit template, the Status field is populated with a value of In Progress.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Change Summary", "definition": "Type a summary of changes to the version of the subject visit template. Defining Subject Visits This topic describes how to define subject visits in a subject visit template. 53", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Visit Type", "definition": "The type of site visit.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "End of Study", "definition": "You can add, modify, or delete values for the Visit Type field.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Planned", "definition": "Select this field to define a subject visit as a planned visit. This field is selected by default.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Status Tracking Visit", "definition": "Select this field to enable automatic tracking of the status in the Subject Status MVG (multi value group) for each visit type. You can set only one visit as the status tracking visit for each visit type. For example, if TreatmentPhase1, TreatmentPhase2, and TreatmentPhase3 clinical visits exist for a Treatment visit type, then you can set only TreatmentPhase3 as the status tracking visit. When you select the Status Tracking Visit field for a visit, the following status records are automatically created in the Subject Status MVG when each predefined visit type is processed: ◦ A Visit Type value of Screening creates a Screened status record in the Subject Status MVG. ◦ A Visit Type value of Re-Screening creates a Re-screened status record in the Subject Status MVG. ◦ A Visit Type value of Enrollment creates an Enrolled status record in the Subject Status MVG. ◦ A Visit Type value of End of Study creates a Completed status record in the Subject Status MVG. You can manually override the automatic value in the Subject Status MVG. Any create, update, or delete operations on the tracked status fields trigger automatic create, update, and delete operations in the Subject Status MVG. Automatic status tracking is not enabled for custom values in the Visit Type list. 54", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Lead", "definition": "Type the lead time from the start date. You define the start date in the Schedule Date field when scheduling the subject.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Lead Units", "definition": "Select the units for the lead time.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Min", "definition": "Type the time before the lead time that the visit can occur. For example, if Min is 1 and Min/Max Units is days, then the visit can occur one day before the scheduled date. Do not leave this field empty.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Max", "definition": "Type the time after the lead time that the visit can occur. For example, if Max is 2 and Min/Max Units is days, then the visit can occur up to two days after the scheduled visit. Do not leave this field empty. Min/Max Units Select the units for the Min and Max values. Do not leave this field empty. # CRF Pages Type the number of CRF (case report form) pages.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Withdrawn", "definition": "The Subject record automatically updates as follows: • A record with a Status field value of Withdrawn is added to the Subject Status MVG, and the value in the Withdrawn Date field is copied to the Date field of the Subject Status MVG. • The Status field is updated to Withdrawn. When a Screen Failure or Early Terminated event occurs, all remaining visits for the subject are deleted. For more information, see the LS Subject Terminate Study Status Value 1 user property in User Properties for Business Components in Siebel Clinical. Applying Protocol Amendments to Sites and Clinical", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Duration", "definition": "Type an estimate of the numeric value for the time that is needed to complete the training topic. 176", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Payment Flag", "definition": "Select this field to indicate that you pay the investigator for this activity. This flag is selected by default. For more information about payments, see Setting Up and Making Clinical Payments 56", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Payment Amount", "definition": "Type the standard amount that you pay the investigator for this activity. You can adjust this amount for each site or each individual. Approving Subject Visit Templates Setting the status of a subject visit template to approved sets the subject visit template as read-only. You can modify only the Approved Date field of an approved subject visit template. To approve a clinical subject visit template 1. Navigate to the Administration - Clinical screen, then the Visit Templates view. 2. In the Template Versions list, select the version of the template to approve. 3. Enter the date in the Approval Date field. 4. Select Approved in the Status field. About Automatic Tracking of Subject Status This topic describes the fields that the mechanism for automatically tracking subject status uses, and the automatic operations that they trigger in the Subject Status MVG (multi value group). The Subject Status MVG contains a history of the subject’s status. It contains the following fields. • Date. The date that users change or update the status. • Status. The status of the subject, for example, Screened, Enrolled, or Re-screened. • Primary. A flag that sets the current status. This field appears in the Status field of the Subjects view. • Comments. Comments about the subject status. • Visit Type. The type of clinical subject visit, such as Screening or Enrollment. This field is null for status records, such as Randomized and Withdrawn. Status Tracking Fields that Trigger Create and Delete Operations on Records in Subject Status MVG The following table lists the status tracking fields that trigger create and delete operations on the records in the Subject Status MVG. The records in the Subject Status MVG are automatically updated as follows: • Populating a status tracking field listed in the following table automatically creates the corresponding status record in the Subject Status MVG, including Status, Date, and Visit Type fields, where applicable • Deleting a status tracking field listed in the following table automatically deletes the entire corresponding status record in the Subject Status MVG, including the Status, Date, and Visit Type fields, where applicable. 57", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Status Tracking Field", "definition": "Field of Subject Status MVG Automatically Updated Creating Records for Clinical Subjects CRAs (clinical research associates) can enter information about clinical subjects. When they create the subject record, the subject visit template that is active for the site is used to set up a schedule of visits and activities for the subject. To create a record for a clinical subject 1. Navigate to the Site Management screen, then the Protocol Site List view. 2. In the Protocol Site list, drill down on the site number field of the site to which you want to add a subject. 3. Navigate to the Subjects view. 4. In the Subjects list, create a new record and complete the necessary fields. Some fields are shown in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Completed", "definition": "The end user completed the trip report, and it is ready for the end user to submit for review.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Override Status", "definition": "Status field in the Subject Status MVG is updated to Missed, or the status value in the Visit Plans list for that visit. 58", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Subject Status MVG", "definition": "The following table lists the status tracking fields that trigger update operations on the Date and Status fields of the Subject Status MVG. The fields of the Subject Status MVG are automatically updated as follows: • Populating or updating a status tracking field listed in the following table automatically triggers an update to the corresponding Date or Status field value in the Subject Status MVG. • Deleting a status tracking field listed in the following table automatically triggers a delete operation on the corresponding Date or Status field value in the Subject Status MVG.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Randomized Date", "definition": "Select the date that you randomize the subject into an arm of the trial. 6. Save the record. The Subject record automatically updates as follows: ◦ A Randomized status record is added to the Subject Status MVG (multi value group), and the value in the Randomized Date field is copied to the Date field of the Subject Status MVG. ◦ The Status field is updated to Randomized. Overriding Initial Subject Status You can override the initial subject status for a status tracking clinical visit by selecting a new status in the Override Status field. For example, for a status tracking clinical visit with a Missed value, you can subsequently set the Override Status field to Completed. 65", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Screen Failure Date", "definition": "Displays the date that the subject fails screening. 5. Click Schedule. The Schedule applet is launched. 6. Select a date in the Schedule Date field, and click OK. The subject visits record updates as follows: ◦ All the visits in the active subject visit template are copied to the Visit Plans list. ◦ The Visit Type, Name, Start Date, Planned, Status Tracking Visit, and Status fields are copied from the subject visit template. ◦ The planned dates and due dates are calculated using the lead time in the subject visit template and the start date in the Schedule Date field. The planned dates and due dates are calculated as follows: planned or due date equals schedule date plus lead time. Note: You can also schedule subjects through workflows. Set the Enroll Screen Rescreen Through WorkFlow user property to true to execute the schedule subject tasks in workflows instead of executing these tasks through applets and business component methods. If the Enroll Screen Rescreen Through WorkFlow user property is set to true, then workflows in other user properties are executed according to context. You can change workflow names to execute custom workflows. You can also modify other workflows and business service methods according to your needs. For more information about the Enroll Screen Rescreen Through WorkFlow user property, see User Properties for Business Components in Siebel Clinical. 61", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Withdrawn Date", "definition": "Displays the date that the subject withdraws from the clinical trial.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Early Terminated Date", "definition": "Displays the date that the subject’s participation in the trial terminates.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Completed Date", "definition": "Select the resolution date and time of the follow-up issue. You must populate this field because the filter in the All Follow-Up Items list uses this date to determine the closed follow-up items to show. 6. Set the Trip Report Status field to Submitted. The report is submitted to the reviewer for review. Completing Questionnaires for Clinical Trip Reports This topic describes how to complete a questionnaire for a clinical trip report. You launch the questionnaire in the SmartScript player from the Questions view of the trip report. Questions and responses along with comments (if any) are saved in the Questions list. To complete a questionnaire for a clinical trip report 1. Navigate to the Site Visits screen, then the Clinical Site Visits List view. 2. In the Clinical Site Visits list, drill down on the Visit Start field of the site visit for the required trip report. The Trip Report form for the selected site visit appears. 147", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Subject ID", "definition": "Type a unique identifier for the subject.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Encounter Date", "definition": "Select the date that the subject first registers for the trial. Screening # Displays the screening number for the subject. This field is automatically generated from the Subject ID field and the Encounter Date field. The screening number is automatically generated after you enter the Subject ID field and the Encounter Date field, and save the record.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Enrollment ID", "definition": "Type the principal ID number for the subject.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Randomization ID", "definition": "Type an ID number for the subject, which you can use in randomized studies where both an enrollment ID and a randomization ID are required. Informed Consent Dates Select the date that the subject signs the informed consent form for participation in the clinical trial. You must obtain informed consent prior to initiation of any clinical screening procedures.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Screen Failure Reason", "definition": "Select the reason the subject fails screening.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Withdrawn Reason", "definition": "Select the reason the subject withdraws from the clinical trial. Early Termination Reason Select the reason the subject’s participation in the trial terminates early. The following values are available: ◦", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Lost to Follow-Up", "definition": "◦ Non-Compliance with Study Drug ◦", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Screen Failure", "definition": "The Subject record automatically updates as follows: 69", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Not Done", "definition": "Scheduling Clinical Subjects Scheduling a subject applies the activated subject visit template. You enter a single start date for all subject visit types in the Schedule Date field. 60", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Fixed Date", "definition": "The remaining subject visit dates are rescheduled using the date in the Reschedule applet. Last Completed Visit Date The remaining subject visit dates are rescheduled using the delay between Planed Date and Completed Date for the last completed visit. The rescheduled dates for the planned dates and due sates are calculated as follows: Planned or Due Date equals Planned Date or Due Date plus Delay Administering Subject Visits in Batch Mode The Visit Types view displays the subject visit plan by visit type. Each distinct visit type for the subject appears in the Visit Types applet, with a read-only field indicating whether or not each visit type is planned for the subject. Associated 62", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Random ID", "definition": "Type the ID number assigned to the subject for the randomized trial.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Missed", "definition": "5. For a completed visit, enter the completion date in the Completed Date field. Transferring Clinical Subjects The subject transfer feature allows you to manage the transfer of subjects from one study site to another, with options to retain the subject’s visit data and also the destination study site’s visit template. During subject transfers, the payment exceptions from the destination site are applied and the payment data is recalculated. A history of all subject transfers is tracked at the subject and the site level. To ensure more robust data audit trails, you can no longer delete subjects and sites. To transfer clinical subjects 1. Navigate to the Subjects screen, then the Subject List view. 2. In the Subject list, select the subject that you want to transfer. 3. Click Transfer to open the Transfer applet. 4. In the Transfer applet, complete the fields as shown in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Informed Consent Date", "definition": "Select an informed consent date if prompted.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Scheduled Date", "definition": "The start date for the destination site’s clinical subject visits.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Reason", "definition": "Select a reason for the subject transfer from the Reason drop-down list.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Transfer Date", "definition": "The date of the subject transfer. 5. If the destination site’s Subject Visit Template version differs from the originating site, then you are prompted with a message similar to the following: Would you like to delete uncompleted visits from the old version and completed visits from the new version? Click OK to confirm the deletion of non applicable visits. After confirmation, the subject transfer process completes, new visits and payment exceptions are applied as defined by the destination site, and the transfer history for the subject and the site is updated accordingly. For more information, see Viewing Subject Transfer Information for Clinical Subjects and Sites. Viewing Subject Transfer Information for Clinical", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Subjects and Sites", "definition": "The Transfer History and Subject Transfer History views show all the subject transfer information for subjects and sites respectively. To view subject transfer information for clinical subjects and sites 1. View the subject transfer information for a subject as follows: a. Navigate to the Site Management screen, then the Protocol Site List view. b. Drill down on the Site # field of the site that you want. c. Navigate to the Subjects view. d. Drill down on the Screening # field of the subject for which you want to view subject transfer information. The Visits view of the Subjects screen appears showing the Visit Plans. e. Navigate to the Transfer History view. The Transfer History view appears showing all subject transfers against the subject. The view includes the following information for each subject transfer: Source Site Name, Destination Site Name, Transfer Date, Transferred By, Status at Transfer, Description. 2. View the subject transfer information for a site as follows: a. Navigate to the Site Management screen, then the Protocol Site List view. b. Drill down on the Site # field of the site for which you want to view subject transfer information. c. Navigate to the Subject Transfer History view. The Subject Transfer History view appears showing all subject transfers against the site as follows: - The Subject In applet lists all subject visits transferred to the site. 67", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Done", "definition": "Select the date that the visit occurred.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Done Flag", "definition": "Select this field to indicate that the visit occurred.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Calendar Planned", "definition": "Select the date and time that the subject visit is due. This field value is automatically populated in the Due field.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Lock Assignment", "definition": "Lock the assignment as required by selecting this field. 4. If required, click the plus (+) icon to create additional activity plans. 5. If required, click Pause at any time to pause the task. The task moves to your Inbox where you must go when you want to resume work on the task. 6. If required, click Cancel at any time to cancel the task. 7. Click the check box next to each activity plan that you want to associate with the site and then click Submit to submit the task - that is, create the activity plan(s) for the site. Creating Activity Plans for Existing Protocols The following procedure shows how to create activity plans for existing protocols using Siebel Clinical task-based UI. To create an activity plan for an existing protocol 1. Click the Tasks icon on the application taskbar to open the Task Pane applet for Siebel Clinical. 2. Click Other Protocol Tasks in the Task Pane applet. 3. Select the record that you want in the Protocols list and then click Next. 4. Select Yes for Activity Plans and then click Next. 5. Enter activity plan details on the page that appears - all fields are described Creating Activity Plans for Sites. 6. If required, click the plus (+) icon to create additional activity plans. 7. If required, click Pause at any time to pause the task. The task moves to your Inbox where you must go when you want to resume work on the task. 8. If required, click Cancel at any time to cancel the task. 9. Click the check box next to each activity plan that you want to associate with the protocol and then click Submit to submit the task - that is, create the activity plan(s) for the protocol. Creating Activity Plans for Existing Regions 197", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Early Terminated", "definition": "You can track additional custom subject statuses by configuring additional values for this property type. This property takes the following comma-delimited list of parameters: \"[Business Component Field Name]\", \"[DateField]\", \"[Status Value\" • [Business Component Field Name] is the name of the business component field that is tracked for automatic status tracking, for example, Randomization ID. • [Date Field] is the name of the corresponding date field that is tracked for automatic status tracking, for example, Randomized Date. • [Status Value] is the corresponding status value that is tracked for automatic status tracking, for example, Randomized. Status Tracking Field 1", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Subjects", "definition": "This topic describes how to set up PSDV (partial source data verification) for clinical subjects. You set up this verification by entering PSDV values in some fields when you create or update a record for a clinical subject. To set up partial source data verification for a clinical subject 1. Navigate to the Site Management screen, then the Protocol Site List view. 2. In the Protocol Site list, create a new record and complete the necessary fields. For more information, see Creating Sites for Clinical Trials. Alternatively, you can select an existing site record. 3. In the Protocol Site list, drill down on the site number field of the site record. 4. Navigate to the Subjects view. 5. In the Subjects list, create a new record and complete the necessary fields as shown in the following table. For more information, see Creating Records for Clinical Subjects. Alternatively, you can select an existing subject record to update it.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Early Termination", "definition": "In addition to automatically deleting non-applicable visits for protocol amendments, visits that are scheduled and deemed non-applicable by early termination are deleted. Visits that are not scheduled through a template are not deleted. The following rules apply to deleting the appropriate visits: 1. Delete non-applicable, scheduled visits after a subject terminates the study. When the Status field of a subject is changed to Early Terminated and the Early Terminated date is populated, all future visits are deleted. Future visits are visits with a Due date and an Early Terminated date. 2. Delete non-applicable, scheduled visits after a subject fails screening. When the Status field of a subject is changed to Screen Failure and the Screen Failure date is populated, all future visits are deleted. Future visits are visits with a Due date and a Screen Failure date. About Rolling Up Information for Subject Enrollment Siebel Clinical supports clinical organizations in better managing subject enrollment for their trials in real-time. To implement this tracking, subject information is rolled up from the site level to the region level and then to the protocol level or directly from the site level to the protocol level. However, frequently this data is not available to the clinical organization, which presents significant business challenges. For example, if organizations out source trials to CROs (clinical research organizations), then the clinical organizations cannot always receive subject level information. The enhanced subject rollup functionality provides accurate subject enrollment data at the region and protocol level, when subject level information is not available for each site or region. Characteristics of Trials Where Subject Level Data is Available for", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Each Site", "definition": "Trials, for which subject level information is available for each site, display the following rollup characteristics: • Subject enrollment information is automatically rolled up from the subject level to the site level, from the subject level to the region level, and from the subject level to the protocol level. • When a subject is the first subject to enroll for a site, region, or protocol, the date in the First Subject Enrolled field for that site, region, or protocol, is automatically populated. 73", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "for Each Site", "definition": "Trials, for which subject level information is not available for a site, display the following characteristics: • You can select the No Subject Info field for sites that do not have subject level information. • CRAs (clinical research associates) can enter information in the following fields for sites that do not have subject or site level information: ◦ # Screened ◦ # Re-Screened ◦ # Screen Failure ◦ # Enrolled ◦ # Completed ◦ # Early Terminated ◦ First Subject Enrolled ◦ Last Subject Off Study ◦", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Terminated Date", "definition": "◦ First Site Initiated Date ◦ Last Site Terminated Date • Information that you manually for regions without site data is rolled up in the same manner as the information for regions with subject data. Viewing Status Accruals for Clinical Subjects of Sites Clinical subject data is automatically rolled up to the clinical site record. The Status Field RollUp user properties determine the criteria for the automatic rollup of subject status accruals to the clinical site record. For more information about these user properties, see User Properties for Business Components in Siebel Clinical. You must select the Status Tracking Visit field (in the Visits list in the Visit Templates view of the Administration - Clinical screen) to automatically create subject status accruals for pairs of a visit type value and a subject status value. For more information about the Status Tracking Visit field, see Defining Subject Visits. The task in this topic describes how to view subject status accruals for each visit type and subject status of a clinical site. To view status accruals for clinical subjects of a site 1. Navigate to the Site Management screen, then the Protocol Site List view. 2. In the Protocol Site list, drill down on the site number field of the site for which you want to view subject accruals. 3. Navigate to the Subject Status Accruals view. Some fields are described in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Total Accrual Number", "definition": "Displays the number of current and past subject status accruals that are automatically created for the site visit.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Regions", "definition": "The Payments view of the Regions screen is automatically populated with the existing payment records for the sites that are associated with the region. These payment records apply to the complete payment activities for those sites. In this view, you can generate payment records for complete, unpaid payment activities for the sites in the region. To generate payment records for sites associated with a clinical region 1. Navigate to the Regions screen, then the Region List view. 2. In the Region list, drill down on the Region field of the region for which you want to generate payment records for sites. 3. Navigate to the Payments view. If payment records exist in this view, then the selected payment record is irrelevant to this procedure. 4. Click Generate Payment. A dialog box of site records applicable to the region appears. Your system administrator can set the Popup Visibility Type property to configure the records that appear in this dialog box. For more information about this property, see Applet Properties in Siebel Clinical. 5. In the dialog box of site records applicable to the region, select the appropriate sites, and click OK. The complete payments are removed from the Payment Activities view in the Protocol Site List view of the Site Management screen. Payment records for each unique contract, account and payee combination are generated in the Payments view in the Protocol Site List view of the Site Management screen. You must again access the Payments view in the Region List view of the Regions screen to view the generated payments. For the protocol that is associated with the selected sites, the generated payments also appear in the Payments view in the Protocol List view of the Protocols screen. 6. (Optional) To access a notification containing details about the payment generation, click Notification on the menu toobar. Your system administrator can set the WF properties to configure notifications for bulk payments. For more information about these properties, see Applet Properties in Siebel Clinical. For more information about using notifications, see Siebel Fundamentals Guide . Generating Payment Records for Unplanned Payment", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Protocols", "definition": "The Payments view of the Protocols screen is automatically populated with the existing payment records for the sites that are associated with the protocol. These payment records apply to the complete payment activities for those sites. In this view, you can generate payment records for complete, unpaid payment activities for the sites in the protocol. To generate payment records for sites associated with a clinical protocol 1. Navigate to the Protocols screen, then the Protocol List view. 2. In the Protocol list, drill down on the protocol number field of the protocol for which you want to generate payment records for sites. 3. Navigate to the Payments view. If payment records exist in this view, then the selected payment record is irrelevant to this procedure. 4. Click Generate Payment. A dialog box of site records applicable to the protocol appears. Your system administrator can set the Popup Visibility Type property to configure the records that appear in this dialog box. For more information about this property, see Applet Properties in Siebel Clinical. 5. In the dialog box of site records applicable to the protocol, select the appropriate sites, and click OK. The complete payments are removed from the Payment Activities view in the Protocol Site List view of the Site Management screen. Payment records for each unique contract, account and payee combination are generated in the Payments view in the Protocol Site List view of the Site Management screen. You must again access the Payments view in the Protocol List view of the Protocols screen to view the generated payments. For the region that is associated with the selected sites, the generated payments also appear in the Payments view in the Region List view of the Regions screen. 6. (Optional) To access a notification containing details about the payment generation, click Notification on the menu toobar. Your system administrator can set the WF properties to configure notifications for bulk payments. For more information about these properties, see Applet Properties in Siebel Clinical. For more information about using notifications, see Siebel Fundamentals Guide . 134", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Employee Login", "definition": "Displays the username of the user who changed the record.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Business Component", "definition": "Displays the business component for the record where the database change occurred.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Field", "definition": "Displays the name of the field where the change occurred.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Operation", "definition": "Displays the type of operation that was performed, for example, New Record, or Modify.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Old Value", "definition": "Displays the value in the field before the database change occurred.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "New Value", "definition": "Displays the value in the field after the database change occurred.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Record ID", "definition": "Displays the unique identifier of the record that was changed.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Base Table", "definition": "Displays the name of the primary database table where the database change occurred. 161", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Column", "definition": "Displays the name of the column in which the change occurred.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Group ID", "definition": "Displays the unique identifier of the group to which the user who changed the record belongs.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Node", "definition": "Displays the name of the database table node where the change occurred.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Table", "definition": "Displays the name of table to which the selected field belongs in the Siebel database.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Row ID", "definition": "Displays the unique identifier of the row in which the change occurred. Generating Oracle BI Publisher Reports for Site Visits You can integrate Siebel Clinical with Oracle Business Intelligence Publisher (BI Publisher) to generate reports. You can generate, view, and schedule preconfigured Oracle BI Publisher reports in Siebel Clinical. For more information about using Siebel Reports and integrating with Oracle BI Publisher, see Siebel Reports Guide . The following preconfigured reports apply to site visits: • Clinical Trip Report With CRF • Clinical Trip Report Without CRF To generate an Oracle BI Publisher report for a site visit 1. Navigate to the Site Visits screen, then the Clinical Site Visits List view. 2. In the Clinical Site Visits list, drill down on the Visit Start field of the site visit for which you want to generate an Oracle BI Publisher report. 3. On the application toolbar, click Reports. 4. In the Run Report pane, complete the necessary fields as shown in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Employee ID", "definition": "Displays the unique identifier of the user who changed the record. Generating Oracle BI Publisher Reports for Site", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Enrollment Status", "definition": "You can integrate Siebel Clinical with Oracle Business Intelligence Publisher (BI Publisher) to generate reports. You can generate, view, and schedule preconfigured Oracle BI Publisher reports in Siebel Clinical. The preconfigured Site Enrollment Status report applies clinical trials. For more information about using Siebel Reports, and integrating with Oracle BI Publisher, see Siebel Reports Guide . To generate an Oracle BI Publisher report for the site enrollment status 1. Navigate to the Protocols screen, then the Protocol List view. 2. In the Protocol list, drill down on the protocol number field of the protocol for which you want to generate an Oracle BI Publisher report. 3. Navigate to the Sites view. 4. On the application toolbar, click Reports. 5. In the Run Report pane, complete the appropriate fields as shown in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Report Name", "definition": "Select the Clinical Trip Report With CRF report or the Clinical Trip Report Without CRF report.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Output Type", "definition": "Select the output type for the report. 5. Click Submit. The report runs. 162", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Managing Sites and Contacts for Clinical Trials", "definition": "To generate a report for planned and actual dates of subject visits 1. Navigate to the Site Management screen, then the Protocol Site List view. 2. In the Protocol Site list, drill down on the site number field of the site for which you want to generate an Oracle BI Publisher report. 3. On the application toolbar, click Reports. 4. In the Run Report pane, complete the appropriate fields as shown in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Adding Notes to Sites", "definition": "When you work with site records, you often find that you want to make notes. In the Notes view, you can enter public notes or private notes. Use the link bar in the Notes view to switch between public and private notes. Anyone who can access the record can see a public note. Only the person who creates the note can see a private note. This task is a step in Process of Managing Sites and Contacts for Clinical Trials. 109", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Payee Last Name", "definition": "Select the payee last name from the list. The list displays all primary accounts for the selected contract.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Visit Name", "definition": "The name of the site visit.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Visit Start", "definition": "Starting date of the site visit.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Visit Status", "definition": "Select the status of the site visit, for example, Planned or Completed.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Trip Report Completed", "definition": "Select the date that the trip report is complete and ready for submission. This field is populated with the system date when you update the Visit Status field of the trip report to Completed. 146", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Trip Report Status", "definition": "To use audit trail for changes to a clinical trip report 1. Navigate to the Site Visits screen, then the Clinical Site Visits List view. 2. In the Clinical Site Visits list, drill down on the Visit Start field of the site visit for the required trip report. The Trip Report form for the selected site visit appears. 3. Navigate to the Audit Trail view. Some fields are described in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Rejected", "definition": "The reviewer or approver rejected the trip report.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "To group site visits", "definition": "1. Navigate to the Site Visits screen. The existing Clinical Site Visits are listed, if any. 2. Do one of the following as required: ◦ Click All Visits to see a complete list of all visits, irrespective of the Visit or Trip Report status. ◦ Click Pending Visits to see a list of all visits in Pending Visit status. ◦ Click Pending Approval to see a list of all visits in Pending Approval status. ◦ Click Closed Visits to see a list of all visits in Closed Visit status. The following table shows the default Visit and Trip Report status combinations for Site Visits that are defined in Siebel Tools.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Closed Visits", "definition": "Reviewed with Comments Submitted with Approval", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Revised", "definition": "The end user modified the rejected trip report, and it is not yet resubmitted for review.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Last Name", "definition": "The first name of the contact.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Address Type", "definition": "Select an address type from the list of values to associate with the site.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Address Line 1", "definition": "Select the appropriate site location from the MVG (multi value group). Assigning Employees to Site Teams CRAs (clinical research associates) assign employees to the site team. You can roll up the team members and make them visible at the region and protocol levels. Note: If the CRA is working from a mobile Web client, then the administrator must set up position rollup on the Web client. For more information, see Setting Up Mobile Web Clients for Position Rollup. Before you can add an employee to the site team, an administrator must set up the employee record. For more information, see Siebel Security Guide . You can also automatically assign an employee to the site team using the Position Rollup button or Position Rolldown button. For more information, see Automatically Assigning Team Members Using the Position Rollup and Rolldown Buttons. For more information about removing employees from the site team, see About Removing Team Members From the Team of a Site. This task is a step in Process of Managing Sites and Contacts for Clinical Trials. To assign employees to the site team 1. Navigate to the Site Management screen, then the Protocol Site List view. 2. In the Protocol Site list, select the site to which you want to add employees. 3. Edit the Team field of the site record. The employees are added to the site team. You also roll up the employees to the region and protocol levels. Creating Activity Plans for Sites An activity plan for a site is a list of activities and documents associated with the site. Although you can create activities without a template, using a clinical protocol site template as described in this topic makes creating activities for sites more efficient. For more information, see Creating Clinical Protocol Site Templates. 100", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Planned Start", "definition": "Select the date and time to start the activity plan.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Template", "definition": "Select the template for the activity plan. Only templates with a type of Clinical Protocol Site and with a protocol that matches the protocol at the site are available for selection. Only activities with type of Document or Site-Initiation appear in the document tracking views.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Expected Date", "definition": "Select the date that the site is expected to return the signed document.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Sent Date", "definition": "Select the date that you send the document to the site.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Received Date", "definition": "Select the date that the site returns the signed document.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Expiration Date", "definition": "Select the date that the document expires. 103", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Suppress Calendar", "definition": "Select this field to indicate that the activity does not appear on the user’s calendar. Applying Activity Templates to Sites You can simultaneously apply one or multiple activity templates to one or multiple sites for a study. Activity records with a type of Document and Site-Initiation appear in the Document Tracking view of the Site Management screen. Activity records with a type of Correspondence appear in the Activities view of the Site Management screen. The applied templates also appear in the Activity Plans view of the Site Management screen for each of the selected sites. This task is a step in Process of Managing Sites and Contacts for Clinical Trials. Applying Activity Templates to Sites in a Region Complete the procedure in this topic to simultaneously apply one or multiple activity templates to multiple sites in a region. To simultaneously apply one or multiple activity templates to multiple sites in a", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Protocol", "definition": "The protocol or study ID.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Activity", "definition": "Select the type of trip report activity. The following values are available: 144", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Activity Type", "definition": "Select an activity type. The following values are available: ◦", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Retrieved", "definition": "Select this field when you retrieve the CRFs from the site.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Retrieved Date", "definition": "Select the date and time that the CRF is retrieved.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Received in House", "definition": "Select the date and time that the CRA (clinical research associate) receives the CRF in house. Received by Data Management Select the date and time that a data management process receives the CRF.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Source Verified", "definition": "Select this field when you verify the CRF against the source document.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Source Verified Date", "definition": "Select the date and time that the CRF is source verified. 107", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Note Type", "definition": "Select the type of note. Examples include Exclusion, Pre-existing Condition, Permanent, System, Temporary, Business Description, Regional Plans, and Contracts Process.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Note", "definition": "Type the note text. 5. Click Check Spelling to make sure your note has no spelling errors. Viewing the Status History for Sites For each site, you can view information about how the Status field changed in the past. To view the status history for a site 1. Navigate to the Site Management screen, then the Protocol Site List view. 2. In the Protocol Site list, drill down on the site number field of the site for which you want to view the status history. 3. Navigate to the Status History view. Some fields are described in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Old Status", "definition": "Displays the value in the Trip Report Status field before the change occurred.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "New Status", "definition": "Displays the new value in the Status field for the site record.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Tracking", "definition": "You can integrate Siebel Clinical with Oracle Business Intelligence Publisher (BI Publisher) for generating reports. You can generate, view, and schedule preconfigured Oracle BI Publisher reports in Siebel Clinical. For more information about using Siebel Reports and integrating with Oracle BI Publisher, see Siebel Reports Guide . 111", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Subject Visits", "definition": "This topic describes how to generate the preconfigured Planned vs Actual Patient Dates report for Oracle BI Publisher. This report lists the planned and completed subject visit dates for a site. This task is a step in Process of Managing Sites and Contacts for Clinical Trials. 115", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Managing Partial Source Data Verification", "definition": "To recalculate the subjects requiring source data verification 1. Navigate to the Site Management screen, then the Protocol Site List view. 2. In the Protocol Site list, drill down on the site number field of a site record. 3. Navigate to the More Info view. 4. Click Reapply Auto-Select Rate. If the value in the Number of Initial Subjects field or the Subject Auto-Select Rate field changed, then the value in the Total Subjects Requiring SDV field is recalculated, and this recalculation considers the subjects in the site pool. About Partial Source Data Verification for Protocol", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Visit Templates", "definition": "This topic describes how to set up PSDV (partial source data verification) for subject visit templates. You set up this verification by entering PSDV values in some fields when you create a record for a subject visit template. These values are automatically populated in the same fields for all of the CRF (case report form) tracking records that are associated with the subject visit template. 119", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "SDV Required", "definition": "Displays an indication of whether SDV (source data verification) is necessary for the site visit. This field is read-only, and automatically populated from the value of the same field in the subject visit template that is associated with this CRF tracking record. Page Numbers to Verify Displays the page numbers of the CRF that are included in PSDV. This field is read-only, and automatically populated from the value of the same field in the subject visit template that is associated with this CRF tracking record. Tracking Case Report Forms for Partial Source Data Verification During Site Visits This topic describes how to track the CRFs (case report forms) for PSDV (partial source data verification) during a site visit. During a site visit, CRAs (clinical research associates) do not review all CRFs and all pages on those CRFs. The field values for PSDV in the subject visit template that is associated with the site visit determine the information that appears in the PSDV fields of CRFs. The CRA restricts the review to the information in these PSDV fields. To track the case report forms for partial source data verification during a", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "SDV Policy", "definition": "Select the Source Data Verification Policy.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Verification", "definition": "If you change the value in the Number of Initial Subjects field or the Subject Auto-Select Rate field of a site record, then you must recalculate the value in the Total Subjects Requiring SDV field of that site record. If you change the number of subjects in the site pool by changing the value in the SDV Required field of the site’s subject records, then you must recalculate the value in the Total Subjects Requiring SDV field of the site record. If you change the number of subjects in other pools, such as the subject pool and the status pool, then you do not have to recalculate the value in Total Subjects Requiring SDV field because these other pools are not included in the calculation of this field. For more information about pools, see Setting Up Partial Source Data Verification for Clinical Subjects. 125", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "site visit", "definition": "1. Navigate to the Site Visits screen, then the Clinical Site Visits List view. 2. In the Clinical Site Visits list, drill down on the Visit Start field of the site visit for which you want to track case report forms for partial source data verification. 3. Navigate to the Case Report Forms Tracking view. 4. To add case report forms for scheduled subject visits, complete the following steps: 124", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Page Numbers Verified", "definition": "Type the CRF page numbers that you verify.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Charts Reviewed Date", "definition": "Select the date and time that you review the clinical charts.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Forms Signed Date", "definition": "Select the date and time that you sign the CRFs. 149", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Amendments", "definition": "When you change a subject visit template, you create a new version of that template. This new version results in a protocol amendment. When you apply the protocol amendment to the subjects, the following processing occurs: 1. The subject visits that are no longer valid are deleted. 2. CRF (case report form) records associated with the subject visits are deleted. 3. New subject visits are created. 4. New CRFs are created with values for PSDV (partial source data verification) fields from the latest version of the subject visit template. For CRFs that already have values for PSDV fields, new CRFs are also created. The values for fields that are not available in the subject visit template are copied from the prior CRFs to the new CRFs, and the values for PSDV fields are copied from latest version of the subject visit template to the new CRFs. 5. Activities are created according to the protocol amendment. 126", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Setting Up and Making Clinical Payments", "definition": "Generating Oracle BI Publisher Reports for Clinical", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Subject Activities", "definition": "You set and adjust payments to investigators and sites at the following levels: • The financial administrator sets standard payment amounts through the subject visit template. • You set exceptions to standard payment amounts according to agreements that individual sites negotiate. • You can further adjust payments on a one-time only basis before you generate the payments. Not all subject activities have payment amounts associated with them. For example, obtaining informed consent might be a subject activity for which you do not pay the site, but you pay a site for performing a blood test. Subject activities for which you pay the site are payment subject activities. (In the Siebel Clinical interface, the Payment Flag field indicates these activities.) In addition to subject activities, you can pay sites for other activities that end users create at the site level, such as IRB (institutional review board) fees and equipment costs. End user can designate those activities as payable to the site with the Payment Flag field. For information about managing budgets at the protocol level, see Managing Clinical Projects. 127", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Templates", "definition": "Select the template for the activity plan.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Contract", "definition": "Select the payee contract from the list of contracts.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Payee First Name", "definition": "Displays the payee first name. Generating Payment Records for Sites This topic describes how to generate payment records from payment activities. Payments are generated for each unique contract, account and payee combination for complete payment activities. To generate a payment record for a site 1. Navigate to the Site Management screen, then the Protocol Site List view. 2. In the Protocol Site list, drill down on the site number field of the site for which you want to generate a payment. 3. Navigate to the Payment Activities view. 4. In the Payment Activities list, select the Completed field for each payment activity to use for generating the payment record. 5. Click Generate Payment. The complete payments are removed from the Payment Activities list. Payment records for each unique contract, account and payee combination are generated in the Payments list. Generating Payment Records for Sites Associated with Clinical Protocols and Regions You can generate payment records for the sites that are associated with a clinical protocol and with a clinical region. After you complete the payment generation task, the payment records can appear in the Payments view in the Protocol List view of the Protocols screen and in the Payments view in the Region List view of the Regions screen. Also, the payment records appear in the Payments view in the Protocol Site List view of the Site Management screen. Payment 133", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Split Percentage", "definition": "Type the split percentage for each payment split. The total split percentage for all of the payment splits must be one hundred percent. The split percentage is automatically calculated if you manually enter the amount in the Split Amount field. 130", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Split Amount", "definition": "Displays the split amount by using the split percentage of the total payment amount. You can also manually enter the amount. 7. After you create all payment splits, verify that the Split Status (icon) in the Payment Exceptions view is activated (or green in color), which indicates that the total splits for the payment amount to one hundred percent. Copying Details for Payment Splits You can copy the details of a split payment activity to multiple payment activities. You copy payment activity splits at the payment exceptions level. To copy details for a payment split 1. Navigate to the Site Management screen, then the Protocol Site List view. 2. In the Protocol Site list, drill down on the site number field of the site for which you want to copy split payment details. 3. Navigate to the Payment Exceptions view. 4. In the Payment Exceptions list, select the record that you want to copy. 5. Click Apply Split to Other. The Payment Activities Select Applet appears. 6. Select the payment activities to which you want to apply the split details, and click OK. The split details are created in the Split Details applet for all of the selected payment activities. 7. Navigate to the Split Details applet, and make any required amendments. Reversing Splits for Payment Activities This topic describes how to reverse a payment activity split. The split details for each payment are deleted from the Split Details applet. To reverse a split for a payment activity 1. Navigate to the Site Management screen, then the Protocol Site List view. 2. In the Protocol Site list, drill down on the site number field of the site for which you want to reverse payment activity splits. 3. Navigate to the Payment Exceptions view. 4. In the Payment Exceptions list, select the records that you want to reverse. 5. Click Unsplit. 6. Click OK to delete the split details for the selected payment activities. The split details for each selected payment activity are deleted from the Split Details applet. 131", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Payment", "definition": "Select this field to define the activity as a payment activity.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Standard Amount", "definition": "Type the payment amount prior to any adjustment. The actual amount is calculated as follows: Standard Amount plus Deviation Amount equals Actual Amount 5. Click Generate Payment. The complete payments are removed from the Payment Activities list, and the payment record is created in the Payments list. 6. To complete the payment, follow Step 6 to Step 8 in Adjusting Payment Amounts and Generating Payment Records for Sites. Adjusting Payment Amounts and Generating Payment", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Deviation Amount", "definition": "Type a value in this field to adjust the value in the Actual Amount field. 132", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Actual Amount", "definition": "Displays the actual amount to pay to the site. The actual amount is calculated as follows: Standard Amount plus Deviation Amount equals Actual Amount", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Activities", "definition": "Follow the steps to assign CPT Codes and Billing Designation to Visit Template activities: Note: A new mandatory field of ‘Branch’ has been added to Visits in SVTs. 1. In Activities, go to the CPT Code field. 2. Select Charge Sheet item from the associated Charge Sheet version from a pick list. The CPT Code, CPT Description, and Payment Amount is populated after the selection. 3. Select a value for Billing Designation from the following default values: ◦ Standard of Care (SOC) ◦ Research Related (RR) ◦ Non Billable (NB) 306", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Records for Sites", "definition": "Although payments are generally set for each site, occasionally the financial administrator might want to make additional adjustments to the paid amount for a given payment activity. For more information, see Setting Payment Exceptions for Sites. When the financial administrator finalizes the amounts, payments are generated for all complete payment subject activities in the currency for the site. Each payment record is given a unique identity number. You can later enter other information, such as check number, check date, and check amount, either manually or by importing the data from a back-office finance application. Note: This task requires administrator privileges. To adjust the payment amounts and generate payment records for a site 1. Navigate to the Site Management screen, then the Protocol Site List view. 2. In the Protocol Site list, drill down on the site number field of the site for which you want to generate payments. 3. Navigate to the Payment Activities view. This view lists all scheduled payment subject activities for subjects associated with the site. 4. (Optional) Adjust the Actual Amount to pay to the site by entering a value in the Deviation Amount field. Standard Amount plus Deviation Amount equals Actual Amount 136", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Earned Amount", "definition": "Type the sum of the actual payment amounts for the complete payment activities. The sum of all values in the Earned Amount column equals the Earned to Date value of the site.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Requested Amount", "definition": "Displays the requested amount of payment for the complete payment activities at this site. This field is calculated as follows: Requested Amount equals Earned Amount times [(100 less Withholding Percentage divided by 100) less Withholding Amount]", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Check Amount", "definition": "Type the amount of money for the check. This field is usually, but does not have to be, the same as the earned amount.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Check Date", "definition": "Select the issue date for the check.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Check Number", "definition": "Type the number of the check.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "VAT Amount", "definition": "Type the value added tax for the site payment. The total VAT amount for all payments is rolled up to the region, protocol, and program levels, and appears in the VAT to Date field. 138", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "To Be Processed", "definition": "The Revert button is disabled for other statuses, and if you select multiple records. Your system administrator can modify the payment status values for which the Revert button is enabled by configuring the LS Clinical Enable Revert On Status user property in Siebel Tools. For more information about this user property, see User Properties for Business Components in Siebel Clinical. A reverted payment activity record is removed from the Payments view and returned to the Payment Activities view. To revert a payment record 1. Navigate to the Site Management screen, then the Protocol Site List view. 2. In the Protocol Site list, drill down on the site number field of the site for which you want to revert a payment. 3. Navigate to the Payments view. 4. Select the payment to revert. 5. Click Revert. The payment records and split payment activities are updated as follows: ◦ The reverted payment activity record is removed from the Payments view and returned to the Payment Activities view for further processing. ◦ If the payment record consists of multiple payment activities, and if you revert only some of the payment activities, then the Earned Amount and Requested Amount fields are recalculated for the Payment record. 139", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Payments", "definition": "You can integrate Siebel Clinical with Oracle Business Intelligence Publisher (BI Publisher) to generate reports. You can generate, view, and schedule preconfigured Oracle BI Publisher reports in Siebel Clinical. The preconfigured Protocol Payments report applies to clinical payments. For more information about using Siebel Reports and integrating with Oracle BI Publisher, see Siebel Reports Guide . To generate an Oracle BI Publisher report for Siebel clinical payments 1. Navigate to the Protocols screen, then the Protocol List view. 2. In the Protocol list, drill down on the protocol number field of the protocol for which you want to generate an Oracle BI Publisher report. 3. Navigate to the Payments view. 4. On the application toolbar, click Reports. 5. In the Run Report pane, complete the appropriate fields as shown in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Administering and Using Clinical Trip Reports", "definition": "6. Click My Reports to navigate to the Reports view of the BI Publisher Reports screen. A record for the report appears in the Reports view. For information about viewing and printing the report, see Siebel Fundamentals Guide . 163", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Reports", "definition": "A third-party application records dates, times, and geographical location details for sites in the trip report for each site monitor visit to each clinical site. Multiple site monitors can create multiple site visit records in the trip report for the same site visit. Each site monitor can create multiple site visit records in the trip report for different times on the same site visit. To view geographical location details for a clinical trip report 1. Navigate to the Site Visits screen, then the Clinical Site Visits List view. 2. In the Clinical Site Visits list, drill down on the Visit Start field of the site visit for which you want to view geographical location details. The Trip Report form for the selected site visit appears. 3. Navigate to the Geo Location Details view. Some fields are described in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Site close-out", "definition": "Preparing Trip Reports The CRA (clinical research associate) is the end user of the Siebel Clinical product. Before visiting a site, the CRA uses the trip report to prepare for the visit. The follow-up items list reminds the CRA of the open activities from previous visits that the CRA can close. After preparing a draft trip report, the CRA makes a hard copy of the report and takes this copy on the site visit. He can use the report as a reference to help keep track of the activities he completes while at the site. After returning from a site visit, the CRA completes the trip report and generates a final report. He then submits this report to the study manager for approval. The manager reviews the report and approves it if it is satisfactory. If the manager approves the trip report, then it is then locked to prevent the CRA from making any further changes. If the trip report is not satisfactory, then the manager can reject the report and return it to the CRA for further attention. 142", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "SmartScript", "definition": "Select a SmartScript questionnaire for the trip report template. The questionnaire is copied to the Questions view of the trip report when you apply the trip report template to the trip report. The questionnaire uses branching logic to dynamically determine the flow of questions by using answers to prior questions. The Siebel Clinical Trial Management System administrator determines the question hierarchy by using Siebel SmartScript. For more information about Siebel SmartScript, see Siebel SmartScript Administration Guide . 3. Drill down on the Name field of the trip report template to display the Trip Report Template Details view. 4. Create new records to define each trip report activity.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Question Setting", "definition": "Questionnaire Exception for Clinical Trip Reports", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Answer Type", "definition": "The Currency data type is not supported.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Auto Sub Parm", "definition": "This setting is not applicable.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Search Spec", "definition": "This setting is not applicable.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Save Business Object", "definition": "This setting is not applicable because the answers to questionnaires for clinical trip reports are automatically saved as part of the clinical trip reports.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Save Bus Comp", "definition": "This setting is not applicable because the answers to questionnaires for clinical trip reports are automatically saved as part of the clinical trip reports.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Save Field", "definition": "This setting is not applicable because the answers to questionnaires for clinical trip reports are automatically saved as part of the clinical trip reports.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Save Currency Field", "definition": "This setting is not applicable because the answers to questionnaires for clinical trip reports are automatically saved as part of the clinical trip reports.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Pick Applet", "definition": "This setting is not supported.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Mvg Applet", "definition": "This setting is not supported.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Detail Applet", "definition": "This setting is not supported.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Save Answer Table", "definition": "This setting is not applicable because the answers to questionnaires for clinical trip reports are automatically saved as part of the clinical trip reports. 143", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Currency", "definition": "This setting is not applicable. You can assign a SmartScript questionnaire to a clinical trip report template after it is released using Siebel SmartScript. For information about how to release a script using Siebel SmartScript, see Siebel SmartScript Administration Guide . Creating Clinical Trip Report Templates Typically, the clinical administrator prepares a number of generic trip report templates, perhaps one designed for each of the different stages in the study. This topic describes how to create a clinical trip report. You can define additional activities, such as follow-up tasks to complete after the visit to the clinical site, in the Trip Report Template Details view. To create a clinical trip report template 1. Navigate to the Administration - Clinical screen, then the Trip Report Templates view. 2. Create a new record and complete the necessary fields as shown in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Priority", "definition": "Select the priority of the trip report activity. Applying Clinical Trip Report Templates The Template field of the Site Visits view displays all of the trip report templates that are applicable to protocol, region, and visit type details for that site visit. When you apply a trip report template to a trip report, all of the details in the template are copied to the trip report. Checklist and follow-up activities in the Trip Report Template Details view of the trip report template are copied to the trip report, and overwrite any existing checklist and follow-up activities in the trip report. SmartScript questionnaires in the SmartScript field of the trip report template are applied to the trip report, and appear in the Questions view. For performance reasons, the SmartScript questionnaire does not appear in the Questions view until after the user launches the SmartScript questionnaire. For more information about SmartScript questionnaires, see Completing Questionnaires for Clinical Trip Reports. To apply a clinical trip report template 1. Navigate to the Site Visits screen, then the Clinical Site Visits List view. 2. In the Clinical Site Visits list, drill down on the Visit Start field of the site visit to which you want to apply a trip report template. The Trip Report form for the selected site visit appears. 3. Click the select button in the Template field. A list of templates that correspond to the protocol, region, and visit type details for the site appears. 4. Select the name of the trip report template that you want to apply, and click OK. When you save the Template field, the activities in the template appear in the Checklist Activities list and Follow-Up Items list. Completing Clinical Trip Reports After the site visit, you record the trip report details, such as: • The planned activities that you complete • Additional activities that you complete 145", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Reviewer Comments", "definition": "Type reviewer comments about the trip report. Only the assigned reviewer can modify this field.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Approver Comments", "definition": "Configure the value as follows: • To enable this property, set the value as follows: \"Rejected\", \"Y\" • To disable this property, set the value as follows: \"Rejected\", \"N\" This property is enabled by default.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Attendees", "definition": "Select the contacts (site personnel) that you meet during the visit.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Reviewer", "definition": "Select the user who reviews the trip report. An automated email notification is sent to the reviewer when you update the status of the trip report to Submitted.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Approver", "definition": "Select the user who approves the trip report. An automated email notification is sent to the approver when the reviewer updates the status of the trip report to Submitted for Approval.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Clinical Trip Reports", "definition": "The Inbox provides a centralized list of items requiring your attention, such as clinical trip reports requiring review, revision, and approval. The following procedure shows you how to access and view Universal Inbox notifications for action items of clinical trip reports. Alternatively, you can click Notification on the application banner to access and view all notification messages, including those for clinical trip reports. Note: The Notifications feature is enabled by default for all Siebel Business Applications so all notification messages for clinical trip reports are sent directly to the screens of users. Messages appear in notification panes that users access by clicking Notification on the application banner. For more information about using, administering and reviewing notifications, see Siebel Fundamentals Guide . 154", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "report", "definition": "1. From the application-level menu, choose Navigate, then Site Map. 2. Click Inbox. The views available for the inbox appear. 3. Click Inbox Items List. The list of notifications for your trip reports appears. The action required for each trip report appears in the Name field. 4. Drill down on the Name field to view each trip report requiring action. Enabling a Trip Report Completed Alert The following procedure shows how to enable an alert pop-up message to automatically appear when you try to complete a trip report (that is, set the Status of the trip report to Completed) without updating at least one of the Open Follow-Up items for the report. Once enabled, the following alert pop-up message appears when you try to complete a trip report without updating the Follow-Up items for the report: Follow-Up items have not been updated for this Trip Report. Are you sure you want to proceed? Click OK to close the alert message and proceed with trip report completion. To enable a trip report completed alert 1. Log in to Siebel Tools. 2. Set the Follow-Up Update Alert user property for the Clinical Trip Report business component to Y (N or Null is the default value) to enable the trip report completed alert. The trip report status that triggers the Follow-Up Update Alert is Completed by default. Follow-Up Update Alert = N Follow-Up Update Alert Status = Completed 3. (Optional) To change the trip report status that triggers the Follow-Up Update Alert, set the Follow-Up Update Alert Status user property for the Clinical Trip Report business component to a different trip report status. For example: Follow-Up Update Alert Status = Revised Reviewing Clinical Trip Reports The user designated in the Reviewer field of the Trip Report form can review trip reports with a status of Submitted. An automated notification email is sent to the reviewer when an end user updates the trip report status to Submitted. Access control applies to the Reviewer Comments field. Only the user designated in the Reviewer field of the Trip Report form can edit the Reviewer Comments field. 155", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Questions Answered", "definition": "Displays the number of answered questions, the total number of SmartScript questions for the trip report, and the SmartScript questionnaire status. The value ranges are as follows: ◦ 0 of 0, Not Assigned. The user has not assigned a SmartScript questionnaire to the trip report. ◦ [Total Answered] of [Total Questions], In Progress. The user launched the SmartScript questionnaire, and it is in progress. ◦ [Total Answered] of [Total Questions], Finished. The user completed answering all of the questions. The drill-down navigates to the Questions view. Current Follow-Ups Completed Displays the number of completed follow-up activities and the total number of follow-up activities for the current trip report. The drill-down navigates to the Current Trip Follow- Up Items view. All follow-ups in the Current Trip Follow-Up Items view with a value in the Completed Date field are listed as completed in the Summary view. 150", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Total Attendees", "definition": "Displays the total number of attendees assigned to the trip report. Tracking Status Accruals for Clinical Subjects of Sites This task describes how to track the progress of subject status accruals for a clinical site by creating a real-time snapshot of the current subject status accruals for the site. The CRA (clinical research associate) can use the snapshot to verify the subject status data that an end user manually records during a site visit. To track status accruals for clinical subjects of a site 1. Navigate to the Site Visits screen, then the Clinical Site Visits List view. 2. In the Clinical Site Visits list, drill down on the Visit Start field of the site visit for which you want to track the status accruals for subjects. The Trip Report form for the selected site visit appears. 3. Navigate to the Summary view. 4. Click Capture in the Subject Status Snapshot applet to generate a real-time snapshot of the status accruals for clinical subjects. Some fields are described in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Checklists Completed", "definition": "Displays the number of completed checklists and the total number of checklists. The drill-down navigates to the Checklist Activities view. All checklists in the Checklist Activities view that have a Status of Done or Completed are listed as completed in the Summary view.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Subject Status", "definition": "Displays the subject status. The following subject visit statuses are preconfigured: 151", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Total Actual Number", "definition": "Type the number of current and past subject status accruals that you manually recorded during the site visit. Current Accrual Number Displays the number of current subject status accruals that are automatically created for the site visit.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Current Actual Number", "definition": "Type the number of current subject status accruals the you manually recorded during the site visit.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Time Stamp", "definition": "Displays the date and time that the snapshot is generated. Automated Validation and Notification Messages for", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Not Started", "definition": "The end user created the trip report, but processing is not yet started. This status is the initial default value for all trip reports.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "In Progress", "definition": "The end user started to work on the trip report, and it is not yet complete. System, if the trip report is created using a template. User, if the trip report is not created using a template. N/A N/A", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "the Completed Date is", "definition": "populated. The Completed Date is a prerequisite for selecting Completed. If enabled, an alert pop- up message will appear if you try to complete a trip report without updating the Follow-Up items for the report. For more information, see Enabling a Trip Report Completed Alert.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Submitted", "definition": "The end user submitted the trip report to the reviewer for review.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "The trip report is", "definition": "validated to ensure that an approver is assigned. The following automated", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "email is sent to the", "definition": "approver: Approve Trip Report [Trip Report Name].", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "If you change the", "definition": "reviewer when the Trip Report Status field is Submitted, then this notification email is sent to the new reviewer.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "added review comments", "definition": "requiring a modification to the trip report.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "User", "definition": "Not applicable. N/A Submitted for Approval The reviewer submitted the trip report for formal review, without comments or with comments that do not require a change to the trip report.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "one of the following", "definition": "fields is completed: The following automated message is sent to the trip report owner: Rectify Trip Report [Trip Report Name]. 153", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "If the you change the", "definition": "approver when the Trip Report Status field is Submitted for Approval, then this notification email is sent to the new approver.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Approved", "definition": "The approver approved the trip report, and it is now read- only. When the approver clicks the Approved button, the Trip Report Status field is updated to Approved.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "User Id", "definition": "Displays the user ID of the site monitor who logs the geographical location details in the third- party clinical application.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Latitude", "definition": "Displays the latitude coordinate of the site. Latitude coordinates are represented in decimal degree format. The range of acceptable values is 0 to plus or minus 90. Northern hemisphere latitudes are represented by a positive number. The number is preceded by a minus sign to represent southern hemisphere latitudes. 159", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Longitude", "definition": "Displays the longitude coordinate of the site. Longitude coordinates are represented in decimal degree format. The range of acceptable values is 0 to plus or minus 180. Eastern hemisphere latitudes are represented by a positive number. The number is preceded by a minus sign to represent western hemisphere longitudes.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Login", "definition": "Displays the login credentials of the user who modified the field.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Managing Clinical Projects", "definition": "2. In the Project list, drill down on the Name field of the project. 3. Navigate to the Costs view. 4. Click a hyperlink in the Clinical Payments or Project Tasks list to see the activities associated with a cost item. Managing Risk for Clinical Projects An important aspect of project management is risk management. The features of the Risks view allow you to enter information about project risks and create and assign resolution activities to address the risks. For more information about assessing risks for project management, see Siebel Project and Resource Management Administration Guide . This task is a step in Process of Managing Clinical Projects. To manage risk for clinical projects 1. Navigate to the Projects screen, then the List view. 2. In the Project list, drill down on the Name field of the project. 3. Navigate to the Risks view. 4. In the Risks list, create a new record and complete the necessary fields. 5. In the Risks list, drill down on the Name field. 6. In the Resolution Activities list, create a new record and complete the necessary fields. About Views in the Projects Screen Many views are available in the Projects screen of the Siebel Clinical. You can choose to use only some of these views. The following table describes the views that are available in the Projects screen.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Administrator Tasks", "definition": "The tasks that an administrator must complete to support projects depends on the project features that the organization uses. You might not have to perform all the tasks listed in this topic. These tasks must occur before the project manager creates the project. The following list shows the tasks administrators typically perform to manage clinical projects: • Creating Activity Templates for Clinical Projects. Many project managers use these templates to carry out similar clinical trials. • Setting Up Employee Profiles for Clinical Projects. Maintain the employee profiles of skills and competencies that Siebel Assignment Manager uses. 166", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Project Resource", "definition": "Select this field to indicate that the product is a project resource.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Creating Rate Lists", "definition": "Complete the procedure in this topic to create a rate list.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "To create a rate list", "definition": "1. Navigate to the Administration - Pricing screen, then the Rate List view. 2. In the Rate Lists list, create a new record and complete the necessary fields. 3. In the Rate Lists list, drill down on the Rate List field. 4. In the Rate List Line Items list, create a new record. 5. In the Add Position Types dialog box, select the Position Type and click OK. This list of resources is created as a product. 6. In the Rate List Line Items list, complete the remaining fields. Creating Clinical Projects You can create a project record and associate a protocol with the project. 168", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Project ID", "definition": "Type a unique identification number for the project.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Start", "definition": "Select the start date and time for the project.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "End", "definition": "Select the end date and time for the project. Protocol # Select the protocol for the project. All available protocols are available for selection from the Pick Protocol dialog box. The project creator does not have to be a member of the protocol team.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Actual Cost", "definition": "Displays the actual cost of the project. This field is calculated by summing the actual costs of all the tasks, activities, and site payments associated with the project.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Revenue", "definition": "Type the total revenue for the project. Click the currency calculator button for this field to enter the amount of revenue, the currency, and the exchange date for the currency.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Budgeted Cost", "definition": "Displays the budgeted cost of the project. This field is calculated by summing the budgeted costs of all the tasks, activities, and site payments associated with the project. 169", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Rate List", "definition": "Select the rate list for the project if a rate list is set up for the project team members. Click the show more button if this field is not visible. For more information, see Setting Up Position Types and Rate Lists for Billing.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Access", "definition": "Use this view to provide project access. Add the names of the project team members and also managers or executives who want access to monitor the progress of the project. The Access view has a similar function to the Team field in other screens.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Activity Plans", "definition": "Select Yes if you want to create activity plans for this site, otherwise select No.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Attachments", "definition": "Use this view to attach project documents. For general information about attachments, see Siebel Fundamentals .", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Calendar", "definition": "Use this view to manage a monthly calendar of the activities associated with the project. Activities belonging to tasks and standalone activities appear in this view. For general information about the calendar views, see Siebel Fundamentals . 173", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Financial Profile", "definition": "Use this view to gain an overall perspective of a project’s financial information, status, and progress. You can change the Delivery status for the project. For more information, see Siebel Project and Resource Management Administration Guide .", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Invoices", "definition": "Use this view to create invoices for time and expenses that apply to a project. For more information, see Siebel Project and Resource Management Administration Guide .", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Notes", "definition": "Use this view to keep private and public notes about the project. For general information, see Siebel Fundamentals .", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Orders", "definition": "Use this view to create a product or material order and associate it with the project. For more information, see Siebel Project and Resource Management Administration Guide . Organizational Analysis Use this view to see an organizational chart of contacts that shows the relationships between them.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Partners", "definition": "Use this view to maintain a list of partner accounts associated with the project. You can keep a list of accounts associated with the project, such as vendors who handle printing of the clinical trial materials or shipping of sample drugs. Because the views for Partners, Subcontractors, and Clinical Contacts contain account information, you can use one or more of these views to keep track of accounts associated with a project.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Risks", "definition": "Use this view to maintain a list of the risks associated with the project and resolution activities required to address those risks.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Status Report", "definition": "Use this view to create a status report summarizing the project’s progress, forecast, and issues. For more information, see Siebel Project and Resource Management Administration Guide .", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Subcontractors", "definition": "Use this view to keep a list of subcontractors associated with the project. For more information, see the description of the Partners field.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Tasks", "definition": "Use this view to create and modify tasks for the project.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Team Workbook", "definition": "Use this view to assign team members to roles in the project. You can manually assign team members, or Siebel Assignment Manager can automatically assign them. Team members must be listed in the workbook before you can assign them to activities. Time & Expense Use this view to adjust and summarize time sheets and expense reports associated with the project. For more information about time sheets and expense reporting, see Siebel Project and Resource Management Administration Guide . 174", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Managing Clinical Training", "definition": "To view the training information for a clinical region 1. Navigate to the Regions screen, then the Region List view. 2. In the Region list, drill down on the Region field of the region for which you want to view training information. 3. Navigate to the Training view. 4. Click Refresh to update the view with the latest information about completed training. 5. Review the training information in the training overview applet. Some fields are described in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Mandatory", "definition": "Select this field to indicate that completing the training topic is mandatory.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Duration Unit", "definition": "Select the units of time that apply to the numeric value that you enter in the Duration field.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Created Date", "definition": "Displays the date and time that you create the version of the training plan. 180", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Obsolete Date", "definition": "Select the date and time that the training plan is inactive. After you populate this field and then save the plan record, you cannot change the field values for the training plan, the criteria for the training plan, or the versions for the training plan. You cannot delete training plans. If you want to indicate that the training plan is again active, then clear this field. After the training plan is again active, you can change it. Sites Processed/ Total Sites Displays the number of sites that the publishing process associated with the training plan (sites processed) compared to the number of sites that apply to the training plan (total sites). This field is automatically populated when you publish the training plan. If you successfully publish the training plan, then the number of processed sites equals the number of total sites. If you fail to successfully publish the training plan, then the number of processed sites is not equal to the number of total sites. If you create a new approved version of the training plan, then this field value is cleared. % Completed Displays the fraction in the Sites Processed/Total Sites field as a percentage. This field is automatically populated when you publish the training plan. If you successfully publish the training plan, then this field value is 100%. If you fail to successfully publish the training plan, then this field value is less than 100%. If you create a new approved version of the training plan, then this field value is cleared.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Version Number", "definition": "Displays an automatically generated version number. The first version record that you create is automatically populated with a version number of 1, the second version record that you create is automatically populated with a version number of 2, and so on.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Process Status", "definition": "Displays the status of the publishing process for the training plan as follows: ◦ When you create a new record for a training plan, this field value defaults to Not Started. ◦ When you click the Publish button to publish the training plan, this field value changes from Not Started to Publishing. ◦ After publishing is complete, and publishing is unsuccessful, this field value changes from Publishing to Failed. ◦ After publishing is complete, and publishing is successful, this field value changes from Publishing to Published. ◦ When you select a value of Approved in the Status field for a new version (but not the first version) of the training plan, and then save the version record, this field value changes from Published to Not Started.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Publish Result", "definition": "Displays the final result of the process to publish the training plan. If you create a new approved version of the training plan, then this field value is cleared. 178", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Scope", "definition": "Select the scope of the training plan as follows: ◦ If you select a value of All, then the training plan applies to all sites in Siebel Clinical, and you cannot enter values in the other fields of the Training Plan Criteria list. You cannot create more criteria records for the training plan. ◦ If you select a value of Specific, then the training plan applies to specific sites in Siebel Clinical, and you designate these sites in the other fields of the Training Plan Criteria list. You can create more criteria records for the training plan.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Indication", "definition": "Select the clinical indication that applies to the training plan.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Trial Phase", "definition": "Select the trial phase that applies to the training plan.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Site Status", "definition": "Select the site status that applies to the training plan. Protocol # Select the protocol number that applies to the training plan. The values that you select for the indication and trial phase determine the values that are available to you for selection in this field. If you select a protocol number and subsequently select a value for an indication or a trial phase that is not associated with that protocol number, then the criteria record that you create has no effect because no protocols meet the criteria.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Published Date", "definition": "Displays the date and time that you publish the version of the training plan.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Archived Date", "definition": "Displays the date and time that you archive the version of the training plan. 5. Complete the following steps to select the training topics for the version: a. In the Training Topics list, click Add Topics. b. In the Training Topics dialog box that appears, select the training topics for the version. To select multiple topics, hold down the CTRL key and click each topic record. Topics that have a value in the Obsolete Date field are not available for selection. For information about setting up the training topics that appear in this dialog box, see Setting Up Training Topics for Clinical Training. c. Click OK. The selected topics appear in the Training Topics list. You cannot change the field values in these selected topic records. However, you can delete the topic for which you want to change the field values, change the field values for the topic in the Training Topics view of the Administration - Clinical screen (if the topic is not associated with another published training plan), and then add the changed topic to the version again. About Publishing Training Plans After administrators create training plans, they publish the training plans so that the training topics in those plans are automatically associated with the appropriate contacts for the appropriate sites. The Role field value for the training topics in the plans determines the appropriate contacts. The plan criteria determines the appropriate sites. The publishing process for a training plan is a batch job that runs in the background. Consequently, users are not prevented from using other functionality in Siebel Clinical while this batch job runs. To optimally run this batch job, administrators can set the Clinical_Training_Commit_Freq system preference. For more information about this system preference, see System Preferences in Siebel Clinical. The duration of the publishing process for a training plan is determined by the following factors: • The number of training topics in the plan • The number of site records to associate with the training topics in the plan • The number of contact records to associate with the training topics in the plan When a user creates a site record after an administrator publishes a training plan that applies to that site, the topics in that plan are automatically associated with the new site. Likewise, the topics in that plan are automatically associated with the contacts for the new site if the Role field in the topics is blank or a Role field value in the topics is the same as the Role field value in the contact records. If the administrator populates the Obsolete Date field (in the Training Topics view of the Administration - Clinical screen) for some topics in a plan after the plan publication, then those topics are not automatically associated with a new site even though the topics exist in the training plan. When a user creates a new contact record for an existing site after an administrator publishes a training plan that applies to that site, the topics in that plan are automatically associated with the new contact if the Role field in the topics is blank or a Role field value in the topics is the same as the Role field value in the new contact record. If the 181", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Total Trainings", "definition": "Displays the total number of training topics for the region. Each of these topics appears in the training details applet.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Trainings Completed", "definition": "Displays the number of training topics that all associated contacts completed for all the sites in the region.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Sites Completed", "definition": "Displays the number of sites in the region for which all associated contacts completed the training topic.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Topic Name", "definition": "Displays the training topic name for the region. If a topic associated with a site record for the region does not apply to any contacts for the region, then that topic does not appear in the training details applet.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Sites Not Completed", "definition": "Displays the number of sites in the region for which all associated contacts have not completed the training topic. 187", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Siebel Clinical Task-Based UI", "definition": "b. Click Transfer. c. In the Transfer To dialog that appears, select the user to whom you want to transfer the task. d. Enter a comment in the Comments box to notify the user and then click OK. 5. To delete a task: a. Select a task in the Inbox Items List. b. Click Delete Task. 201", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Creating Protocols", "definition": "The following procedure shows how to create a protocol using Siebel Clinical task-based UI.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Creating Sites", "definition": "The following procedure shows how to create a site using Siebel Clinical task-based UI.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Clinical Tasks", "definition": "By default, Siebel Clinical administrators can access all the predefined task-based UI clinical tasks. To assign another responsibility to one or all of the predefined clinical tasks, then complete the steps in the following procedure. 190", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "To create a protocol", "definition": "1. Click the Tasks icon on the application taskbar to open the Task Pane applet for Siebel Clinical. 2. Click Create Protocol in the Task Pane applet. 3. Enter protocol data on the page that appears - the fields are described in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Central Lap", "definition": "Select the Account name to assign the associated central lab.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "To create a site", "definition": "1. Click the Tasks icon on the application taskbar to open the Task Pane applet for Siebel Clinical. 2. Click Create Site in the Task Pane applet. 3. Enter site data on the page that appears - the fields are described in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Site Contacts", "definition": "Select Yes if you want to create contacts for this site, otherwise select No.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "RACT Assessment", "definition": "Select Yes if you want to create risk assessments for this site, otherwise select No. 4. If required, click Pause at any time to pause the task. The task moves to your Inbox where you must go when you want to resume work on the task. 5. If required, click Cancel at any time to cancel the task. 6. Click Submit to do one of the following: a. Submit the task - that is, create the site. 194", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "First Name", "definition": "The last name of the contact.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "City", "definition": "The city where the contact is located. 195", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "State", "definition": "The state where the contact is located.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Postal Code", "definition": "The postal code for the contact's address.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Country", "definition": "The country where the contact is located.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Main Phone", "definition": "The phone number for the contact.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "E-Mail", "definition": "The email address for the contact. 4. If required, click the plus (+) icon to create additional site contacts. 5. If required, click Pause at any time to pause the task. The task moves to your Inbox where you must go when you want to resume work on the task. 6. If required, click Cancel at any time to cancel the task. 7. Click the check box next to each contact that you want to associate with the site and then click Submit to submit the task - that is, create the site contact(s). Creating Activity Plans for Sites The following procedure shows how to create activity plans for a site using Siebel Clinical task-based UI. To create an activity plan for a site 1. Click the Tasks icon on the application taskbar to open the Task Pane applet for Siebel Clinical. 2. Complete one of the following to create an activity plan for a new or an existing site. a. To create an activity plan for a new site: - Click Create Site in the Task Pane applet. - Enter site data on the page that appears - all fields are described in Creating Sites. - Select Yes for Activity Plans and then click Submit. b. To create an activity plans for an existing site: - Click Other Site Tasks in the Task Pane applet. - Select the record that you want in the Sites list and then click Submit. - Select Yes for Activity Plans and then click Submit. 196", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Supporting Blinded and Unblinded Users for Clinical Trials", "definition": "This topic describes the inheritance hierarchy for blinded and unblinded users. Sites, Regions, and Protocols are never blinded or unblinded in Siebel Clinical. It is the users at the different (site, region, or protocol) levels that are either blinded or unblinded. For example, if a user is blinded at site and protocol level but unblinded at the region level, then that user will automatically have blinded access for all site level work like site visits and activities. However, rolling up or down the configuration will change the type of user access accordingly - see the following tables for more information. Precedence is given to configuration of the Unblinded field for users at site level. If the Unblinded field for a user is updated at site level, then position roll down from protocol and/or region cannot override the Unblinded field definition at site level, since site has more precedence.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Users", "definition": "Access control is the term used to describe the set of Siebel application mechanisms that control user access to data and application functionality. Controlling access to data for blinded and unblinded users involves the following tasks: • Controlling Access to Blinded Data for Unblinded Users • Setting Blinded and Unblinded User Access at Protocol Level • Setting Blinded and Unblinded User Access at Region Level • Setting Blinded and Unblinded User Access at Site Level • Setting Access to Unblinded Activities in Core Activities View and Contact Activities View Controlling Access to Blinded Data for Unblinded Users Unblinded users by default see both blinded data and unblinded data, where blinded data is displayed in read-only mode. However, administrators can modify this default behaviour by changing the protocol level setting Hide blinded content for unblinded users. Access to blinded data by unblinded users is controlled by the protocol level setting Hide blinded content for unblinded users. This setting is deselected (set to N) by default in Siebel Clinical, can be changed only by the Siebel Clinical administrator, and is configured at the protocol level. To configure access to blinded data for unblinded users 1. Navigate to the Administration - Clinical screen, then the Protocol List view. 2. In the Protocol list, drill down on a protocol number field. 204", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "unblinded users", "definition": "Y (Selected) Indicates that unblinded users cannot view blinded data, such as Site Visits and Activities. Setting Blinded and Unblinded User Access at Protocol Level The following procedure shows how administrators configure blinded or unblinded user access for protocol team members. To set blinded or unblinded user access at protocol level 1. Navigate to the Administration - Clinical screen, then the Protocol List view. 2. In the Protocol list, drill down on the protocol (number field) for which you want to configure the blinded or unblinded user access. 3. Click the multiple select button in the Team field. A multiple selection dialog box appears showing a list of the available and selected protocol team members (users). 4. For each user in the Selected list that should have unblinded protocol level access, configure the Unblinded field as shown in the following table.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Unblinded", "definition": "Yes, to Site level for the selected region.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "views", "definition": "1. Log in to Siebel Tools. 2. Go to Business Component and query for the name Action. 3. Go to the Business Component User Properties for the Action business component. 4. Query for the name Unblinded Activity Responsibility. 5. Update the Value field for Unblinded Activity Responsibility by adding the required user roles (or responsibilities) to it. These user roles will be able to view unblinded data in Siebel Clinical core Activities and Contact Activities views. Siebel Administrator is mapped by default to the Unblinded Activity Responsibility business component user property. 207", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Position Rollup", "definition": "The position roll up hierarchy goes from Site, to Region, and then to Protocol level. • When you click Position Rollup at site level, all changes (in the Unblinded field for users at site level) will be rolled up to the region and protocol levels. • When you click Position Rollup at region level, all changes (in the Unblinded field for users at region level) will be rolled up to the protocol level. The following table shows a user's Unblinded field value after performing a Position Rollup.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Level", "definition": "User's Unblinded Field Value Position Rolldown? Unblinded Field Value After Position Rolldown", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Blinded", "definition": "Note: If Position Rolldown is performed at Region level, it does not apply since precedence is always given to the value at site level. Blinded and Unblinded Support in Siebel Mobile Disconnected Applications In Siebel Mobile disconnected applications: • Blinded users can view and access blinded site visits. • Unblinded site visits are not available. • Unblinded users can view blinded records in read-only mode irrespective of the protocol level setting Hide blinded content for unblinded users. Blinded and Unblinded Customization Support You can add new custom child applets under Site and Site visits and, if required, enable the blinding or unblinding functionality for those applets as follows: • For the business component in question, create a new Enable Unblinding user property with the value Y. • If your business component is based on the Event Activity table, then use the CSSBCClinicalGenericActivity class for your business component. Otherwise use the CSSBCClinicalGenericBusComp class for your business component. • List Applet must use the following class: CSSSWEClinicalListBase. • Form Applet must use the following class: CSSSWEClinicalFormBase. • The Business Component field must contain the following field defined on column: UNBLINDED_FLG. 213", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Position Rolldown", "definition": "The position roll down hierarchy goes from Protocol, to Region, and then to Site level. • When you click Position Rolldown at protocol level, all changes will be rolled down to all the regions and sites for the protocol. • When you click Position Rolldown at region level, all changes will be rolled down to all the sites for the region. The following table shows a user's Unblinded field value after performing a Position Rolldown.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Setting Up and Configuring Clinical Data Capture and", "definition": "Query Management System Integration 220", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "CDMS Study ID", "definition": "Type the ID of the CDMS study. This required field links a clinical protocol in Siebel Clinical to a clinical study in an external application. For integration with Oracle Health Sciences InForm, set the value to the trial name in Oracle Health Sciences InForm. Synchronize Active Study Sites This field is not applicable for InForm integration.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Safety Study ID", "definition": "This field is not applicable for InForm integration.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Plan Study ID", "definition": "This field is not applicable for InForm integration. 217", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "System Integration", "definition": "• Screen Date. The customer decides which date from Oracle Health Sciences InForm to map to Screen Date in Siebel Clinical, and configures the mapping accordingly. • Informed Consent Date. Informed Consent Date is required only if a new Subject Visit Template is being applied. If Siebel Clinical does not have an informed consent date, then an Effective Date (for the Subject View Template) is sent instead of an informed consent date. About Integrating Data for Activity Completion Oracle Health Sciences InForm controls the integration of activity completion data between Siebel Clinical and Oracle Health Sciences InForm. The customer defines in the Oracle Health Sciences InForm trial what data must be collected to determine that a visit or activity in Siebel Clinical is complete. When those conditions are met, Siebel Clinical receives a message. The visit or activity is updated with the status of Complete, and the completion date is populated. If the message from Oracle Health Sciences InForm does not contain a completion date, and the visit or activity in Siebel Clinical already has a status of Complete, then no change is made to the completion date or status in Siebel Clinical. Oracle Health Sciences InForm and Siebel Clinical integrate activity completion data as follows: • Siebel Clinical searches for the subject using the unique subject identifier (row ID). When the subject is found, it searches for the activity as follows: ◦ Siebel Clinical searches for the activity using the clinical item for the visit and the clinical item for the visit activity as follows: - If the clinical item in the update corresponds to a subject visit, then the completed date for that visit is updated. - If the clinical item in the update corresponds to an activity for a subject visit, then the completed date for that activity is updated. ◦ If the clinical item sent from Oracle Health Sciences InForm cannot be mapped to an activity completion item in Siebel Clinical, then an error is generated to indicate that the update failed. 225", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Setting Up and Using the Siebel Mobile Disconnected", "definition": "Application for Siebel Clinical Viewing Site Visits Information Complete the following procedure to view the visits associated with a site. To view site visit information 1. Tap Side Menu on the application banner, and then tap My Site Visits to display the following: ◦ The My Sites list in the side pane. ◦ The site details in the main pane. 2. Tap a record in the My Sites list. All the site details for the selected record appear. 3. Tap the down arrow next to Site Visits. All the visits associated with the selected site appear. 240", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Component", "definition": "eClinical Mobile Data Extraction (ENU) 228", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "ENU", "definition": "OM - Configuration File eclinicalm.cfg Application Splashtext Siebel Clinical Mobile", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Application Title", "definition": "Siebel Clinical Mobile c. Click Activate to activate the new eClinical Mobile Data Extraction (ENU) component. d. Click Synchronize to synchronize the eClinical Mobile Data Extraction (ENU) component 3. Navigate to the Synchronize view, and click Synchronize. 4. Navigate to the Component Definition view, and do the following: a. Select the eClinicalMObjMgr_enu component and set the following parameters for the component:", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "MobileSync", "definition": "c. For each of these component groups: - Select the component group and then click Enable. - In the Component Group Assignments applet, click Enable. 5. Restart Siebel Services and Siebel Gateway. 6. Navigate to the Administration - Server Management screen, then the Jobs view and do the following: a. Create a new job with the parameter values shown in the following table:", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Job Parameter", "definition": "Value: -MOBILE b. Submit the job. The status of the job changes to Success when the database extraction job is successful. One-Time Activity for Customers with existing Site Visit Records Note: Check if you meet the following three criteria: 1. You are upgrading to the 24.4 release 2. Planning to use the Mobile Disconnected Application 3. Have existing Site Visit records Then, there is a mandatory one-time activity required to update the Page_id on existing Site Visits In order to use the mobile app. To update the existing Site Visit records 1. Log in as an Admin user to Siebel Clinical. 2. Navigate to Administration > Server Management. a. In the Job list, click New 230", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Answering a Question", "definition": "• Click to enter or update a response to the question. • Free text comments can be entered in the Comments field, if needed. ◦", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Saving a response", "definition": "• Answers are auto-saved by the app when a user performs any of the following actions: - Navigating to the next question - Navigate to the previous question - Jump to another question in Navigation Tree - Click on the Finish button Note: Clicking only on the Cancel button will result in potential unsaved data changes be lost and user to be exited from the SmartScript player. ◦ Validation on mandatory questions • For mandatory questions, a pop-up message will be presented if you do the following: - Navigate (next, previous, or jumping) away from a mandatory question that has not been answered. - Click on Finish to complete the trip report and exit the SmartScript Player. ◦ Completing questions and exiting the SmartScript Player • A user can exit the Smartscipt player with the following choices: - Click on Finish to save and exit the SmartScript player. Finish will trigger validation to ensure that all mandatory fields have been completed across questions in the Site Visit before saving and exiting. - Click on Finish Later to save any unsaved changes and exit the SmartScript player. Finish Later will not trigger validation of mandatory questions. - Click on Cancel to exit the SmartScript player. Note: Any unsaved changes will be lost. Managing My Sites for Siebel Clinical A site is an account that a principal investigator manages for a particular protocol. The following procedures related to site management are included in this topic: • Displaying My Sites Information • Viewing Site Contacts Information • Viewing Site Visits Information 238", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Setting Up Integration Between CTMS and eTMF", "definition": "This task is a step in Process of Setting Up Integration Between CTMS and eTMF. To manually generate a trip report file 1. Navigate to the Site Visits screen, then the Clinical Site Visits List view. 2. Query for the approved site visit that you want (where the trip report file failed to generate). 3. Click Generate Report. A trip report file is generated with a status of Approved and it is automatically added to the Site Visit Attachment", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "TR File Type", "definition": "The default file naming convention for the trip report file is as follows: ◦ TR File Name: ___Row ID ◦ TR File Type: PDF <-- Default Value --> TR File Name = ___Row ID Configuring Email Recipients When an approved trip report file is automatically added to the Site Visit Attachment view, an email is sent to key CRA and eTMF recipients notifying them that an approved trip report file is ready to send to the eTMF system. The following procedure shows you how to configure the email recipients. This task is a step in Process of Setting Up Integration Between CTMS and eTMF. To configure email recipients 1. Navigate to the Administration - Application screen, then the System Preferences view. 243", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "SMTP Server Port", "definition": " Configuring the LS Clinical Trip Report File Web Service The eTMF system calls the LS Clinical Trip Report File Transfer Web service (an inbound SOAP Web service provided by CTMS) to pull the trip report file into the system and in turn send the trip report received date and URL link from eTMF back to CTMS. To pull a specific trip report or a group of trip reports from CTMS into the eTMF system, then configure one or more of the input parameters, as listed in the following procedure, in the LS Clinical Trip Report File Transfer Web service. This task is a step in Process of Setting Up Integration Between CTMS and eTMF. 244", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Trip Report ID", "definition": "The ID associated with the trip report.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Approved Date", "definition": "The date the trip report was approved. Manually Generating Trip Report Files If for some reason a trip report file is not generated automatically once a site visit is approved (this is usually due to BI Publisher not working), then a notification is sent to the concerned team members notifying them of the situation and the Generate Report button on the Site Visits screen is activated. You configure the team members to notify (upon failure to generate a trip report file once a site visit is approved) in the BC user property BIP Error Notification List. The values you can assign to BIP Error Notification List are Approver, Reviewer, Primary, and Team. The default value is Primary and a maximum of two values can be configured. For example: Approver, Reviewer. 245", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "One", "definition": "Configuring Enterprise Level Parameter Add the following enterprise parameter: 1. Navigate to Administration->Server Configuration->Enterprises->Parameter. 2. Search for Enterprise Parameter Name = 'Business Service Query Access List'. 3. Append the following value for this parameter: Workflow Process Manager,LS Clinical Subjects Rest Service If a value already exists, append the new value after the comma and without a space. 4. Restart the Siebel environment. Disabling User Properties If you are using this integration, you need to disable the following user properties to avoid deletion of future visits when subject is screen failed or early terminated: BC: Clinical Subject Status User Property to Inactivate: • LS Subject Terminate Study Status Value 1 • LS Subject Terminate Study Status Value 2 Enabling Auto Apply SVT A new button is added in 'Site Management->Protocol Site->Clinical Subject' to apply the new/updated subject template to all subjects in one action. The batch apply feature is a new option in addition to the ability to apply the subject template to one subject at a time. Out-of-the-box, the functionality of Apply SVT is available with the 'Siebel Administrator' Responsibility. If this functionality is required for any additional responsibility, it can be provided in the 'CL - Apply SVT Access' System Preference as a comma separated list. 250", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "EDC Item Library", "definition": "The “LS Clinical EDC Item Library View” is available under Administration - Clinical -> Visit Templates. When a new SVT is inserted using APIs in Siebel Clinical, as part of the process the unique Activities (identified by Clinical Item + Form Id) are inserted under the “EDC Item Mapping” view from each Template Version.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Refresh Activity", "definition": "This button is available at Administration Clinical->Visit Templates->Template Versions. 247", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Setting Up Integration Logging", "definition": "Configuring Email Notifications You can configure the Email notifications to be notified in case of errors in log and log purging. Once the Study is successfully enabled for logging, do the following: 1. Check (enable) the Notify flag for the respective integrated system on the Integration Systems List applet. 2. Enter the Email address to which the notifications are required to be sent. Note: Multiple Email addresses can be entered separated by semicolon (;). Configuring the Attributes You can add the following preferences/attributes: Integration Log Email Profile- You can create your own Email profile, see section Creating a Communication Profile. 1. Click + button on the Attributes List applet. 2. Enter the Name of the profile and enter the Value. Integration Log Email Template Failure- You can create an Email template for all log failure notifications. 1. Create an Email template by navigating to Administration - Communications > Templates. 2. After successful creation of the template, click on the+ button on the Attributes List applet. 3. Enter the Name and value of the template. Integration Log Email Template Success- You can create an Email template for all the successful log notifications. 1. Create an Email template by navigating to Administration - Communications > Templates. 2. After successful creation of the template, click on the+ button on the Attributes List applet. 3. Enter the Name and value of the template. Configuring the Logs Purge Process You can configure the number of days for which the logs are required to be retained by the CTMS system after which they will be purged. Enter a value in days. For example, if 10 is entered under the Value, all the logs created from current system date shall be purged after 10 days from the system date. 1. Navigate to Administration - Clinical > Integration Administration. 2. From the drop down, select Integration Systems. 3. Under the Attributes for the Purge Log Cutoff Days, under value add a number in days in order the system to wait for the given number of days and then to purge the logs. 252", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Developer’s Reference for Siebel Clinical", "definition": "Request Message: ClinicalTripReportFileTransferQueryPage The following table describes the ClinicalTripReportFileTransferQueryPage request message. SWILSClinicalTripReportFileTransfer Operations The following table lists the operations associated with the SWILSClinicalTripReportFileTransfer Web service.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Date VBC", "definition": "This property defines the workflow process that is invoked when the Enroll Screen Rescreen Through WorkFlow property is set to Y. The default value is LS Clinical - SubjectVisits Process. This workflow process applies the approved subject visit template to the subject. If the subject visit template that is applied is different from the previous version of the subject visit template that was applied, and if the Disable Delete Non App Visit property is set to N, then a pop-up message appears to confirm if incomplete visits in the previous template version must be deleted, and if complete visits in the new template version must be deleted. Clicking OK in the pop-up message deletes the non applicable subject visits. Clicking Cancel retains the non applicable subject visits. User Properties for Business Services in Siebel Clinical The following table describes the business service user properties that you can use to enable and configure functionality for Siebel Clinical.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Visit Plan", "definition": "This property configures automatic status tracking. The value is set to Missed. 259", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Clinical Trip Report", "definition": "LS Clinical VAT Amount Rollup", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Date RollUp", "definition": "Fields:Protocol n Clinical Protocol Site This property configures automatic rollup of the Last Subject Off Study date from the site record to the protocol record. The value takes the following parameters: \"\", \"\", \"\" The value is set as follows: \"Last Subject Off Study Date\", \"Last Subject Off StudyDate\", \"(DESCENDING)\" Note: Do not change this value. Date RollUp Fields:Region", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Clinical Protocol Site", "definition": "This property configures automatic rollup of the Last Subject Off Study date from the site record to the region record. The value takes the following parameters: \"\", \"\", \"\" The value is set as follows: \"Last Subject Off Study Date\", \"Last Subject Off Study Date\", \"(DESCENDING)\" Note: Do not change this value. Delete NonApp WorkFlow", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Clinical Subject", "definition": "LS Clinical State Validation Third-party payments application integration LS Clinical Total Contract Amount Rollup Clinical operations integration LS Clinical Training Implementation", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "WorkFlow Process Name", "definition": "• Apply Templates WorkFlow Process Name • Delete NonApp WorkFlow Process Name Last Subject Off Study", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Date Rollup Status n", "definition": "Clinical Protocol Site This property identifies the qualified subject statuses that are used to populate the date in the Last Subject Off Study field. By default the following statuses are set: •", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Clinical Payments", "definition": "LS Clinical Payments Outbound Third-party payments application integration 279", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Study Status Value 1", "definition": "Clinical Subject Status This property turns on or off the deletion of incomplete future visits for a subject. These incomplete visits are visits for which the date associated with the subject status has a value, the Completed field for the visit is not selected, and the date in the Date field of the visit is later than the date associated with the subject status. When enabled, the incomplete future visits are deleted if the status of the subject is a value in this property. By default, this property includes the following values: •", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Named Method 1", "definition": "Action (No Owner Lock) This property generates payment records. The value string is set as follows: \"GenerateNewPayment\", \"INVOKESVC\", \"Action(No Owner Lock)\", \"LS SubjectVisits Service\", \"GeneratePayment\", \"'Site Id'\", \"[Protocol Site Id]\", \"'srcBusComp'\", \"'Action (No Owner Lock)'\", \"'srcBusObj'\", \"'Clinical Protocol Site'\", \"'tgtBusObj'\", \"'Clinical Payments'\", \"'tgtBusComp'\", \"'Clinical Payments'\" Note: Do not change this value.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Status Snapshot", "definition": "This property generates the subject status snapshot for a clinical site. The value string is set as follows: \"SiteSnap\", \"INVOKESVC\", \"LS Clinical Subject Status Snapshot\", \"LS Clinical Trip Report Svc\", \"GetSiteSnapshot\", '\"SVId\"', \"ParentFieldValue('Id')\" Note: Do not change this value.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "for Popup", "definition": "This property associates accounts, activities, and documents for clinical protocols and clinical regions with sites. It specifies the method (for example, ProtocolAccountRolldownToSite) for the appropriate popup applet, the underlying business component (Clinical Protocol Site for Popup) for that applet, the method (ApplyRolldown) in that business component that calls the rolldown business service (LS Clinical Record Rolldown Service), and the appropriate user property (for example, Protocol Account To Site Rolldown) as the input argument for the business service. To associate accounts for clinical protocols with sites, set the value string as follows: \"ProtocolAccountRolldownToSite\", \"INVOKESVC\", \"Clinical Protocol Site for Popup\", \"LS Clinical Record Rolldown Service\", \"ApplyRolldown\", \"UserPropertyName\", \"Protocol Account To Site Rolldown\" To associate accounts for clinical regions with sites, set the value string as follows: \"RegionAccountRolldownToSite\", \"INVOKESVC\", \"Clinical Protocol Site for Popup\", \"LS Clinical Record Rolldown Service\", \"ApplyRolldown\", \"UserPropertyName\", \"Region Account To Site Rolldown\" To associate activities for clinical protocols with sites, set the value string as follows: \"ProtocolActivityRolldownToSite\", \"INVOKESVC\", \"Clinical Protocol Site for Popup\", \"LS Clinical Record Rolldown Service\", \"ApplyRolldown\", \"UserPropertyName\", \"Protocol Activity To Site Rolldown\" To associate activities for clinical regions with sites, set the value string as follows: \"RegionActivityRolldownToSite\", \"INVOKESVC\", \"Clinical Protocol Site for Popup\", \"LS Clinical Record Rolldown Service\", \"ApplyRolldown\", \"UserPropertyName\", \"Region Activity To Site Rolldown\" To associate documents for clinical protocols with sites, set the value string as follows: \"ProtocolDocumentRolldownToSite\", \"INVOKESVC\", \"Clinical Protocol Site for Popup\", \"LS Clinical Record Rolldown Service\", \"ApplyRolldown\", \"UserPropertyName\", \"Protocol Document To Site Rolldown\" To associate documents for clinical regions with sites, set the value string as follows: \"RegionDocumentRolldownToSite\", \"INVOKESVC\", \"Clinical Protocol Site for Popup\", \"LS Clinical Record Rolldown Service\", \"ApplyRolldown\", \"UserPropertyName\", \"Region 261", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Status Field RollUp n", "definition": "Clinical Subject Status This property collates subject numbers for each status value. You can also configure it to collate subjects status and visit type value pairs. The subject accruals data is rolled up to the site record. The following comma-delimited parameter configurations are supported: • \"[Subject Status]\", \"[Business Component Field Name]\", \"[Visit Type]\" • \"[Subject Status]\", \"[Business Component Field Name]\", \"[Null]\" • \"[Subject Status]\", \"[Business Component Field Name]\" The Subject Status and Business Component Field Name parameters are mandatory. The Visit Type parameter is optional. The Visit Type value is not populated by default. The following example provides the default configuration for the Enrolled status, and collates the subjects with an Enrolled status for automatic rollup to the site record: \"Enrolled\", \"# Enrolled\"", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Status RollUp", "definition": "Fields:Region n Clinical Protocol Site This property collates subject numbers for each status value. You can also configure it to collate subject status and visit type value pairs. The subject accruals data is rolled up to the region record. The following comma-delimited parameter configurations are supported: • \"[Subject Status]\", \"[Business Component Field Name]\" • \"[Subject Status]\", \"[Business Component Field Name]\", \"[Visit Type]\" • \"[Subject Status]\", \"[Business Component Field Name]\", \"[Null]\" 262", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Enhancement", "definition": "This property configures rollup of subject status data to sites, regions, and protocols. Configure the value as follows: • To enable this property, set the value to Y. • To disable this property, set the value to N. This property is enabled by default. Protocol Account To Site", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "LS Clinical Admin", "definition": "This property associates documents for clinical regions with sites. It specifies the data map information in the form of an input argument for the business service method. The named method property for the underlying business component calls this business service when you click the OK button in the LS Clinical Region Document Site Popup applet. This applet appears when you click the Apply To Sites button in the Document Tracking view of the Region screen. The default value for this property follows: 'LS Clinical Region Document To Sites', 'LS Document Tracking', 'Id', 'Clinical Protocol Site for Popup', 'Id' In this value, LS Clinical Region Document To Sites is a data map that maps the fields in documents for regions to the fields in documents for sites, LS Document Tracking is the name of the source business component, and Clinical Protocol Site for Popup is the name of the underlying business component for the LS Clinical Region Document Site Popup applet. Id is a field that uniquely identifies a document in the source business component and a site in the business component for the popup applet. You can change the field mapping for data maps in the Data Component list in the Data Map Administration view of the Administration - Application screen. For more information about changing data maps, see Siebel Order Management Infrastructure Guide . You can configure additional properties for the LS Clinical Record Rolldown Service business service to set up additional functionality for rolldowns. An example of such a custom property follows: 'Custom Data Map', 'Source Business Component', 'Id', 'Business Component for the Popup Applet', 'Id' In this value, Custom Data Map is a data map that maps the fields in the source business component to the fields in the target business component, Id is a field that uniquely identifies a record in the source business component and a record in the underlying business component for the popup applet. The named method property for the underlying business component calls this business service when you click the OK button in the appropriate popup applet. The value for the named method property specifies the method for the appropriate popup applet, the underlying business component for that applet, the method in that business component that calls the rolldown business service, and the appropriate user property as the input argument for the business service. An example of such a custom property follows: 269", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "ClosePopUp", "definition": "LS Clinical Protocol Account Site", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Popup Applet", "definition": "This property configures notifications for bulk payments by specifying the value for the Severity field that is passed as an input argument to the workflow that the GenerateBulkPayment method calls when you click the OK button in the LS Clinical Site Bulk Payment Applet. This applet appears when you click the Generate Payment button in the Payments view of the Protocols screen or the Regions screen. The input argument is a key-value combination. An example follows: Severity=\"Normal\" This example shows the default value for this property, but you can change this value to any value for the Severity field that has a Type field value of BRDCST_MSG_TYPE in the list of values and that is active in the list of values. Alternate values include: High, Urgent, and Urgent with Alert. For applets that are associated with the CSSSWEFrRolePopup class and that have Clinical Protocol Site Bulk Operations as the underlying business component, you can create additional input arguments for the workflow that the GenerateBulkPayment method calls by creating additional applet properties as WF.field name , and recompiling the applet. For example, a custom property of WF.CustomerName might have the following input argument for the workflow: CustomerName=\"Elixir Labs\" Field Properties in Siebel Clinical The following information lists the field properties that you can use to enable and configure functionality for Siebel Clinical. 274", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Site Popup Applet", "definition": "This property closes the pop-up applet when an end user performs an action to close this applet, such as clicking the OK button. This action calls the method in the value for this property. You can enter a comma-delimited list in this property to specify multiple methods. An example follows: ClosePopUp=\"method 1\", \"method 2\", \"method 3\" The default value (method name) for each of the six applets follows: • ProtocolAccountRolldownToSite • ProtocolActivityRolldownToSite • ProtocolDocumentRolldownToSite • RegionAccountRolldownToSite • RegionActivityRolldownToSite • RegionDocumentRolldownToSite You can use this property in applets that are associated with the CSSSWEFrRolePopup class.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "HideInQueryMode", "definition": "Any applet that uses one of the following classes or a class derived from these classes: CSSSWEClinicalList Base CSSSWEClinicalForm Base This property hides the buttons that call the methods in this property. This property hides these buttons when end users click Query in the applet. You can enter a comma-delimited list in this property to specify multiple methods. An example follows: HideInQueryMode=\"ShowAll\", \"ShowCurrent\", \"CallCustomerMaster\" If the button calls the ShowPopup method, you do not have to include that method in this property.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "History Target BC", "definition": "(Internal) Clinical Protocol Team Mvg", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Applet", "definition": "This property specifies the target business component. The default values follow: • Clinical Protocol Site Team Assignment History BC • Clinical Protocol Team Assignment History BC 270", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Version Assoc Applet", "definition": "This property configures the versions of the subject visit template that appear in the Site Management Versions MVG (multi value group). Configure this property as follows: • To display only the versions of the subject visit templates with a status of Approved, set the value as follows: [Status Cd] = LookupValue(\"CLNCL_VERSION_ STATUS\",\"Approved\") • To display the versions of the subject visit templates with a status of Reviewed or Approved, set the value as follows: [Status Cd] = LookupValue(\"CLNCL_VERSION_ STATUS\",\"Approved\") OR [Status Cd] = LookupValue(\"CLNCL_VERSION_STATUS\",\" Reviewed\") The default configuration displays only subject visit templates with a status of Approved in the Site Management Versions MVG.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Popup Visibility Type", "definition": "LS Clinical Site Bulk Payment", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Catalog", "definition": "View modes determine the records that end users can see in a view that is associated with an underlying business component. For example, if you set this property value to Organization, then the end user who clicks the Generate button can see the site records for the protocol or region that are associated with the organization of the end user. This property overrides the pop-up visibility of the underlying business component. The underlying business component must support the view mode associated with the value that you enter. For more information about view modes, see Siebel Security Guide . 271", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Divisions", "definition": "LS Clinical Site Bulk Payment", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Positions", "definition": "LS Clinical Site Bulk Payment", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Rescreen Date", "definition": "Note: This property is deprecated in version 8.1.1.9 and later. This property configures the subject visit template type that is used when applying a template. The default property value for each parent field follows: Enrollment Date = ‘Enrollment’ Screen Date = ‘Screening’ Rescreen Date =‘Re-Screening’ System Preferences in Siebel Clinical The following table lists the system preferences that you can use to configure core functionality in Siebel Clinical Trial Management System and to integrate Siebel Clinical Trial Management System with third-party applications. 275", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Screen Date", "definition": "This property configures whether or not subject informed consent is mandatory when scheduling a clinical subject. When enabled, the Informed Consent Date field is checked for data when the user clicks Schedule. Configure this property as follows: • To set the Informed Consent Date as a mandatory field when scheduling a clinical subject, set the value to Y. • To set the Informed Consent Date as an optional field when scheduling a clinical subject, set the value to N. The default value is N. Template Type Code]", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Functionality", "definition": "The user can set the value to either Y or N. If the value is set to Y, the system won't create the CRF records during scheduling of the Subject. If the value is set to N, the system creates the CRF records during Subject scheduling. The default value must be set to N. CL- Enable C1 integration This setting helps the user to decide whether the CTMS application is integrated with Clinical One application. The user can set the value to either Y or N. If the value is set to Y, it means that the CTMS application is integrated with Clinical One application. If the value is set to N, it means there's no integration. The default value must be set to N. Workflows in Siebel Clinical The following information lists the required workflows for Siebel Clinical core functionality and for integrating Siebel Clinical with third-party applications. For more information about each workflow, see the corresponding functional area in this guide. You can use Siebel Business Process Designer to modify the workflows to suit your own business model. For information about configuring workflows, see Siebel Business Process Framework: Workflow Guide .", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Workflow Name", "definition": "Siebel Clinical Functionality SWI LS Clinical Subject Inbound - Subject Clinical data management system integration SWI LS Clinical Subject Inbound Clinical data management system integration SWI - Protocol Number Lookup Clinical data management system integration Web Services in Siebel Clinical You can customize the Web services in Siebel Clinical for integration with any third-party clinical application or for specific business requirements. The following information lists the Web services for mobile and external application integration. For more information about each Web service, see the corresponding integration chapter in this guide. For information about customizing Web services, see Siebel CRM Web Services Reference .", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Clinical Site", "definition": "LS Clinical Site Subject Delete Accruals", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Clinical Template", "definition": "LS Clinical - DeleteNonAppVisits Process Clinical Subject Visits LS Clinical - SubjectVisits Process Clinical Subject Visits LS Clinical Contract Rollup", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Clinical Contract", "definition": "LS Clinical Create Inbox Item for New Trip", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Clinical Training", "definition": "LS Clinical Trip Report Approval", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "NewSite", "definition": "Site visit data integration LS ClinicalProtocolSite Outbound -", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Location", "definition": "Site visit data integration SWI LS Clinical Payments Inbound Third-party payments application integration SWI LS Clinical Query Protocol Site_Site", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "ClinicalSubject", "definition": "Clinical data capture integration LS Clinical CRF Tracking Interface Clinical operations integration LS Clinical Protocol Site Interface Service Clinical data capture integration Clinical operations integration LS Clinical Subject Information Interface", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Service", "definition": "CTMS and eTMF integration SWI LS Clinical Payments Inbound Payments application integration SWILSClinicalActivityTemplate", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Mobile integration", "definition": "SWILSClinicalTripReportFileTransfer Web Service This Web service is used to send out trip report details to external applications and to update the trip report details in CTMS. Operations supported to query the trip report details are Query, QueryPage, and Upsert. Operations supported to alter the trip report are Query, Query Page, and Insert or Update. Whoever invokes this Web service must be a member of the site visit team. External applications can invoke this Web service with any methods. Mobile application users can also use this Web service to update trip reports offline. For more information about using the SWILSClinicalTripReportFileTransfer Web service, see the following topics: • SWILSClinicalTripReportFileTransfer Operations • Request Message for TripReportFileTransferQuery • Request Message for ClinicalTripReportFileTransferQueryPage • Request Message for TripReportFileTransferUpsert • Response Message for SWILSClinicalTripReportFileTransfer • SWILSClinicalTripReportFileTransfer Application Interface 282", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Optional", "definition": "SWILSClinicalTripReportFileTransfer Application Interface The topic describes the application interface for SWILSClinicalTripReportFileTransfer. The following application objects are called by the SWILSClinicalTripReportFileTransfer Web service: • Service Object (Business Service or Workflow) • Data Object (Integration Object) For more information on application implementation, refer to your application development documentation on Oracle Technology Network. Service Object (Business Service or Workflow) The following table describes the service object for SWILSClinicalTripReportFileTransfer.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Class", "definition": "LS Clinical Trip Report File Transfer", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Business Service", "definition": "CSSEAISiebelAdapterService Data Object (Integration Object) The following table describes the data object for SWILSClinicalTripReportFileTransfer.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "External Name", "definition": "LS Clinical Trip Report File Transfer", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "systems", "definition": "Configuration of Bearer Token Authentication in Siebel", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "PowerTrials systems", "definition": "Setting Up Integration Between CTMS and PowerTrials This chapter describes how to integrate CTMS with PowerTrials system. It includes the following topics: • Overview of Integration Between CTMS and PowerTrials • CTMS and PowerTrials Integration Process Flow • Process of Setting Up Integration Between CTMS and PowerTrials • Configuring Email Recipients • Configuration of Bearer Token Authentication in Siebel CTMS • Setup and Configuration of Billing Grid Designation and SVT Mandatory Fields Overview of Integration Between CTMS and PowerTrials For customers using Siebel CTMS and Oracle Health PowerTrials, the primary goal of the outbound integration is to securely automate the creation, update, or sharing of records in one system to flow into the other. The new integration between CTMS and PowerTrials will leverage standard APIs in PowerTrials based upon the Retrieve Protocol for Execution (RPE) specifications with optional uptake by new or existing customers who have PowerTrials. It is delivered via new SOAP-based APIs and new security responsibilities for restricting access to sensitive patient records. Following are some terminologies: • PROTOCOL: A PowerTrials integration enabled Protocol in CTMS can trigger a workflow to create and update information with a minimum set of configurable fields in CTMS over to PowerTrials. • PATIENT / SUBJECT: A new controlled Patient screen in CTMS will allow controlled access to research site for users with the ability to link patients to be identified as a subject in a Protocol to trigger the enrollment and updates of subject details from CTMS over to PowerTrials. • SUBJECT VISIT TEMPLATE (SVT): A PowerTrials integration enabled Protocol in CTMS can securely automate the creation, update, or sharing of Subject Visit Template records containing billing details over to PowerTrials. CTMS and PowerTrials Integration Process Flow CTMS provides a outbound API to support pushing protocol/subject/SVT details from CTMS to PowerTrials. Patient/Subject, Protocol, and SVT details are pushed from CTMS to PowerTrials systems via outbound APIs. 291", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Add Proxy settings", "definition": "Add Proxy in setenv.sh under applicationcontainer_internal/bin. Command: CATALINA_OPTS=\"-Dhttp.proxySet=true -Dhttps.proxySet=true - Dhttps.proxyHost=www-proxy.us.oracle.com -Dhttps.proxyPort=80 -Dhttp.proxyHost=www- proxy.us.oracle.com -Dhttp.proxyPort=80 - Djavax.net.ssl.trustStore=/u01/siebel/ses/applicationcontainer_internal/siebelcerts/siebelkeystore.jks - Djavax.net.ssl.trustStorePassword=siebel - Djavax.net.ssl.keyStore=/u01/siebel /ses/applicationcontainer_internal/siebelcerts/siebelkeystore.jks - Djavax.net.ssl.keyStorePassword=siebel\" Path for Cloud customers: /u01/siebel/ ses/applicationcontainer_internal/siebelcerts/siebelkeystore.jks - Djavax.net.ssl.trustStorePassword=siebel - Djavax.net.ssl.keyStore=/ u01/siebel /ses/applicationcontainer_internal/siebelcerts/siebelkeystore.jks - Djavax.net.ssl.keyStorePassword=siebel\" For Cloud Customers and for Multi-Server Setup Use any GW Cluster node in the OURBOUNDSHA2 profile jbs parameter as shown below for multi-server setup. 1. Navigate to Administration > Server Configuration > Enterprise Profile. 2. Query for Profile OUTBOUNDSHA2 and change container URL. Note: To avoid the burden on primary and secondary cluster nodes, preferably use the third GW cluster node. 293", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "For Non-ENU customers", "definition": "1. In Siebel Web Tools, navigate to Workflow, search for name LS Clinical Protocol Synchronization. Under Create Log Step, change the input argument Wf Run Status value to IIF([&ResponseValue] = 'Failure', LookupValue (\"LSCL_INT_STATUS\", \"ERROR\"), LookupValue (\"LSCL_INT_STATUS\", \"SUCCESSFUL\")) . 2. In Siebel Web Tools, navigate to Workflow Query for LS Clinical Create Integration Log. a. In Log Error condition decision step update expression value to ‘Wf Run Status’ value to ‘([&Log Level]=LookupValue(\"LSCL_INT_LOG_LEVEL\", \"ENABLE\")) OR ( [&Wf Run Status] = LookupValue(\"LSCL_INT_STATUS\", \"ERROR\") AND [&Direction] = LookupValue(\"LSCL_INT_DIRECTION\", \"OUTBOUND\")) b. In Inbound condition decision step update expression value to ([&Direction] = LookupValue(\"LSCL_INT_DIRECTION\", \"INBOUND\") OR ([&Direction] = LookupValue(\"LSCL_INT_DIRECTION\", \"OUTBOUND\") AND [&OperationType] <> \"Retry\")) 3. Navigate to Administartion-Integration under Data Map Editor, search for LS Clinical PT Protocol and then for name code6 under Integration Component Map. Remove Source Search Specification value that is already provided. 294", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "CTMS", "definition": "To configure bearer token authentication in Siebel CTMS 1. Authentication Details: A Bearer token (encrypted System Account) needs to be configured for authentication in Siebel CTMS. This token will be authenticated by PowerTrial. 2. System Account Creation: The required System Account will be created in Cerner. 3. Siebel Configuration: a. Navigate to Siebel Web Tools. b. Locate the Clinical Protocol Business Component. c. Under the Business Component, go to User Properties, search for a user property named Token. d. In the Value field of the Token User Property, add the encrypted value of the System Account. e. Navigate to SIEBSRVR_ROOT\\bin, encryptstring systemaccount to set up encryption. 4. To send a request from CTMS to PowerTrail without Bearer token Authentication, the user property Token must be set to null. Setup and Configuration of Billing Grid Designation and", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "SVT Mandatory Fields", "definition": "To setup and configure Billing Grid Designation and SVT mandatory fileds: 1. Go to Administration – Clinical > Integration Administration > Integration Systems > Attributes. You can add PT SVT Mandatory Fields and PT SVT Billing Grid Designation. Note: ◦ You can define the mandatory fields needed for SVT integration. ◦ You can define the values for the Billing Grid designation. 295", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Missed Milestones", "definition": "To setup the milestone Email alerts, you need to create a job and Email profile as described: You can configure email alerts for approaching milestone completion dates. 1. Create a new job as Workflow Process Batch Manager under Jobs. 2. You can configure the job details like “Repeat Interval”, “Scheduled Start”, “Repeat Unit”, and other details. 3. Add under the Job Parameters, select Name as Workflow Process Name and enter value as LS Clinical Milestone Activity Alert Email. 4. Add a Job Parameter Search Specification and set it to: ([Actual Date] IS NULL AND [Category] = \"Milestone\" AND ([Due Approach] = \"Y\" OR [Due Crossed] = \"Y\")). This condition will pick the Milestones that are either approaching the due date or past the due date. 5. Add an email profile name as Milestone Activity Alert Email Profile. 6. Set appropriate profile parameters for your environment. For more information on setting up an Email Profile, refer to, Email Administration Guide in Siebel Bookshelf. 301", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Effective End Date", "definition": "◦ Status (defaults to ‘Draft’ for a new version) State transitions: Draft > Reviewed Reviewed > Active Active > Archived Archived > Active Note: ◦ In Draft status, you can manually enter, update or import Charge Sheet Items into a Charge Sheet Version. ◦ When status is set to ‘Reviewed’ the Reviewed by is auto-populated. ◦ When status is set to ‘Active’ the ‘Activated By’ is auto-populated. Importing Charge Sheet Items To Import records to a Charge Sheet version: 1. Click on the Import button. 2. You can see a pop-up window to browse for a comma separated value (.csv) following the Charge Sheet import format. Refer to the table for Charge Sheet IO: Charge_ Sheet_ Version.Version Charge_ Sheet_ Version.Currency Charge_ Sheet_ Code.Code Charge_ Sheet_ Code.Name Charge_ Sheet_ Code.Code", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Fee", "definition": "Charge_ Sheet_ Code.Category Charge_ Sheet_ Code.Category", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Subtype", "definition": "Charge_ Sheet_ Code.Comments Charge_ Sheet_ Code.Code", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "CT", "definition": "Teston1058updTestCodeDescupd Managing a Charge Sheet Versions Following are the actions that you can perform when working on a Charge Sheet Version: Charge Sheet Version state", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Actions", "definition": "Draft/Reviewed • Can delete Sheet version • Edit sheet version attributes • Manual entry or import Charge Sheet version items (records)", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Archived", "definition": "• Cannot delete, edit, import any records • Effective End date is mandatory Assigning Charge Sheet to Subject Visit Template 1. Under Administration Clinical, go to Visit Template. 305", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Template Version", "definition": "b. Subject Visit Template Version level: - You can select from ‘Active’ Charge Sheet Versions only and set it to a version different from the Master Visit Template. Assigning CPT Codes and Billing Designation to SVT", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "PII Business Service", "definition": "Siebel CTMS utilizes the Siebel AI framework to identify Personally Identifiable Information (PII) across various business components. It achieves this by calling the SiebelClinicalOCIAIService within the Clinical class code, which connects to Oracle Cloud Infrastructure (OCI) AI services for text analysis.This functionality is configurable and can be extended to other components based on customer requirements. 308", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "Masking", "definition": "Siebel Administrators can setup a configuration to mask the PII information automatically under the Business Component User Properties. You can add a property for ‘modifyText’ and when set to true, the text where PII information is suspected can be masked. Siebel Developers can also configure the masking character that they would like to use for masking the PII information under the Business Component User Properties. You can add a configuration, ‘maskingCharacter:’. For example, you can set the ‘maskingCharacter:*’ where the text is masked with ‘*’.", "sources": [ "clinical-trial-management-system-guide.pdf" ], "file": "clinical-trial-management-system-guide.pdf", "type": "pdf" }, { "term": "CLINICAL RESEARCH", "definition": "Glossary of Terms and DefiniƟons FDA-NIH Clinical Research Working Group Glossary Terms and DefiniƟons", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Page 1 of 11", "definition": "DraŌ Product of FDA-NIH CRWG IntroducƟon: The U.S. Food and Drug AdministraƟon and the NaƟonal InsƟtutes of Health (NIH) Joint Leadership Council convened a working group to bring clarity to terms that are inconsistently used within the scienƟfic community. Working together with the goals of improving communicaƟon and scienƟfic understanding, the two agencies developed a glossary with definiƟons of terms used in clinical research. To fulfill their charge, the FDA-NIH Clinical Research Working Group (CRWG) developed definiƟons for 37 clinical research terms related to innovaƟve clinical study designs, including studies using real-world data (RWD) to generate real-world evidence (RWE), that support scienƟfic, clinical, and/or regulatory decision-making. In addiƟon, 8 terms are included for reference and FDA-NIH are not seeking comment on these 8 terms. The membership of the FDA-NIH CRWG consisted of staƟsƟcians, epidemiologists, pharmacologists, clinicians, biomedical engineers, and policy experts from both agencies. This document is intended to facilitate communicaƟon within the clinical research community, by helping establish a common vocabulary to more uniformly characterize clinical research, including innovaƟve trial designs and studies using RWD to generate RWE. In turn, the community may be beter situated to evaluate potenƟal strengths and weaknesses of individual studies, and to convey innovaƟve and other aspects of clinical research in a meaningful way to funders, reviewers, and other interested parƟes. Terms in the glossary are grouped into the following two areas: 1. Terms for Comment: These terms include approaches to clinical study design, methodology, and interpretaƟon and descripƟon of research results. Where an exisƟng definiƟon is used or adapted, the source(s) are listed. 2. Terms for Reference: These are terms with well-established definiƟons (e.g., well-established in the scienƟfic community) included for completeness and are not newly defined with this project. They are included as Appendix A to add context for the terms the FDA-NIH CRWG have defined under the “Terms for Comment”. Glossary Terms and DefiniƟons", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Page 2 of 11", "definition": "DraŌ Product of FDA-NIH CRWG", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "AdministraƟve Claims Data", "definition": "The informaƟon obtained from claims that health care providers submit to insurers to receive payment for treatments and other intervenƟons. Claims data use standardized medical coding systems (nomenclatures), such as the World Health OrganizaƟon InternaƟonal ClassificaƟon of Diseases Coding (ICD-CM) to idenƟfy diagnoses, NaƟonal Drug Code (NDC) to idenƟfy drugs, and Current Procedural Terminology (CPT®) to idenƟfy procedures.", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Causal Effect", "definition": "A measure of difference in outcome that would be expected in individuals subjected to an exposure of interest compared to the expected outcome if those same individuals were subjected to a specified alternaƟve exposure (including no exposure). Source: Adapted from Musci R.J. & Stuart, E. (2019). Ensuring causal, not casual, inference. Prevention Science, 20, 452–456, htps://doi.org/10.1007/s11121-018-0971-9.", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Causal Inference", "definition": "The process of evaluaƟon, esƟmaƟon, and atribuƟon of a causal effect. Cluster Randomized Trial A trial in which randomizaƟon is at a group level (for instance, by community, health care facility or medical provider) rather than an individual level. Source: Adapted from the Secretary’s Advisory Commitee on Human Research ProtecƟons, RecommendaƟons on Regulatory Issues in Cluster Studies. (2014). Available at: Attachment C: Recommendations on Regulatory Issues in Cluster | HHS.gov.", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Common Data Element", "definition": "A standardized, precisely defined variable or question that is paired with a set of specific allowable values or responses, that are used systematically across different sites, studies, or clinical trials to ensure consistent data collection and/or analysis. Source: Adapted from the NIH CDE Repository. Glossary Terms and DefiniƟons", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Page 3 of 11", "definition": "DraŌ Product of FDA-NIH CRWG Common Data Model (CDM) Comprehensive framework that includes definiƟons, specificaƟons, and operaƟonal rules for data to be presented and used in a common manner to enable interoperability. Source: Adapted from Duke Margolis Center for Health Policy, Characterizing RWD Quality and Relevancy for Regulatory Purposes. Available at htps://healthpolicy.duke.edu/sites/default/files/2020- 03/characterizing_rwd.pdf. Completeness of Capture The extent to which a data source includes a complete representaƟon of the exposures, outcomes, and covariates needed for the proposed analysis. Incomplete capture may be due to variables that were not recorded or variables that include some missing values.", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Computable Phenotype", "definition": "A clinical condiƟon or characterisƟc that can be ascertained using a computerized query to Electronic Health Record (EHR) system, administraƟve claims database, or clinical data repository using a defined set of data elements and logical expressions. Computable phenotype definiƟons provide the specificaƟons for idenƟfying populaƟons likely to have the condiƟons or characterisƟcs of interest. Source: Adapted from FDA draŌ guidance Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products (September 2021). When final, this guidance will represent FDA’s current thinking on these topics.", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Confounding", "definition": "SystemaƟc error in esƟmaƟon of the measure of the effect of a medical product on an outcome due to another factor that is associated with both the exposure and the outcome and not through the causal pathway between exposure and outcome. Source: Adapted from Porta M. et al, A DicƟonary of Epidemiology. (2014). United Kingdom: Oxford University Press. ConƟnuity of Coverage The period of Ɵme over which an individual is enrolled in a health care system (provider, pharmacy, insurer, or other) and for which data on provided or reimbursed healthcare services and treatments are captured in that system. Source: Adapted from FDA draŌ guidance Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products (September 2021). When final, this guidance will represent FDA’s current thinking on these topics. Glossary Terms and DefiniƟons", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Page 4 of 11", "definition": "DraŌ Product of FDA-NIH CRWG", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Data CuraƟon", "definition": "Processing of source data (unstructured and/or structured data) into a dataset suitable for analyses. The curaƟon process involves the applicaƟon of standards for the exchange, integraƟon, sharing, and retrieval of source data, oŌen from various sources. For example, the applicaƟon of standard medical diagnosƟc codes to adverse events, disease staging, the progression of disease, and other medical and clinical concepts. Source: Adapted from FDA final guidance CVM GFI #266 Use of Real-World Data and Real-World Evidence to Support Effectiveness of New Animal Drugs (October 2021). Data HarmonizaƟon The process of combining data from different sources and reorganizing it according to a single schema so that data are compaƟble and comparable. Data are combined by either idenƟfying equivalent data elements between the sources or by applying specific transformaƟons between the elements to derive a common data element. Source: Adapted from the NaƟonal InsƟtute of Environmental Health Sciences, Common Language Glossary from the Environmental Health Language CollaboraƟve. Data ImputaƟon A process using staƟsƟcal techniques to esƟmate missing data values, facilitaƟng subsequent analyses. Source: Adapted from Food and Agricultural OrganizaƟon of the United NaƟons, StaƟsƟcal Standard Series ImputaƟon Version 2.0. Available at: htps://www.fao.org/3/cb9339en/cb9339en.pdf.", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Data Lake", "definition": "A controlled, centralized environment that stores structured and unstructured data in its naƟve form and provides infrastructure for organizing large volumes of diverse data from mulƟple sources. Source: Adapted from Amazon AWS. Data TransformaƟon The process of converƟng data from one format or structure into another format or structure. It is a process of data extracƟon and conversion or normalizaƟon in construcƟon of analyƟc datasets. Source: Adapted from FDA final guidance CVM GFI #266 Use of Real-World Data and Real-World Evidence to Support Effectiveness of New Animal Drugs (October 2021). Glossary Terms and DefiniƟons", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Page 5 of 11", "definition": "DraŌ Product of FDA-NIH CRWG", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Data Warehouse", "definition": "A controlled, centralized environment that stores structured data in a processed form for analysis and provides infrastructure for data access by mulƟple applicaƟons. Source: Adapted from FDA draŌ guidance Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products (September 2021). When final, this guidance will represent FDA’s current thinking on these topics. Distributed Data Network A network in which data from mulƟple sites are transformed into a single common data model with the ability to execute a query without substanƟal modificaƟons on mulƟple datasets. Source: Adapted from FDA draŌ guidance Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision Making for Drug and Biological Products (September 2021). When final, this guidance will represent FDA’s current thinking on these topics.", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Electronic Health Record", "definition": "An individual paƟent record contained within an electronic system. A typical individual record may include a paƟent medical history, diagnoses, treatment plans, immunizaƟons, allergies, imaging, pharmacy orders, laboratory values, and test results. Source: Adapted from FDA draŌ guidance Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products (September 2021). When final, this guidance will represent FDA’s current thinking on these topics.", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Immortal Time", "definition": "A span of Ɵme in the observaƟon or follow-up period of a cohort during which the outcome under study could not have occurred, due to the cohort design and/or exposure definiƟon. Source: Adapted from Suissa, S. (2008). Immortal Ɵme bias in pharmacoepidemiology, American Journal of Epidemiology, 167(4),492–499, htps://doi.org/10.1093/aje/kwm324. Glossary Terms and DefiniƟons", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Page 6 of 11", "definition": "DraŌ Product of FDA-NIH CRWG InformaƟon Bias SystemaƟc error in esƟmaƟon of an associaƟon or other parameter of interest arising from measurement error in the data. For categorical variables, measurement error is usually called classificaƟon error or misclassificaƟon. Source: Adapted from Daniel, G., Silcox, C., Bryan, J., McClellan, M., Romine, M., & Frank, K. (2018). Characterizing RWD Quality and Relevancy for Regulatory Purposes. Duke Margolis Center for Health Policy, htps://healthpolicy.duke.edu/sites/default/files/2020- 03/characterizing_rwd.pdf and Rothman KJ, Greenland S, Lash TL, Lippincot Williams & Wilkins. (2008). Modern Epidemiology. IntervenƟonal Study A study involving parƟcipants (e.g., healthy individuals or individuals with a disease or condiƟon of interest) whose exposure or interacƟon with a medical product is assigned according to a study protocol to evaluate the effect on health outcomes or product performance. Source: Adapted from FDA draŌ guidance ConsideraƟons for the Use of Real-World Data and Real-World Evidence to Support Regulatory Decision-Making for Drug and Biological Products (December 2021). When final, this guidance will represent FDA’s current thinking on these topics.", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Missing Data", "definition": "Data that would have been used in the study analysis but were not observed, collected, or accessible. This refers to informaƟon that is intended to be collected but is absent and informaƟon that is not intended to be collected and is therefore absent. There may be special consideraƟons regarding real world data sources (e.g., electronic health records or claims); such data are generally not collected for primary research purposes and therefore may not have systemaƟc data capture to answer a research quesƟon. Source: Adapted from FDA draŌ guidance Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products (September 2021). When final, this guidance will represent FDA’s current thinking on these topics.", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "N of 1 Trial", "definition": "A clinical trial evaluaƟng an intervenƟon or mulƟple intervenƟons in a single parƟcipant according to a protocol in which there is either switching between intervenƟon(s) and control or a planned comparison between an intervenƟon and a natural history. Glossary Terms and DefiniƟons", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Page 7 of 11", "definition": "DraŌ Product of FDA-NIH CRWG Non-interventional (observational) study A type of study in which individuals are not assigned to a medical product according to a protocol. Source: Adapted from FDA final guidance Use of Real-World Evidence to Support Regulatory Decision- Making for Medical Devices (August 2017) and FDA draŌ guidance ConsideraƟons for the Use of Real- World Data and Real-World Evidence to Support Regulatory Decision-Making for Drug and Biological Products (December 2021). When final, this guidance will represent FDA’s current thinking on these topics.", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "ObservaƟonal Study, RetrospecƟve", "definition": "A study that idenƟfies the populaƟon and determines the exposure/treatment from data collected before the iniƟaƟon of the study. The variables and outcomes of interest are determined at the Ɵme the study is designed. Source: Adapted from the Framework for FDA’s Real-World Evidence Program (December 2018) and FDA final guidance Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices (August 2017).", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "PragmaƟc Clinical Trial", "definition": "A clinical trial designed to efficiently inform decision-making on the benefits, burdens, and risks of health intervenƟons in representaƟve populaƟons by including pragmaƟc elements (see definiƟon below) that 1) are parƟally or fully integrated into rouƟne clinical pracƟce and/or 2) that streamline trial design and conduct. Source: Adapted from Califf R.M., Sugarman J. (2015). Exploring the ethical and regulatory issues in pragmaƟc clinical trials. Clinical Trials, (5), 436-441, htps://journals.sagepub.com/doi/10.1177/1740774515598334. PragmaƟc Elements Design features that can be integrated into a clinical trial, including but not limited to, one or more of the following elements: broad eligibility criteria, simplified recruitment and follow-up, flexibility in delivery of the intervenƟon (e.g., community seƫngs), flexibility in assessment frequency, and measurement of outcomes relevant to the populaƟon. Glossary Terms and DefiniƟons", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Page 8 of 11", "definition": "DraŌ Product of FDA-NIH CRWG", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Propensity Score", "definition": "The condiƟonal probability of assignment to a parƟcular treatment given a set (e.g., vector) of observed covariates. Source: Adapted from Rosenbaum, P.R., Rubin, D.B. (1983). The central role of the propensity score in observaƟonal studies for causal effects, Biometrika, 70(1), 41–55, htps://doi.org/10.1093/biomet/70.1.41.", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Registry", "definition": "An organized system that collects clinical and other data in a standardized format for a populaƟon defined by a parƟcular disease, condiƟon, or exposure. Source: Adapted from FDA final guidance Real-World Data: Assessing Registries To Support Regulatory Decision-Making for Drug and Biological Products (December 2023) and FDA final guidance Use of Real- World Evidence to Support Regulatory Decision-Making for Medical Devices (August 2017).", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Residual Confounding", "definition": "Confounding (see definition) that remains aŌer adjusƟng for measured confounders. Source: Adapted from Porta, M. A dicƟonary of epidemiology (6th ed.), https://www.oxfordreference.com/display/10.1093/acref/9780199976720.001.0001/acref- 9780199976720-e-1649.", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "SelecƟon Bias", "definition": "SystemaƟc error in esƟmaƟon of an associaƟon or other parameter that occurs from factors that influence study parƟcipaƟon [or eligibility]. Source: Adapted from Rothman KJ, Greenland S, Lash TL, Lippincot Williams & Wilkins. (2008). Modern Epidemiology. SequenƟal, MulƟple Assignment, Randomized Trial (SMART) A trial designed to evaluate a collecƟon of intervenƟons guided by a sequence of decision rules that specifies when and how the type and/or intensity of an intervenƟon should be modified depending on the paƟent’s past or present characterisƟcs and/or ongoing clinical state or performance (e.g., response, adherence) to opƟmize clinically important outcomes. In such a trial, paƟents move along mulƟple stages and are randomly assigned to one of several suitable intervenƟon opƟons at each stage. Source: Adapted from FDA final guidance InteracƟng with the FDA on Complex InnovaƟve Trial Designs for Drugs and Biological Products (December 2020). Glossary Terms and DefiniƟons", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Page 9 of 11", "definition": "DraŌ Product of FDA-NIH CRWG Stepped Wedge Cluster Randomized Trial In a stepped wedge group-randomized trial, also called a stepped wedge cluster randomized trial, groups or clusters are randomized to sequences that direct them to switch from a control to the intervenƟon at predetermined Ɵme points in a sequenƟal, staggered fashion unƟl all clusters receive the intervenƟon. Source: Adapted from Home | Research Methods Resources (nih.gov). SyntheƟc Data Data that have been created arƟficially (e.g., through staƟsƟcal modeling, computer simulaƟon) so that new values and/or data elements are generated. Generally, syntheƟc data are intended to represent the structure, properƟes and relaƟonships seen in actual paƟent data, except that they do not contain any real or specific informaƟon about individuals.", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Target Trial EmulaƟon", "definition": "A framework for designing and analyzing an observaƟonal study based on conceptualizing a target randomized trial to answer a scienƟfic quesƟon and designing the observaƟonal study to mimic the trial esƟmand(s) (including specificaƟon of populaƟon eligibility criteria, treatment strategies and assignment procedures, outcomes, handling of intercurrent events, and follow-up period).", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Time-Related Bias", "definition": "SystemaƟc error in esƟmaƟon of an associaƟon or other parameter of interest due to misclassificaƟon or exclusion of person-Ɵme atributed to the treatment, intervenƟon, or exposure. Examples include protopathic bias, latency Ɵme bias, immortal Ɵme bias, Ɵme-window bias, depleƟon-of-suscepƟbles, immeasurable Ɵme bias, and other such biases.", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Umbrella Trial", "definition": "Trial designed to evaluate mulƟple medical products in separate sub-studies concurrently for a single disease or condiƟon. Source: Adapted from FDA draŌ guidance Master Protocols for Drug and Biological Product Development | FDA (December 2023). When final, this guidance will represent FDA’s current thinking on these topics. Glossary Terms and DefiniƟons", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Page 10 of 11", "definition": "DraŌ Product of FDA-NIH CRWG", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Terms for Reference – These terms are included because they provide context for terms under “Terms for", "definition": "Comment”. We are not requesƟng comment on these terms. AdapƟve Design A clinical trial design that allows for prospectively planned modifications to one or more aspects of the design based on accumulating data from subjects in the trial. Source: FDA final guidance AdapƟve Design Clinical Trials for Drugs and Biologics Guidance for Industry (December 2019).", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Basket Trial", "definition": "Trial designed to evaluate a medical product for different diseases, condiƟons, or disease subtypes. Source: FDA draŌ guidance Master Protocols for Drug and Biological Product Development | FDA (December 2023). When final, this guidance will represent FDA’s current thinking on these topics. Conceptual DefiniƟon Explains a study construct (e.g., exposure, outcomes, covariates) or feature in general or qualitaƟve terms. Source: FDA draŌ guidance Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products (September 2021). When final, this guidance will represent FDA’s current thinking on these topics.", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Master Protocol", "definition": "A protocol designed with mulƟple sub-studies, which may have different objecƟves and involve coordinated efforts to evaluate one or more medical products in one or more diseases or condiƟons within the overall study structure. Source: FDA draŌ guidance Master Protocols for Drug and Biological Product Development | FDA (December 2023). When final, this guidance will represent FDA’s current thinking on these topics. Glossary Terms and DefiniƟons", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Page 11 of 11", "definition": "DraŌ Product of FDA-NIH CRWG ObservaƟonal Study, ProspecƟve A study in which the populaƟon of interest is idenƟfied at the start of the study, and exposure/treatment and outcome data are collected from that point forward. The start of the study is defined as the Ɵme the research protocol for the specific study quesƟon was iniƟated. Source: Framework for FDA’s Real-World Evidence Program (December 2018). OperaƟonal DefiniƟon The data-specific operaƟon or procedure a researcher followed to measure constructs in a parƟcular study. Source: FDA draŌ guidance Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products (September 2021). When final, this guidance will represent FDA’s current thinking on these topics. PaƟent Generated Health Data Health-related data created, recorded, or gathered by or from paƟents, family members, or other caregivers to help address a health concern. Source: DefiniƟon adapted from htps://www.healthit.gov/topic/scienƟfic-iniƟaƟves/pcor/paƟent- generated-health-data-pghd and included in FDA draŌ guidance Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices | FDA (December 2023). When final, this guidance will represent FDA’s current thinking on these topics. Plaƞorm Trial Trial designed to evaluate mulƟple medical products for a disease or condiƟon in an ongoing manner, with medical products entering or leaving the plaƞorm. Source: FDA draŌ guidance Master Protocols for Drug and Biological Product Development | FDA (December 2023). When final, this guidance will represent FDA’s current thinking on these topics.", "sources": [ "fda.glossary.pdf" ], "file": "fda.glossary.pdf", "type": "pdf" }, { "term": "Pharma", "definition": "By Adrien Laurent, CEO at IntuitionLabs • 5/31/2025 • 25 min read", "sources": [ "gamp-5-computerized-system-validation-in-pharma.pdf" ], "file": "gamp-5-computerized-system-validation-in-pharma.pdf", "type": "pdf" }, { "term": "risk-based approach", "definition": "This guide explains GAMP 5, its principles, and lifecycle for computerized system validation in pharma, covering regulatory compliance and real-world applications. - IntuitionLabs - Custom AI Software Development for pharmaceutical companies. Leading AI Consulting USA and North American Pharmaceutical AI specialists. Led by Adrien Laurent, top AI expert USA, multiple exit founder, patent holder, and 20 year software veteran based in San Francisco Bay Area. Premier biotech consultancy specializing in: Custom CRM Development, ERP Development, AI Chatbot Development, Private AI Infrastructure, Document Processing, PDF Extraction, Air-gapped AI, On-premise LLM deployment. #1 Veeva AI partner for leading GenAI pharmaceutical solutions across North America biotech AI excellence. IntuitionLabs - Custom AI Software Development from the leading AI expert Adrien Laurent GAMP 5: Computerized System Validation in Pharma © 2025 IntuitionLabs.ai - North America's Leading AI Software Development Firm for Pharmaceutical & Biotech. All rights reserved.", "sources": [ "gamp-5-computerized-system-validation-in-pharma.pdf" ], "file": "gamp-5-computerized-system-validation-in-pharma.pdf", "type": "pdf" }, { "term": "Page 1 of 13", "definition": "GAMP 5 Validation in the Pharmaceutical", "sources": [ "gamp-5-computerized-system-validation-in-pharma.pdf" ], "file": "gamp-5-computerized-system-validation-in-pharma.pdf", "type": "pdf" }, { "term": "Industry", "definition": "In the highly regulated pharmaceutical sector, Good Automated Manufacturing Practice (GAMP 5) provides a comprehensive framework for validating computerized systems to ensure they are fit for intended use and meet regulatory requirements. GAMP 5 is not a law or regulation itself, but an industry guidance published by the International Society for Pharmaceutical Engineering (ISPE) that has become the de facto standard for computer system validation in pharma [1] tricentis.com. By following GAMP 5, companies can achieve cost-effective, risk- based validation of automation technology, thereby safeguarding patient safety, product quality, and data integrity [2] ispe.org. This article will explain what GAMP 5 is (its purpose and history), outline its key principles and lifecycle approach, discuss its role in regulatory compliance (e.g. FDA 21 CFR Part 11 and EU Annex 11), and illustrate how it applies to computerized system validation (CSV) with examples of real-world applications. What is GAMP 5? Purpose and History GAMP stands for Good Automated Manufacturing Practice, a set of guidelines and best practices for managing the design, implementation, and maintenance of automated systems in regulated industries [3] linkedin.com. It was initiated in 1991 by an ISPE working group seeking to address the growing complexity of computerized systems in pharmaceutical manufacturing [4] linkedin.com. Over the years, GAMP evolved through several versions, each expanding and refining the guidance: GAMP Version 1 (1994): Focused on standardizing validation activities for computerized systems [5] linkedin.com. GAMP 2 (1995): Introduced some early concepts of risk-based approaches to system validation [6] linkedin.com. GAMP 3 (1998): Expanded the scope to broader applications, including IT infrastructure and supplier quality audits [7] linkedin.com. GAMP 4 (2001): Emphasized a full system lifecycle model and defined clear validation deliverables for each phase [8] linkedin.com. GAMP 5 (2008): The fifth version (first edition) shifted strongly to a risk-based approach to compliance, promoting scalability and efficiency in validation to reflect advancements in automation and software [9] linkedin.com. GAMP 5 remains the current version, with a Second Edition published in 2022 to update the guidance for modern technologies. The core principles and framework from 2008 were maintained, but the 2022 update addresses new developments such as increased use of cloud service providers, agile software development methods, and emerging tech like AI and blockchain [10] ispe.org [11] ispe.org. Notably, ISPE highlights that GAMP 5 aims to “protect patient safety, product quality, and data integrity by facilitating … computerized systems that are effective, reliable, and of high quality” [12] ispe.org. In practice, GAMP provides a “risk-based approach to compliant GxP computerized systems”, meaning it helps companies focus their validation efforts on what matters most for Good Manufacturing Practice (GMP) compliance [2] ispe.org. It is widely recognized by regulators and industry alike as the leading guidance for GxP computer system validation and compliance[13] ispe.org. GAMP 5 is a non-mandatory guidance, but its adoption is widespread because it aligns closely with regulatory expectations. ISPE and its GAMP Community of Practice continually update the guidance to stay current with good IT and software engineering practices [14] ispe.org. This ensures that using GAMP 5 helps firms meet the latest standards of quality and compliance in computerized systems without relying on outdated methods. IntuitionLabs - Custom AI Software Development from the leading AI expert Adrien Laurent GAMP 5: Computerized System Validation in Pharma © 2025 IntuitionLabs.ai - North America's Leading AI Software Development Firm for Pharmaceutical & Biotech. All rights reserved.", "sources": [ "gamp-5-computerized-system-validation-in-pharma.pdf" ], "file": "gamp-5-computerized-system-validation-in-pharma.pdf", "type": "pdf" }, { "term": "Page 2 of 13", "definition": "Key Principles and Lifecycle Approach of GAMP 5 GAMP 5 is built on a set of key principles that guide its practical implementation. These principles ensure that validation efforts are science-based, risk-based, and efficient. The five guiding principles of GAMP 5 can be summarized as follows [15] tricentis.com: 1. Product and Process Understanding: Successful validation begins with deep knowledge of the product and process. GAMP 5 encourages critical thinking to distinguish critical aspects from non-critical ones, focusing on areas impacting patient safety, product quality, and data integrity [16] scilife.io. A solid understanding of how a system supports the pharma process allows teams to make risk-based decisions and ensure the system is truly suitable for its intended use. 2. Lifecycle Approach within a Quality System: GAMP 5 adopts a system lifecycle model for computerized systems, from initial concept through retirement [17] scilife.io. Rather than treating validation as a one-time event, it must be an ongoing process that covers requirements definition, design, development, testing, deployment, operation, and eventual decommissioning. All these stages occur under a pharmaceutical quality management system to maintain control. Following a lifecycle approach ensures that validation and quality assurance activities are planned and executed at every critical phase [18] linkedin.com. 3. Scalable Validation Effort: One principle of GAMP 5 is that the rigor of the lifecycle activities should be scalable to the size and risk of the system [19] scilife.io. In other words, the approach is not “one-size-fits-all.” Simple or low-risk systems may need a leaner validation process, whereas complex or high-risk systems require more extensive effort. GAMP 5 allows using different development models (Waterfall, V-model, Agile, etc.) or even reduced/extended lifecycle models as appropriate [20] scilife.io. This scalability ensures efficient use of resources – applying just enough validation to meet compliance and control risk without unnecessary bureaucracy. 4. Science-Based Quality Risk Management: Consistent with ICH Q9 principles, GAMP 5 places risk management at the forefront. Manufacturers are expected to identify and assess risks to patient safety, product quality, and data integrity, and use those risk assessments to prioritize validation testing and controls [21] scilife.io scilife.io. In practice, this means dedicating the most effort to critical functions of a system (those that, if they failed, could potentially impact product quality or compliance) and applying lighter effort to non-critical features. By focusing on risk, GAMP 5 helps avoid spending excessive time on documentation of inconsequential details, instead advocating “more testing over more documentation” for what truly matters [23] scilife.io. For example, rather than capturing voluminous screenshots for every test step, GAMP 5 suggests using exception-based recording (logging only deviations or unexpected outcomes) for routine tests, while thoroughly investigating and evidencing any issues that arise [23] scilife.io. This risk-based approach improves efficiency and effectiveness of validation. 5. Leveraging Supplier Involvement: Modern pharmaceutical systems often rely on third-party software or services. GAMP 5 advises organizations to leverage vendor activities and documentation wherever possible [24] scilife.io. Suppliers (e.g. software developers or equipment manufacturers) typically provide their own testing, quality certificates, and technical documentation. Rather than duplicating all of this work, regulated companies should make use of supplier documentation and expertise as part of their validation strategy – after performing appropriate supplier assessments. This principle is closely tied to current industry practices of auditing and qualifying vendors. By integrating supplier-provided validation evidence (like vendor test results or compliance certificates), companies can streamline their CSV efforts while still meeting GAMP and regulatory standards [25] scilife.io. Lifecycle Approach: One of the cornerstone concepts in GAMP is managing the computerized system’s entire lifecycle. GAMP 5 defines lifecycle phases such as Concept, Project, Operation, and Retirement[17] scilife.io [26] scilife.io. In the Concept phase, a regulated company identifies a need or opportunity for automation and defines initial requirements at a high level (this phase is often before a specific solution is chosen) [26] scilife.io. Next is the Project phase, where the system is specified in detail, built or configured, and verified (tested) against specifications prior to going live [26] scilife.io. Following deployment, the system enters the Operational phase – this is typically the longest phase, covering day-to-day use in production. During Operation, the focus is on maintaining the validated state through change control, periodic reviews, incident management, and routine re-validation as needed [26] scilife.io. Finally, the Retirement phase ensures that when a system is IntuitionLabs - Custom AI Software Development from the leading AI expert Adrien Laurent GAMP 5: Computerized System Validation in Pharma © 2025 IntuitionLabs.ai - North America's Leading AI Software Development Firm for Pharmaceutical & Biotech. All rights reserved.", "sources": [ "gamp-5-computerized-system-validation-in-pharma.pdf" ], "file": "gamp-5-computerized-system-validation-in-pharma.pdf", "type": "pdf" }, { "term": "Page 3 of 13", "definition": "decommissioned, it is done in a controlled manner (including proper data archival and verification that the replacement system is validated if applicable). Historically, GAMP has often been illustrated by the “V-model” – a visual representation of the development and validation lifecycle that links each stage on the “left” (requirements and design) to a corresponding testing activity on the “right” [27] qbdgroup.com. The V-model shows how the process flows from user requirements down to system configuration/build, and then back up to user acceptance testing, forming a V-shaped sequence [28] qbdgroup.com [29] qbdgroup.com. For example, user requirements defined at the start must be verified by acceptance testing at the end; functional specifications are verified by functional testing; and so forth, ensuring traceability between what was planned and what was tested [30] qbdgroup.com [29] qbdgroup.com. This model also incorporates Installation Qualification (IQ) to verify the system is installed correctly, Operational Qualification (OQ) to verify it performs as intended under various conditions, and Performance Qualification (PQ) to confirm the system meets user needs in the actual operational environment [29] qbdgroup.com. The V- model (a form of waterfall methodology) was explicitly recommended in the original GAMP 5; however, the 2022 Second Edition acknowledges that many projects now use iterative or Agile development approaches [31] scilife.io. GAMP 5 now clarifies that its principles can be applied in a non-linear fashion as well – the key is still maintaining rigor and traceability, even if the software is released in rapid, incremental cycles [31] scilife.io. In all cases, quality risk management accompanies the lifecycle: GAMP 5 aligns with the idea that risk assessments should guide the depth of validation at each stage, consistent with EU and FDA expectations that risk management be applied throughout a system’s life health.ec.europa.eu. Figure: The GAMP 5 “V-model” illustrates the system development lifecycle and its corresponding validation steps. On the left side, user requirements and system specifications are defined by the user (regulated company), while on the right side, various testing and verification activities ensure those requirements are met in the implemented system [30] qbdgroup.com [29] qbdgroup.com. This model underscores traceability: every requirement must be tested, and every test ties back to a specified requirement. Risk management and quality oversight (not shown explicitly in the diagram) underpin each phase of the V-model, aligning with GAMP 5’s risk-based approach. GAMP 5 and Regulatory Compliance (FDA 21 CFR Part 11 and EU Annex 11) A major reason GAMP 5 is so valued in the pharmaceutical industry is its close alignment with regulatory compliance requirements. Pharmaceutical regulators like the U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) mandate control over computerized systems used in GMP environments. GAMP 5 provides a practical roadmap to fulfill these mandates. FDA 21 CFR Part 11: In the United States, 21 CFR Part 11 is the FDA regulation governing electronic records and electronic signatures. Part 11 requires that when companies use digital systems to record GMP data, they must implement certain controls to ensure those records are trustworthy and equivalent to paper records. A foundational requirement of Part 11 is system validation. Specifically, 21 CFR §11.10(a) states that persons who use computerized systems to create or maintain electronic records must validate those systems to ensure accuracy, reliability, consistent intended performance, and the ability to discern invalid or altered records[32] ecfr.gov. Part 11 also calls for technical controls such as secure, computer-generated audit trails that record changes to data (with timestamps and user IDs) [33] ecfr.gov, as well as system access controls, electronic signatures, and record retention mechanisms [33] ecfr.gov. GAMP 5’s entire philosophy of lifecycle validation directly addresses §11.10(a) – by following GAMP’s guidance, firms can produce the documented evidence that a system does what it purports to do and can detect any data alterations. In fact, adopting GAMP 5 makes it easier to comply with diverse regulations, including Part 11, because it integrates requirements like IntuitionLabs - Custom AI Software Development from the leading AI expert Adrien Laurent GAMP 5: Computerized System Validation in Pharma © 2025 IntuitionLabs.ai - North America's Leading AI Software Development Firm for Pharmaceutical & Biotech. All rights reserved.", "sources": [ "gamp-5-computerized-system-validation-in-pharma.pdf" ], "file": "gamp-5-computerized-system-validation-in-pharma.pdf", "type": "pdf" }, { "term": "Page 4 of 13", "definition": "audit trails and security into the validation lifecycle [34] tricentis.com. As an illustration of why this matters, FDA inspectors have issued warning letters to companies for failing to validate their software and implement audit trails. For example, one 2022 FDA warning letter noted a firm’s production software “was not validated and lacked audit trails,” meaning the company could not ensure data integrity or traceability in batch records [35] fda.gov. Such violations lead to regulatory enforcement actions. Using GAMP 5 would help prevent these issues by ensuring validation plans, test protocols, and controls for data integrity (like unique user accounts and audit logging) are in place as part of the system lifecycle. EU GMP Annex 11: In the European Union, the primary guidance for computerized systems is EU GMP Annex 11 (Computerised Systems), which is an annex to the EU GMP guidelines. Annex 11 explicitly requires that all computerized systems used in GMP processes be validated and that IT infrastructure be qualified health.ec.europa.eu. The document emphasizes applying risk management throughout the system’s lifecycle, taking into account patient safety, data integrity, and product qualityhealth.ec.europa.eu – a statement that closely mirrors GAMP 5 principles. Annex 11 is structured to cover project-phase controls (like validation planning, user requirements, and supplier assessment) as well as operational-phase controls (such as data accuracy checks, incident management, change control, periodic review, security, and audit trails) [36] scilife.io. By following GAMP 5, companies inherently address these areas: for example, GAMP’s supplier management principle aligns with Annex 11’s requirement to assess vendor reliability and have formal agreements with suppliers health.ec.europa.eu. GAMP’s focus on documentation and lifecycle validation corresponds to Annex 11’s mandate that validation documentation cover relevant lifecycle steps and that companies justify their approach based on risk assessment health.ec.europa.eu. In summary, GAMP 5 operationalizes the expectations of Annex 11. Regulators in the EU expect firms to have controlled computerized systems, and an inspector will often recognize GAMP-based validation packages as evidence of compliance. While GAMP 5 itself is not legally binding, both FDA and EU regulators reference it in guidance and training, and following GAMP is considered an effective way to demonstrate compliance with 21 CFR Part 11, EU Annex 11, and related regulations[37] scilife.io [38] scilife.io. It should be noted that GAMP 5 also complements other regulatory and industry standards beyond Part 11/Annex 11. For instance, it aligns with ICH Q9 (Quality Risk Management) for risk-based decision making, and with data integrity guidance (such as FDA and MHRA data integrity guidelines) by providing a systematic way to ensure data are complete, consistent, and accurate throughout the system’s life. The second edition of GAMP 5 even references FDA’s newer Computer Software Assurance (CSA) initiative, which similarly encourages critical thinking and risk-based assurance rather than brute-force testing of everything [39] ispe.org. Overall, implementing GAMP 5 helps a company create compliant computerized systems that can stand up to regulatory scrutiny in any jurisdiction. Applying GAMP 5 to Computerized System Validation (CSV) Computerized System Validation (CSV) is the process of planning, testing, and documenting that a computer- based system will do what it is intended to do in a consistent and reliable manner. In pharmaceutical manufacturing and quality operations, CSV is essential because any software or automated system that affects GMP data or product quality must be validated to ensure it consistently meets requirements. GAMP 5 provides a structured methodology to perform CSV effectively. When applying GAMP 5 to CSV, the process typically includes: validation planning, requirements definition, risk assessment, system configuration/build, verification testing, and ongoing control. All of these correspond to the V-model and lifecycle stages discussed earlier. Under GAMP, the CSV effort starts with a Validation Plan that defines the scope and strategy for the system’s validation [40] qbdgroup.com. This plan is informed by risk: critical functionality will require more rigorous validation. Next, User Requirements Specifications (URS) are developed to state exactly what the users (e.g. manufacturing or lab personnel) need IntuitionLabs - Custom AI Software Development from the leading AI expert Adrien Laurent GAMP 5: Computerized System Validation in Pharma © 2025 IntuitionLabs.ai - North America's Leading AI Software Development Firm for Pharmaceutical & Biotech. All rights reserved.", "sources": [ "gamp-5-computerized-system-validation-in-pharma.pdf" ], "file": "gamp-5-computerized-system-validation-in-pharma.pdf", "type": "pdf" }, { "term": "Page 5 of 13", "definition": "the system to do [41] qbdgroup.com. GAMP 5 emphasizes that requirements should cover not only functional needs but also regulatory and data integrity needs (for example, requirements for audit trail functionality, electronic signatures, and report generation can be included to meet compliance) [41] qbdgroup.com. A traceability matrix is usually created to map each requirement through the design and testing process, ensuring nothing is missed [42] qbdgroup.com. System design and configuration (whether it’s software coding or system setup) follows, and supplier involvement is key here – if it’s a third-party software, much of the configuration and unit testing may be done by the supplier according to their quality system. GAMP 5, as noted, allows leveraging that work [25] scilife.io. Once the system is built or configured, verification (testing) is performed to prove the system meets all specifications and user needs. This typically includes IQ/OQ/PQ testing: verifying the installation, challenging the system’s functions under various conditions (operational tests), and confirming it performs correctly in a simulated or actual production scenario (performance qualification) [29] qbdgroup.com. GAMP 5 encourages focusing test efforts on the most critical requirements identified via risk assessment [43] scilife.io. For example, if a system manages critical calculations for a production process, the CSV tests will rigorously verify those calculations (including edge cases), whereas less critical features (like a minor user interface preference) might not need exhaustive testing. The goal is to demonstrate with evidence that all high-risk functions work correctly and that the system as a whole is reliable and secure. Importantly, GAMP 5 extends CSV into the operational phase. After the initial validation and go-live, the system must be kept in a validated state. This means establishing standard operating procedures (SOPs) for system use, training users, instituting change control for any updates (so changes are assessed for risk and the system is re-validated as needed), performing periodic reviews to ensure the system remains compliant as regulations or business needs change, and having incident management procedures for any errors or deviations encountered during use [44] scilife.io health.ec.europa.eu. GAMP’s guidance is that validation is not a one-time checklist, but an ongoing assurance process over the system’s life. For instance, if a software patch is applied or a new feature is configured, a risk-based assessment should determine what level of re-testing or documentation update is required. Another practical element of GAMP-based CSV is software category classification. GAMP 5 classifies software systems into different categories based on their complexity and how they are created: from Category 1 (infrastructure software like operating systems), through standardized configurable packages, up to Category 5 (custom-developed applications) [45] linkedin.com [46] scilife.io. These categories help determine the scope of validation. A Category 3 system (commercial off-the-shelf product used with no customizations) might be validated primarily by verifying proper installation and basic functionality, whereas a Category 5 bespoke system demands a full specification and extensive testing effort. Category definitions were slightly streamlined in GAMP 5 (for example, a category for firmware was removed) but the principle remains that the more novel or custom a system is, the more validation work is expected[46] scilife.io. This is another way GAMP 5 keeps CSV efforts commensurate with risk and complexity. In summary, applying GAMP 5 to CSV means following a lifecycle, risk-based process to attain and maintain system compliance. It provides templates for what documents to produce (like validation plans, requirements specs, test protocols, reports), but it gives flexibility to tailor these to each project’s needs. The end result of a GAMP 5 CSV is a body of evidence demonstrating the system is effective, of high quality, and compliant with GMP regulations[2] ispe.org. This not only satisfies auditors and inspectors, but also gives the pharmaceutical company confidence that their automated processes (whether it’s an equipment control system, a laboratory information management system, or an ERP module handling production records) will consistently work as intended. Examples and Case Studies of GAMP 5 in Practice IntuitionLabs - Custom AI Software Development from the leading AI expert Adrien Laurent GAMP 5: Computerized System Validation in Pharma © 2025 IntuitionLabs.ai - North America's Leading AI Software Development Firm for Pharmaceutical & Biotech. All rights reserved.", "sources": [ "gamp-5-computerized-system-validation-in-pharma.pdf" ], "file": "gamp-5-computerized-system-validation-in-pharma.pdf", "type": "pdf" }, { "term": "Page 6 of 13", "definition": "The principles of GAMP 5 may sound abstract, but they have very tangible applications in pharmaceutical manufacturing and quality systems. Many organizations have documented improvements in compliance and efficiency by using GAMP 5 for validation. Below are a few illustrative examples and case studies: Manufacturing Equipment Software – Risk-Based Validation: One case study comes from IMA Active, a manufacturer of automated capsule fillers and tablet press machines for pharma production. IMA Active applied GAMP 5 principles when developing new control software for two of its machines (a tablet press and a lab-scale granulator). The company recognized that increasing automation meant software had become a critical component for ensuring product quality and patient safety. Using GAMP 5’s risk-based approach, they identified which functions of the machine software were most critical to product quality (for example, dose accuracy, process parameter limits, data recording) and focused validation efforts on those areas ima.it ima.it. They performed a formal risk assessment to determine potential failure points, then devised targeted verification tests and controls to mitigate those risks ima.it ima.it. By leveraging a multi-disciplinary team – including Quality Assurance, software engineers, and even academic experts – the company ensured the software development and verification process met both GAMP 5 guidelines and regulatory expectations. In the end, the software was classified as a GAMP Category 3 (non-configured off-the-shelf) system, because the rigorous process ensured standardization and controlled changes ima.it. This case demonstrates how following GAMP 5 can lead to a well- documented, risk-focused validation, yielding software that reliably supports GMP processes. IMA Active reported that this approach allowed them to meet customer and regulatory requirements while avoiding excessive testing where it wasn’t needed, illustrating GAMP 5’s efficiency benefits in a real project. Quality Management Systems (QMS) – Ensuring Data Integrity: GAMP 5 is also commonly applied to corporate quality systems such as electronic document management systems or deviation tracking systems. For instance, a pharmaceutical company implementing a new Electronic Quality Management System (eQMS) used GAMP 5 to validate the platform. They began by defining user requirements that included not just functional needs (like routing of approvals, audit trail for changes, electronic signatures for approvals) but also compliance needs (e.g., 21 CFR Part 11 technical controls). By using supplier-provided documentation from the QMS vendor (leveraging the GAMP principle of supplier involvement), the company could concentrate its internal testing on how it configured the tool for its own processes. The risk assessment highlighted critical requirements such as audit trail accuracy and permission settings for users. During OQ/PQ testing, the team therefore rigorously challenged those aspects – for example, verifying that the audit trail logs every significant action with timestamp and user ID, and that only authorized roles could perform certain critical tasks. Less critical configuration options (like cosmetic interface settings) were tested minimally. This targeted approach aligns with GAMP 5 guidance and satisfies both FDA and EU inspectors’ focus on data integrity. Indeed, data integrity is a major theme in recent regulatory guidance, and GAMP 5 validation directly supports data integrity by ensuring the system has the controls to produce accurate, reliable, and traceable data[47] europeanpharmaceuticalreview.com [48] europeanpharmaceuticalreview.com. The outcome for the company was a smooth regulatory inspection where the validated QMS was praised for its robust audit trails and documentation — a direct payoff of following GAMP 5 best practices. Manufacturing Execution Systems (MES) – Comprehensive CSV: Another example is the use of GAMP 5 in validating a Manufacturing Execution System at a pharmaceutical production site. An MES coordinates workflows on the shop floor (batch recipes, equipment interfaces, real-time monitoring, electronic batch records). Given the central role of an MES in GMP compliance, a company used GAMP 5 to structure the validation during a major MES upgrade. They wrote detailed user requirements covering every GMP-critical function, from recipe management to electronic signatures on batch record steps. Using the GAMP 5 V-model, they linked each requirement to tests: for example, the requirement “ensure only authorized operators can start a batch” was linked to a security role test in OQ, and the requirement “calculate component weights with 2-decimal precision” was linked to a series of test cases verifying calculation accuracy and rounding rules. The project team categorized the software components of the MES (custom interfaces were treated as higher risk than the base platform which was a proven product). When an FDA inspector later audited the facility, they examined the validation package and found that it covered all life-cycle stages with traceability and had risk justifications for the level of testing performed on each function. This level of thoroughness, which is advocated by GAMP 5, provided confidence to the regulators that the MES was under control and that electronic batch records could be trusted. In addition to positive implementations, it’s worth noting that failing to follow GAMP 5 (or similar rigorous CSV practices) can lead to serious compliance issues. Many FDA warning letters and EU inspection findings in recent years have involved companies who did not adequately validate their computerized systems or ensure data integrity. Common violations include using uncontrolled spreadsheets for critical calculations, lack of audit trail on laboratory data systems, shared user accounts in manufacturing systems, and so on [49] fda.gov [50] fda.gov. Each of these corresponds to a principle in GAMP 5: for example, GAMP 5 would mandate unique user IntuitionLabs - Custom AI Software Development from the leading AI expert Adrien Laurent GAMP 5: Computerized System Validation in Pharma © 2025 IntuitionLabs.ai - North America's Leading AI Software Development Firm for Pharmaceutical & Biotech. All rights reserved.", "sources": [ "gamp-5-computerized-system-validation-in-pharma.pdf" ], "file": "gamp-5-computerized-system-validation-in-pharma.pdf", "type": "pdf" }, { "term": "Page 7 of 13", "definition": "access and audit trails for GMP systems (addressing those data integrity gaps) [49] fda.gov [50] fda.gov. By studying such cases, pharma companies have learned that adopting GAMP 5 is not just about avoiding citations; it also improves their operational reliability. As one industry paper noted, GAMP 5 provides a “significant reduction in the risk of errors and ensures compliance with regulatory standards” in automated systems [47] europeanpharmaceuticalreview.com [48] europeanpharmaceuticalreview.com. The framework essentially acts as a quality assurance blueprint for any GMP computerized system.", "sources": [ "gamp-5-computerized-system-validation-in-pharma.pdf" ], "file": "gamp-5-computerized-system-validation-in-pharma.pdf", "type": "pdf" }, { "term": "Conclusion", "definition": "GAMP 5 has proven to be a cornerstone of pharmaceutical automation compliance. In summary, it is a widely- used framework (originally developed by ISPE) that guides companies on how to validate and manage computerized systems in line with GMP regulations. GAMP 5’s key principles – from lifecycle management and risk-based validation to leveraging supplier documentation – help firms ensure their systems are not only compliant on paper but also effective and reliable in practice. By applying GAMP 5, organizations build quality into the design and operation of their manufacturing IT systems, which in turn protects patient safety, ensures high product quality, and maintains the integrity of data across all digital records [2] ispe.org [51] ispe.org. Its alignment with regulatory requirements like FDA 21 CFR Part 11 and EU Annex 11 makes it an invaluable tool for achieving and demonstrating compliance during inspections. Importantly, GAMP 5 is practical – it scales efforts to the risk at hand and encourages critical thinking over check-box compliance, echoing modern regulatory views. For pharmaceutical professionals, understanding GAMP 5 is essential in today’s environment of increasing automation and data integrity scrutiny. Whether one is implementing a new laboratory information system, updating a production line with advanced robotics, or maintaining an existing ERP for batch release, GAMP 5 provides the methodology to validate that system with confidence. The case studies and examples show that GAMP 5 is not just theoretical: it helps avoid costly mistakes and streamlines validation projects. In an industry where technology continuously evolves (cloud computing, AI, etc.), GAMP 5 (especially with its updated 2nd Edition) will continue to evolve as well, ensuring that good automated manufacturing practices keep pace with innovation. Adhering to GAMP 5 fosters a culture of quality and compliance that ultimately benefits not only the company but also the patients who rely on safe, effective medicines produced under these high standards. Sources: 1. ISPE, GAMP 5 Guide 2nd Edition (2022) – Official ISPE guidance description [2] ispe.org [52] ispe.org. 2. Wyn, Sion & Clark, Chris. “What You Need to Know About GAMP® 5 Guide, 2nd Edition.” Pharmaceutical Engineering, Jan/Feb 2023 – Overview of updates and continued importance of GAMP 5 [10] ispe.org [51] ispe.org. 3. Jeyaraman, R. “Understanding Good Automated Manufacturing Practices (GAMP): A Pillar of Pharmaceutical Integrity.” LinkedIn Article, Nov 17, 2024 – History of GAMP and key concepts [4] linkedin.com [9] linkedin.com. 4. Deshpande, R. “GAMP 5 for GxP Compliant Computerized Systems.” Scilife Blog, Feb 20, 2025 – Explanation of GAMP 5 principles and lifecycle (including Agile updates) [17] scilife.io [21] scilife.io. 5. Tricentis. “Compliance with GAMP 5 guidance: A checklist.” Tricentis Blog – GAMP 5 overview and principles, benefits for compliance [15] tricentis.com [1] tricentis.com. 6. FDA 21 CFR Part 11 (Electronic Records/Electronic Signatures) – §11.10(a) requiring system validation; §11.10(e) requiring audit trails [32] ecfr.gov [33] ecfr.gov. 7. European Commission EudraLex Vol.4 Annex 11 (Computerised Systems), 2011 – Principle of Annex 11 (validation and risk management requirements) health.ec.europa.eu health.ec.europa.eu. IntuitionLabs - Custom AI Software Development from the leading AI expert Adrien Laurent GAMP 5: Computerized System Validation in Pharma © 2025 IntuitionLabs.ai - North America's Leading AI Software Development Firm for Pharmaceutical & Biotech. All rights reserved.", "sources": [ "gamp-5-computerized-system-validation-in-pharma.pdf" ], "file": "gamp-5-computerized-system-validation-in-pharma.pdf", "type": "pdf" }, { "term": "Page 8 of 13", "definition": "8. FDA Warning Letter to Colorful Products Corp (May 10, 2022) – Example of enforcement for unvalidated software lacking audit trails [35] fda.gov. 9. European Pharmaceutical Review, “Role of GAMP 5, data integrity and QbD in QA” (March 2024) – Noting GAMP 5 as a widely used validation framework that reduces errors and ensures compliance [47] europeanpharmaceuticalreview.com [48] europeanpharmaceuticalreview.com. 10. IMA Active Case Study (2021). “Achieving and maintaining GAMP 5 compliance: risk-based approach to software development” – Example of applying GAMP 5 in equipment software validation ima.it ima.it.", "sources": [ "gamp-5-computerized-system-validation-in-pharma.pdf" ], "file": "gamp-5-computerized-system-validation-in-pharma.pdf", "type": "pdf" }, { "term": "External Sources", "definition": "[1] https://www.tricentis.com/learn/compliance-with-gamp-5-guidance-a-checklist#:~:GAMP%... [2] https://ispe.org/publications/guidance-documents/gamp-5-guide-2nd-edition#:~:GAMP%... [3] https://www.linkedin.com/pulse/understanding-good-automated-manufacturing-practices-gamp-jeyaraman-8xasc#:~: GAMP%... [4] https://www.linkedin.com/pulse/understanding-good-automated-manufacturing-practices-gamp-jeyaraman-8xasc#:~: GAMP%... [5] https://www.linkedin.com/pulse/understanding-good-automated-manufacturing-practices-gamp-jeyaraman-8xasc#:~: 1,val... [6] https://www.linkedin.com/pulse/understanding-good-automated-manufacturing-practices-gamp-jeyaraman-8xasc#:~: compu... [7] https://www.linkedin.com/pulse/understanding-good-automated-manufacturing-practices-gamp-jeyaraman-8xasc#:~: 2,bas... [8] https://www.linkedin.com/pulse/understanding-good-automated-manufacturing-practices-gamp-jeyaraman-8xasc#:~: 3,inc... [9] https://www.linkedin.com/pulse/understanding-good-automated-manufacturing-practices-gamp-jeyaraman-8xasc#:~: defin... [10] https://ispe.org/pharmaceutical-engineering/january-february-2023/what-you-need-know-about-gampr-5-guide-2nd- edition#:~:ISPE%... [11] https://ispe.org/pharmaceutical-engineering/january-february-2023/what-you-need-know-about-gampr-5-guide-2nd- edition#:~:Guida... [12] https://ispe.com/pharmaceutical-engineering/january-february-2023/what-you-need-know-about-gampr-5-guide-2nd -edition#:~:GAMP%... [13] https://ispe.org/pharmaceutical-engineering/january-february-2023/what-you-need-know-about-gampr-5-guide-2nd- edition#:~:Since... [14] https://ispe.org/pharmaceutical-engineering/january-february-2023/what-you-need-know-about-gampr-5-guide-2nd- edition#:~:One%2... [15] https://www.tricentis.com/learn/compliance-with-gamp-5-guidance-a-checklist#:~:GAMP%... [16] https://www.scilife.io/blog/gamp5-for-gxp-compliant-computerized-systems#:~:The%2... IntuitionLabs - Custom AI Software Development from the leading AI expert Adrien Laurent GAMP 5: Computerized System Validation in Pharma © 2025 IntuitionLabs.ai - North America's Leading AI Software Development Firm for Pharmaceutical & Biotech. All rights reserved.", "sources": [ "gamp-5-computerized-system-validation-in-pharma.pdf" ], "file": "gamp-5-computerized-system-validation-in-pharma.pdf", "type": "pdf" }, { "term": "Page 9 of 13", "definition": "[17] https://www.scilife.io/blog/gamp5-for-gxp-compliant-computerized-systems#:~:Accor... [18] https://www.linkedin.com/pulse/understanding-good-automated-manufacturing-practices-gamp-jeyaraman-8xasc#:~: 1,tes... [19] https://www.scilife.io/blog/gamp5-for-gxp-compliant-computerized-systems#:~:Key%2... [20] https://www.scilife.io/blog/gamp5-for-gxp-compliant-computerized-systems#:~:compu... [21] https://www.scilife.io/blog/gamp5-for-gxp-compliant-computerized-systems#:~:Key%2... [22] https://www.scilife.io/blog/gamp5-for-gxp-compliant-computerized-systems#:~:Anoth... [23] https://www.scilife.io/blog/gamp5-for-gxp-compliant-computerized-systems#:~:Anoth... [24] https://www.scilife.io/blog/gamp5-for-gxp-compliant-computerized-systems#:~:Key%2... 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[34] https://www.tricentis.com/learn/compliance-with-gamp-5-guidance-a-checklist#:~:,comp... [35] https://www.fda.gov/inspections-compliance-enforcement-and-criminal-investigations/warning-letters/colorful-produc ts-corporation-624415-05102022#:~:Your%... [36] https://www.scilife.io/blog/differences-21cfrpart11-annex11#:~:,rele... [37] https://www.scilife.io/blog/gamp5-for-gxp-compliant-computerized-systems#:~:The%2... [38] https://www.scilife.io/blog/gamp5-for-gxp-compliant-computerized-systems#:~:Inten... [39] https://ispe.org/pharmaceutical-engineering/january-february-2023/what-you-need-know-about-gampr-5-guide-2nd- edition#:~:based... [40] https://www.qbdgroup.com/en/blog/what-is-the-gamp-5-v-model-in-computerized-system-validation/#:~:1... [41] https://www.qbdgroup.com/en/blog/what-is-the-gamp-5-v-model-in-computerized-system-validation/#:~:,risk... [42] https://www.qbdgroup.com/en/blog/what-is-the-gamp-5-v-model-in-computerized-system-validation/#:~:The%2... [43] https://www.scilife.io/blog/gamp5-for-gxp-compliant-computerized-systems#:~:The%2... [44] https://www.scilife.io/blog/differences-21cfrpart11-annex11#:~:phase... [45] https://www.linkedin.com/pulse/understanding-good-automated-manufacturing-practices-gamp-jeyaraman-8xasc#:~: 3,bui... [46] https://www.scilife.io/blog/gamp5-for-gxp-compliant-computerized-systems#:~:GAMP%... [47] https://www.europeanpharmaceuticalreview.com/news/218579/the-role-of-gamp-5-data-integrity-and-qbd-in-pharma ceutical-quality-assurance/#:~:Pedro... IntuitionLabs - Custom AI Software Development from the leading AI expert Adrien Laurent GAMP 5: Computerized System Validation in Pharma © 2025 IntuitionLabs.ai - North America's Leading AI Software Development Firm for Pharmaceutical & Biotech. All rights reserved.", "sources": [ "gamp-5-computerized-system-validation-in-pharma.pdf" ], "file": "gamp-5-computerized-system-validation-in-pharma.pdf", "type": "pdf" }, { "term": "Page 10 of 13", "definition": "[48] https://www.europeanpharmaceuticalreview.com/news/218579/the-role-of-gamp-5-data-integrity-and-qbd-in-pharma ceutical-quality-assurance/#:~:5%20b... [49] https://www.fda.gov/inspections-compliance-enforcement-and-criminal-investigations/warning-letters/colorful-produc ts-corporation-624415-05102022#:~:In%20... [50] https://www.fda.gov/inspections-compliance-enforcement-and-criminal-investigations/warning-letters/colorful-produc ts-corporation-624415-05102022#:~:indiv... [51] https://ispe.org/pharmaceutical-engineering/january-february-2023/what-you-need-know-about-gampr-5-guide-2nd- edition#:~:GAMP%... [52] https://ispe.org/publications/guidance-documents/gamp-5-guide-2nd-edition#:~:Maint... IntuitionLabs - Custom AI Software Development from the leading AI expert Adrien Laurent GAMP 5: Computerized System Validation in Pharma © 2025 IntuitionLabs.ai - North America's Leading AI Software Development Firm for Pharmaceutical & Biotech. All rights reserved.", "sources": [ "gamp-5-computerized-system-validation-in-pharma.pdf" ], "file": "gamp-5-computerized-system-validation-in-pharma.pdf", "type": "pdf" }, { "term": "Page 11 of 13", "definition": "IntuitionLabs - Industry Leadership & Services North America's #1 AI Software Development Firm for Pharmaceutical & Biotech: IntuitionLabs leads the US market in custom AI software development and pharma implementations with proven results across public biotech and pharmaceutical companies. Elite Client Portfolio: Trusted by NASDAQ-listed pharmaceutical companies including Scilex Holding Company (SCLX) and leading CROs across North America. Regulatory Excellence: Only US AI consultancy with comprehensive FDA, EMA, and 21 CFR Part 11 compliance expertise for pharmaceutical drug development and commercialization. Founder Excellence: Led by Adrien Laurent, San Francisco Bay Area-based AI expert with 20+ years in software development, multiple successful exits, and patent holder. Recognized as one of the top AI experts in the USA. Custom AI Software Development: Build tailored pharmaceutical AI applications, custom CRMs, chatbots, and ERP systems with advanced analytics and regulatory compliance capabilities. Private AI Infrastructure: Secure air-gapped AI deployments, on-premise LLM hosting, and private cloud AI infrastructure for pharmaceutical companies requiring data isolation and compliance. Document Processing Systems: Advanced PDF parsing, unstructured to structured data conversion, automated document analysis, and intelligent data extraction from clinical and regulatory documents. 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Contact founder Adrien Laurent and team at https://intuitionlabs.ai/contact for a consultation. IntuitionLabs - Custom AI Software Development from the leading AI expert Adrien Laurent GAMP 5: Computerized System Validation in Pharma © 2025 IntuitionLabs.ai - North America's Leading AI Software Development Firm for Pharmaceutical & Biotech. All rights reserved.", "sources": [ "gamp-5-computerized-system-validation-in-pharma.pdf" ], "file": "gamp-5-computerized-system-validation-in-pharma.pdf", "type": "pdf" }, { "term": "DISCLAIMER", "definition": "The information contained in this document is provided for educational and informational purposes only. We make no representations or warranties of any kind, express or implied, about the completeness, accuracy, reliability, suitability, or availability of the information contained herein. Any reliance you place on such information is strictly at your own risk. 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IntuitionLabs - Custom AI Software Development from the leading AI expert Adrien Laurent GAMP 5: Computerized System Validation in Pharma © 2025 IntuitionLabs.ai - North America's Leading AI Software Development Firm for Pharmaceutical & Biotech. All rights reserved.", "sources": [ "gamp-5-computerized-system-validation-in-pharma.pdf" ], "file": "gamp-5-computerized-system-validation-in-pharma.pdf", "type": "pdf" }, { "term": "Official address Domenico Scarlattilaan 6 ● 1083 HS Amsterdam ● The Netherlands", "definition": "An agency of the European Union Address for visits and deliveries Refer to www.ema.europa.eu/how-to-find-us Send us a question Go to www.ema.europa.eu/contact Telephone +31 (0)88 781 6000 © European Medicines Agency, 2023. Reproduction is authorised provided the source is acknowledged. 9 March 2023 EMA/INS/GCP/112288/2023 Good Clinical Practice Inspectors Working Group (GCP IWG) Guideline on computerised systems and electronic data in", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "clinical trials", "definition": "Adopted by GCP IWG for release for consultation 4 March 2021 Start of public consultation 18 June 2021 End of consultation (deadline for comments) 17 December 2021 Final version adopted by the GCP IWG 7 March 2023 Date of coming into effect 6 months after publication This guideline replaces the 'Reflection paper on expectations for electronic source data and data transcribed to electronic data collection tools in clinical trials' (EMA/INS/GCP/454280/2010).", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Keywords", "definition": "Computerised systems, electronic data, validation, audit trail, user management, security, electronic clinical outcome assessment (eCOA), interactive response technology (IRT), case report form (CRF), electronic signatures, artificial intelligence (AI) Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 2/52 Guideline on computerised systems and electronic data in", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Glossary ...................................................................................................... 5", "definition": "Abbreviations .............................................................................................. 7 Executive summary ..................................................................................... 8 1. Introduction ............................................................................................ 8 2. Scope....................................................................................................... 9 3. Legal and regulatory background .......................................................... 10 4. Principles and definition of key concepts ............................................... 10 4.1. Data integrity .................................................................................................... 10 4.2. Responsibilities .................................................................................................. 11 4.3. Data and metadata ............................................................................................. 11 4.4. Source data ....................................................................................................... 11 4.5. ALCOA++ principles ........................................................................................... 12 4.6. Criticality and risks ............................................................................................. 13 4.7. Data capture ...................................................................................................... 14 4.8. Electronic signatures ........................................................................................... 15 4.9. Data protection .................................................................................................. 16 4.10. Validation of systems ........................................................................................ 16 4.11. Direct access.................................................................................................... 16 5. Computerised systems .......................................................................... 17 5.1. Description of systems ........................................................................................ 17 5.2. Documented procedures ...................................................................................... 17 5.3. Training ............................................................................................................ 17 5.4. Security and access control ................................................................................. 17 5.5. Timestamp ........................................................................................................ 18 6. Electronic data....................................................................................... 18 6.1. Data capture and location .................................................................................... 18 6.1.1. Transcription ................................................................................................... 18 6.1.2. Transfer ......................................................................................................... 18 6.1.3. Direct capture ................................................................................................. 19 6.1.4. Edit checks ..................................................................................................... 19 6.2. Audit trail and audit trail review ........................................................................... 19 6.2.1. Audit trail ....................................................................................................... 19 6.2.2. Audit trail review ............................................................................................. 20 6.3. Sign-off of data .................................................................................................. 21 6.4. Copying data ..................................................................................................... 21 6.5. Certified copies .................................................................................................. 22 6.6. Control of data ................................................................................................... 22 6.7. Cloud solutions .................................................................................................. 23 Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 3/52 6.8. Backup of data ................................................................................................... 24 6.9. Contingency plans .............................................................................................. 24 6.10. Migration of data .............................................................................................. 24 6.11. Archiving ......................................................................................................... 25 6.12. Database decommissioning ................................................................................ 25 Annex 1 Agreements ................................................................................. 27 Annex 2 Computerised systems validation ................................................ 30 A2.1 General principles .............................................................................................. 30 A2.2 User requirements ............................................................................................. 31 A2.3 Trial specific configuration and customisation ........................................................ 31 A2.4 Traceability of requirements ............................................................................... 31 A2.5 Validation and test plans .................................................................................... 31 A2.6 Test execution and reporting............................................................................... 32 A2.7 Release for production ....................................................................................... 32 A2.8 User helpdesk ................................................................................................... 32 A2.9 Periodic review .................................................................................................. 33 A2.10 Change control ................................................................................................ 33 Annex 3 User management ........................................................................ 34 A3.1 User management ............................................................................................. 34 A3.2 User reviews ..................................................................................................... 34 A3.3 Segregation of duties ......................................................................................... 34 A3.4 Least-privilege rule ............................................................................................ 34 A3.5 Individual accounts ............................................................................................ 34 A3.6 Unique usernames ............................................................................................. 35 Annex 4 Security ....................................................................................... 36 A4.1 Ongoing security measures ................................................................................. 36 A4.2 Physical security ................................................................................................ 36 A4.3 Firewalls ........................................................................................................... 36 A4.4 Vulnerability management .................................................................................. 36 A4.5 Platform management ........................................................................................ 37 A4.6 Bi-directional devices ......................................................................................... 37 A4.7 Anti-virus software ............................................................................................ 37 A4.8 Penetration testing ............................................................................................ 37 A4.9 Intrusion detection and prevention ...................................................................... 37 A4.10 Internal activity monitoring ............................................................................... 37 A4.11 Security incident management .......................................................................... 38 A4.12 Authentication method ..................................................................................... 38 A4.13 Remote authentication ..................................................................................... 38 A4.14 Password managers ......................................................................................... 38 A4.15 Password policies ............................................................................................. 39 A4.16 Password confidentiality ................................................................................... 39 A4.17 Inactivity logout .............................................................................................. 39 A4.18 Remote connection .......................................................................................... 39 A4.19 Protection against unauthorised back-end changes .............................................. 39 Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 4/52 Annex 5 Additional consideration to specific systems ............................... 40 A5.1 Electronic clinical outcome assessment ................................................................. 40 A5.2 Interactive response technology system ............................................................... 45 A5.3 Electronic informed consent ................................................................................ 46 Annex 6 Clinical systems ........................................................................... 50 A6.1 Purchasing, developing, or updating computerised systems by sites ........................ 50 A6.2 Site qualification by the sponsor .......................................................................... 50 A6.3 Training ........................................................................................................... 50 A6.4 Documentation of medical oversight .................................................................... 50 A6.5 Confidentiality ................................................................................................... 51 A6.6 Security ........................................................................................................... 51 A6.7 User management ............................................................................................. 51 A6.8 Direct access .................................................................................................... 51 A6.9 Trial specific data acquisition tools ....................................................................... 52 A6.10 Archiving ........................................................................................................ 52 Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 5/52", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Generally used terms", "definition": "Unless otherwise specified (e.g. 'source data' or 'source document') and in order to simplify the text, 'data' will be used in this guideline in a broad meaning, which may include documents, records or any form of information. All references to sponsors and investigators in this guideline also apply to their service providers, irrespective of the services provided. When a computerised system is implemented by an institution where the investigator is conducting a clinical trial, any reference to the investigator in this guideline also includes the institution, when applicable. The term 'trial participant' is used in this text as a synonym for the term 'subject', which is defined in Regulation (EU) No 536/2014 as 'an individual who participates in a clinical trial, either as a recipient of the IMP or as a control'. The term 'responsible party' is frequently used instead of sponsor or principal investigator. Please also refer to section 4.2. and Annex 1. The term 'agreement' is used as an overarching term for all types of documented agreements, including contracts. The term 'validation' encompasses aspects usually known as 'qualification and validation'. Artificial intelligence Artificial intelligence (AI) covers a very broad set of algorithms, which enable computers to mimic human intelligence. It ranges from simple if-then rules and decision trees to machine learning and deep learning.", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Audit trail", "definition": "In computerised systems, an audit trail is a secure, computer generated, time-stamped electronic record that allows reconstruction of the events relating to the creation, modification, or deletion of an electronic record. Clinical outcome assessment Clinical outcome assessment (COA) employs a tool for the reporting of outcomes by clinicians, trial site staff, observers, trial participants and their caregivers. The term COA is proposed as an umbrella term to cover measurements of signs and symptoms, events, endpoints, health-related quality of life (HRQL), health status, adherence to treatment, satisfaction with treatment, etc. Computerised system life cycle The life cycle of a computerised system includes all phases of the system; i.e. typically 1) the concept phase where the responsible party considers to automate a process and where user requirements are collected, 2) the project phase where a service provider can be selected, a risk-assessment is made, and the system is implemented and validated, 3) the operational phase where a system is used in a regulated environment and changes are implemented in a manner that maintains data confidentiality, integrity and availability, and finally, 4) a retirement phase, which includes decisions about data retention/archiving, migration or destruction and the management of these processes.", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Configuration", "definition": "Configuration sets up a system using existing (out-of-the-box) functionality. It requires no programming knowledge. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 6/52", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Customisation", "definition": "Customisation modifies and adds to existing functionality by custom coding. It requires programming knowledge.", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Data governance", "definition": "The total of activities, processes, roles, policies, and standards used to manage and control the data during the entire data life cycle, while adhering to ALCOA++ principles (see section 4.5.).", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Data life cycle", "definition": "All processes related to the creating, recording, processing, reviewing, changing, analysing, reporting, transferring, storing, migrating, archiving, retrieving, and deleting of data.", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Dynamic file formats", "definition": "Dynamic files include automatic processing and/or enable an interactive relationship with the user. A certified electronic copy may be retained in electronic file formats that are different from the original record, but the equivalent dynamic nature (including metadata) of the original record should be retained.", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Event log", "definition": "An automated log of events in relation to the use of a system like system access, alerts or firing of edit checks. Patient-reported outcome Any outcome reported directly by the trial participant and based on the trial participant's perception of a disease and its treatment(s) is called patient-reported outcome (PRO). The term PRO is proposed as an umbrella term to cover both single dimension and multi-dimension measurements of symptoms, HRQL, health status, adherence to treatment, satisfaction with treatment, etc. (Source: CHMP 'Reflection paper on the regulatory guidance for the use of HRQL measures in the evaluation of medicinal products' - EMEA/CHMP/EWP/139391/2004)", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Static file formats", "definition": "Static files containing information or data that are fixed and allow no dynamic interaction.", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Validation", "definition": "'A process of establishing and documenting that the specified requirements of a computerized system can be consistently fulfilled from design until decommissioning of the system or transition to a new system. The approach to validation should be based on a risk assessment that takes into consideration the intended use of the system and the potential of the system to affect human subject protection and reliability of clinical trial results.' (ICH E6 R2 1.65) Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 7/52", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "AI", "definition": "artificial intelligence ALCOA++ attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring, available when needed and traceable", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "DSMB", "definition": "data and safety monitoring board", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "PRO", "definition": "patient-reported outcome Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 8/52", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "SUSAR", "definition": "suspected unexpected serious adverse reactions", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Executive summary", "definition": "Computerised systems are being increasingly used in clinical research. The complexity of such systems has evolved rapidly in the last few years from electronic case report forms (eCRF), electronic patient reported outcomes (ePROs) to various wearable devices used to continuously monitor trial participants for clinically relevant parameters and ultimately to the use of artificial intelligence (AI). Hence, there is a need to provide guidance to all stakeholders involved in clinical trials reflective of these changes in data types and trial types on the use of computerised systems and on the collection of electronic data, as this is important to ensure the quality and reliability of trial data, as well as the rights, dignity, safety and wellbeing of the trial participants. This would ultimately contribute to a robust decision-making process based on such clinical data. This guideline will describe some generally applicable principles and definition of key concepts. It also covers requirements and expectations for computerised systems, including validation, user management, security, and electronic data for the data life cycle. Requirements and expectations are also covered related to specific types of systems, processes, and data. 1. Introduction As described above, the change in data and trial types and thereby the use of computerised systems presents new challenges. The European Medicines Agency (EMA) 'Reflection Paper on expectations for electronic source data and data transcribed to electronic data collection tools in clinical trials' started to address these when it was published in 2010. However, the development of and experience with such systems has progressed. A more up-to-date guideline is needed to replace the Reflection Paper. There is no requirement or expectation that the sponsors and investigators use computerised systems to collect data; however, the use of data acquisition tools if implemented and controlled to the described standard, offers a wide variety of functions to improve data completeness, consistency and unambiguity, e.g. automatic edit checks, automated data transfers, validation checks, assisting information and workflow control. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 9/52 2. Scope The scope of this guideline is computerised systems, (including instruments, software and 'as a service') used in the creation/capture of electronic clinical data and to the control of other processes with the potential to affect participant protection and reliability of trial data, in the conduct of a clinical trial of investigational medicinal products (IMPs). These include, but may not be limited to the following: • Electronic medical records, used by the investigator to capture of all health information as per normal clinical practice. • Tools supplied to investigators/trial participants for recording clinical data via data entry (e.g. electronic clinical outcome assessments [eCOAs]).", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Electronic trial participant data capture devices used to collect ePRO data, e.g. mobile", "definition": "devices supplied to trial participants or applications for use by the trial participant on their own device i.e. bring your own device (BYOD).", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Electronic devices used by clinicians to collect data e.g. mobile devices supplied to", "definition": "clinicians. • Tools supplied for the automatic capture of data for trial participants such as biometrics, e.g. wearables or sensors. • eCRFs (e.g. desktop or mobile device-based programs or access to web-based applications), which may contain source data directly entered, transcribed data, or data transferred from other sources, or any combination of these. • Tools that automatically capture data related to the transit and storage temperatures for investigational medicinal product (IMP) or clinical samples. • Tools to capture, generate, handle, or store data in a clinical environment where analysis, tests, scans, imaging, evaluations, etc. involving trial participants or samples from trial participants are performed in support of clinical trials (e.g. LC-MS/MS systems, medical imaging and related software). • eTMFs, which are used to maintain and archive the clinical trial essential documentation. • Electronic informed consent, for the provision of information and/or capture of the informed consent when this is allowed according to national legislation, e.g. desktop or mobile device- based programs supplied to potential trial participants or applications for use by the potential trial participants on their BYOD or access to web-based applications. • Interactive Response Technologies (IRT), for the management of randomisation, supply and receipt of IMP, e.g. via a web-based application. • Portals or other systems for supplying information from the sponsor to the sites (e.g. investigator brochures (IBs), suspected unexpected serious adverse reactions (SUSARs) or training material), from the sites to the sponsor (e.g. the documentation of the investigator's review of important safety information), or from the sponsor or the site to adjudication committees and others. • Systems/tools used to conduct remote activities such as monitoring or auditing. • Other computerised systems implemented by the sponsor holding/managing and/or analysing or reporting data relevant to the clinical trial e.g. clinical trial management systems (CTMS), pharmacovigilance databases, statistical software, document management systems, test management systems and central monitoring software. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 10/52 • AI used in clinical trials e.g. for trial participant recruitment, determination of eligibility, coding of events and concomitant medication, data clarification, query processes and event adjudication. Requirements to AI beyond the generally applicable expectations to all systems will not be covered in this guideline initially. This may be covered in a future Annex. The approach towards computerised systems used in clinical practice (e.g. regarding validation) should be risk proportionate (please also refer to section 4.6.). 3. Legal and regulatory background • Regulation (EU) No 536/2014, or Directive 2001/20/EC and Directive 2005/28/EC • ICH Guideline for good clinical practice E6 R2 (EMA/CHMP/ICH/135/1995 Revision 2) This guideline is intended to assist the sponsors, investigators, and other parties involved in clinical trials to comply with the requirements of the current legislation (Regulation (EU) No 536/2014, Directive 2001/20/EC and Directive 2005/28/EC), as well as ICH E6 Good Clinical Practice (GCP), regarding the use of computerised systems and the collection of electronic data in clinical trials. The risk-based approach to quality management also has an impact on the use of computerised systems and the collection of electronic data. Consideration should also be given to meeting the requirements of any additional current legal and regulatory framework that may in addition apply to the medicinal product regulatory framework, depending on the digital technology. These may include e.g. medical devices, data protection legislation, and legislation on electronic identification and electronic signatures. Further elaboration of the expectations of the EU GCP Inspectors’ Working group (GCP IWG) on various topics, including those on computerised systems, can be found as GCP IWG Q&As published on the EMA website. 4. Principles and definition of key concepts The following sections outline the basic principles that apply to all computerised systems used in clinical trials. 4.1. Data integrity Data integrity is achieved when data (irrespective of media) are collected, accessed, and maintained in a secure manner, to fulfil the ALCOA++ principles of being attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring, available when needed and traceable as described in section 4.5. in order for the data to adequately support robust results and good decision making throughout the data life cycle. Assuring data integrity requires appropriate quality and risk management systems as described in section 4.6., including adherence to sound scientific principles and good documentation practices. • Data governance should address data ownership and responsibility throughout the data life cycle, and consider the design, operation, and monitoring of processes/systems to comply with the principles of data integrity including control over intentional and unintentional changes to data. • Data governance systems should include staff training on the importance of data integrity principles and the creation of a working environment that enables visibility, and actively encourages reporting of omissions and erroneous results. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 11/52 Lack of integrity before the expiration of the mandated retention period may render the data unusable and is equivalent to data loss/destruction. 4.2. Responsibilities Roles and responsibilities in clinical trials should be clearly defined. The responsibility for the conduct of clinical trials is assigned via legislation to two parties, which may each have implemented computerised systems for holding/managing data: • Investigators and their institutions, laboratories and other technical departments or clinics, generate and store the data, construct the record, and may use their own software and hardware (purchased, part of national or institutional health information systems, or locally developed). • Sponsors that supply, store and/or, manage and operate computerised systems (including software and hardware) and the records generated by them. Sponsors may do this directly, or via service providers, including organisations providing e.g. eCOA, eCRF, or IRT that collect and store data on behalf of sponsors. Please refer to Annex 1 regarding the transfer/delegation to service providers of tasks related to the use of computerised systems and services. 4.3. Data and metadata Electronic data consist of individual data points. Data become information when viewed in context. Metadata provide context for the data point. Different types of metadata exist such as: variable name, unit, field value before and after change, reason for change, trial master file (TMF) location document identifier, timestamp, user. Typically, these are data that describe the characteristics, structure, data elements and inter-relationships of data e.g. audit trails. Metadata also permit data to be attributable to an individual entering or taking an action on the data such as modifying, deleting, reviewing, etc. (or if automatically generated, to the original data source). Metadata form an integral part of the original record. Without the context provided by metadata, the data have no meaning. Loss of metadata may result in a lack of data integrity and may render the data unusable. 4.4. Source data The term source data refers to the original reported observation in a source document. Source documents could be e.g. hospital records, clinical and office charts, laboratory notes. Other examples are emails, spreadsheets, audio and/or video files, images, and tables in databases. The location of source documents and the associated source data they contain, should be clearly identified at all points within the data capture process. Below is an outline (figure 1) of the data processing stages, starting with the data capture. The correct identification of source data is important for adequate source data verification and archiving. Data at different processing stages can be considered source depending on the preceding processing steps. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 12/52", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Figure 1", "definition": "Data capture sometimes requires some degree of processing prior to data recording. In this process, the data generated during an observation, measurement or data collection is checked, processed, and transferred into a new format and then recorded. The retention of unprocessed data records is not always feasible. If the processing is an integral part of the solution used and is recognisable as such in the solution characteristics, there is no need to extract and retain the unprocessed data. It should be possible to validate the correct operation of the processing. As a general principle, the source data should be processed as little as possible and as much as necessary. From a practical point of view, the first obtainable permanent data from an electronic data generation/capture should be considered and defined as the electronic source data. This process should be validated to ensure that the source data generated/captured is representative of the original observation and should contain metadata, including audit trail, to ensure adherence to the ALCOA++ principles (see section 4.5.). The location where the source data is first obtained should be part of the metadata. 4.5. ALCOA++ principles A number of attributes are considered of universal importance to data. These include that the data are:", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Attributable", "definition": "Data should be attributable to the person and/or system generating the data. Based on the criticality of the data, it should also be traceable to the system/device, in which the data were generated/captured. The information about originator (e.g. system operator, data originator) and system (e.g. device, process) should be kept as part of the metadata.", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Legible", "definition": "Data should be maintained in a readable form to allow review in its original context. Therefore, changes to data, such as compression, encryption and coding should be completely reversible. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 13/52", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Contemporaneous", "definition": "Data should be generated by a system or captured by a person at the time of the observation. The time point of the observation and the time point of the storage should be kept as part of the metadata, including the audit trail. Accurate date and time information should be automatically captured and should be linked and set by an external standard.", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Original", "definition": "Data should be the original first generation/capture of the observation. Certified copies can replace original data (see section 6.5. on certified copies). Information that is originally captured in a dynamic state should remain available in that state.", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Accurate", "definition": "The use of computerised systems should ensure that the data are at least as accurate as those recorded on paper. The coding process, which consists in matching text or data collected on the data acquisition tools to terms in a standard dictionary, thesaurus, or tables (e.g. units, scales), should be controlled. The process of data transfer between systems should be validated to ensure the data remain accurate. Data should be an accurate representation of the observations made. Metadata should contain information to describe the observations and, where appropriate, it could also contain information to confirm its accuracy.", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Complete", "definition": "To reconstruct and fully understand an event, data should be a complete representation of the observation made. This includes the associated metadata and audit trail and may require preserving the original context.", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Consistent", "definition": "Processes should be in place to ensure consistency of the definition, generation/capturing and management (including migration) of data throughout the data life cycle. Processes should be implemented to detect and/or avoid contradictions, e.g. by the use of standardisation, data validation and appropriate training.", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Enduring", "definition": "Data should be maintained appropriately such that they remain intact and durable through the entire data life cycle, as appropriate, according to regulatory retention requirements (see sections 6.8. and 6.10. on back-up and archiving).", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Available when needed", "definition": "Data should be stored throughout the data life cycle and should be readily available for review when needed.", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Traceable", "definition": "Data should be traceable throughout the data life cycle. Any changes to the data, to the context/metadata should be traceable, should not obscure the original information and should be explained, if necessary. Changes should be documented as part of the metadata (e.g. audit trail). 4.6. Criticality and risks ICH E6 describes the need for a quality management system with a risk-based approach. Risks should be considered at both the system level e.g. standard operating procedures (SOPs), computerised systems and staff, and for the specific clinical trial e.g. trial specific data and data acquisition tools or trial specific configurations or customisations of systems. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 14/52 Risks in relation to the use of computerised systems and especially critical risks affecting the rights, safety and well-being of the trial participants or the reliability of the trial results would be those related to the assurance of data integrity. Those risks should be identified, analysed, and mitigated or accepted, where justified, throughout the life cycle of the system. Where applicable, mitigating actions include revised system design, configuration or customisation, increased system validation or revised SOPs (including appropriate training) for the use of systems and data governance culture. In general, risks should be determined based on the system used, its complexity, operator, use of system and data involved. Critical component parts of any system should always be addressed. For example, a component part of an IRT system that calculates IMP dose based on data input by the investigator would be high risk compared to other functionalities such as the generation of an IMP shipment report. The interface and interdependency between systems or system components should be taken into consideration. All data collected or generated in the context of a clinical trial should fulfil ALCOA++ principles. Consequently, the arrangements for data governance to ensure that data, irrespective of the format in", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "processed", "definition": "(including analysis, alteration/imputation, transformation, or migration), used, retained (archived), retrieved and destroyed should be considered for data integrity risks and appropriate control processes implemented. The approach used to reduce risks to an acceptable level should be proportionate to the significance of the risk. Risk reduction activities may be incorporated in protocol design and implementation, system design, coding and validation, monitoring plans, agreements between parties that define roles and responsibilities, systematic safeguards to ensure adherence to SOPs, training in processes and procedures, etc. There are special risks to take into consideration when activities are transferred/delegated. These are further elaborated on in Annex 1 on agreements. The risk-assessment should take the relevance of the system use for the safety, rights, dignity and well- being of the participant and the importance and integrity of derived clinical trial data into account i.e. whether the system is used for standard care and safety measurements for participants or if systems are used to generate primary efficacy data that are relied on in e.g. a marketing authorisation application. Systems used for other purposes than what they were developed for, or which are used outside the supplier’s specification/validation are inherently higher risk. In case of well-established computerised systems, which are used as intended in a routine setting for less critical trial data, the certification by a notified body may suffice as documentation whereas other more critical systems may require a more in- depth validation effort. This decision should be justified prior to use in the trial. For systems deployed by the investigator/institution specifically for the purposes of clinical trials, the investigator should ensure that the requirements for computerised systems as described in this guideline are addressed and proportionately implemented. For systems deployed by the investigator/institution, the sponsor should determine during site selection whether such systems (e.g. electronic medical records and other record keeping systems for source data collection and the investigator site file) are fit for purpose. For computerised systems deployed by the sponsor, the sponsor should ensure that the requirements of this guideline are addressed and proportionately implemented. 4.7. Data capture The clinical trial protocol should specify data to be collected and the processes to capture them, including by whom, when and by which tools. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 15/52 Data acquisition tools should be designed and/or configured or customised to capture all information required by the protocol and not more. Data fields should not be prepopulated or automatically filled in, unless these fields are not editable and are derived from already entered data (e.g. body surface area). The protocol should identify any data to be recorded directly in the data acquisition tools and identify them as source data. A detailed diagram and description of the transmission of electronic data (data flow) should be available in the protocol or a protocol-related document. The sponsor should describe which data will be transferred and in what format, the origin and destination of the data, the parties with access to the transferred data, the timing of the transfer and any actions that may be applied to the data, for example, data validation, reconciliation, verification, and review. The use of a data management plan (DMP) is encouraged. The sponsor should ensure the traceability of data transformations and derivations during data processing and analysis. 4.8. Electronic signatures Whenever ICH E6 requires a document to be signed and an electronic signature is used for that purpose, the electronic signature functionality should meet the expectations stated below regarding authentication, non-repudiation, unbreakable link, and timestamp of the signature. The system should thus include functionality to: • authenticate the signatory, i.e. establish a high degree of certainty that a record was signed by the claimed signatory; • ensure non-repudiation, i.e. that the signatory cannot later deny having signed the record; • ensure an unbreakable link between the electronic record and its signature, i.e. that the contents of a signed (approved) version of a record cannot later be changed by anyone without the signature being rendered visibly invalid; • provide a timestamp, i.e. that the date, time, and time zone when the signature was applied is recorded. Electronic signatures can further be divided into two groups depending on whether the identity of the signatory is known in advance, i.e. signatures executed in 'closed' and in 'open' systems. For 'closed' systems, which constitute the majority of systems used in clinical trials and which are typically provided by the responsible party or by their respective service provider, the system owner knows the identity of all users and signatories and grants and controls their access rights to the system. Regulation (EU) No 910/2014 ('eIDAS') on electronic identification and trust services for electronic transactions is not applicable for 'closed' systems ('eIDAS' article 2.2). The electronic signature functionality in these systems should be proven during system validation to meet the expectations mentioned above. For 'open' systems, the signatories (and users) are not known in advance. For sites located in the EU, electronic signatures should meet the requirements defined in the 'eIDAS' regulation. Sites located in third countries should use electronic or digital signature solutions compliant with local regulations and proven to meet the expectations mentioned above. Irrespective of the media used, in case a signature is applied on a different document or only on part of a document (e.g. signature page), there should still be an unbreakable link between the electronic document to be signed and the document containing the signature. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 16/52 4.9. Data protection The confidentiality of data that could identify trial participants should be protected, respecting privacy and confidentiality rules in accordance with the applicable regulatory requirement(s). The requirements of General Data Protection Regulation (EU) No 2016/679 (GDPR) on the protection of individuals with regard to the processing of personal data and on the free movement of such data should be followed except when specific requirements are implemented for clinical trials e.g. that a trial participant does not have the right to be forgotten (and for the data to be consequently deleted) as this would cause bias to e.g. safety data (Regulation (EU) No 536/2014 recital 76 and Article 28(3)). Trial participants should be informed accordingly. In accordance with EU data protection legislation, if personal data of trial participants from an EU Member State are processed (at rest or in transit) or transferred to a third country or international organisation, such data transfer must comply with applicable Union data protection. In summary, this means that the transfer must be either carried out on the basis of an adequacy decision (Article 45 of GDPR, Article 47 of Regulation (EU) No 2018/1727 - EUDPR), otherwise the transfer must be subject to appropriate safeguards (as listed in Article 46 of GDPR or Article 48 of EUDPR) or the transfer may take place only if a derogation for specific situations apply (under Article 49 of GDPR or Article 50 of EUDPR). 4.10. Validation of systems Computerised systems used within a clinical trial should be subject to processes that confirm that the specified requirements of a computerised system are consistently fulfilled, and that the system is fit for purpose. Validation should ensure accuracy, reliability, and consistent intended performance, from the design until the decommissioning of the system or transition to a new system. The processes used for the validation should be decided upon by the system owner (e.g. sponsors, investigators, technical facilities) and described, as applicable. System owners should ensure adequate oversight of validation activities (and associated records) performed by service providers to ensure suitable procedures are in place and that they are being adhered to. Documentation (including information within computerised systems used as process tools for validation activities) should be maintained to demonstrate that the system is maintained in the validated state. Such documentation should be available for both the validation of the computerised system and for the validation of the trial specific configuration or customisation. Validation of the trial specific configuration or customisation should ensure that the system is consistent with the requirements of the approved clinical trial protocol and that robust testing of functionality implementing such requirements is undertaken, for example, eligibility criteria questions in an eCRF, randomisation strata and dose calculations in an IRT system. See Annex 2 for further detail on validation. 4.11. Direct access All relevant computerised systems should be readily available with full, direct and read-only access (this requires a unique identification method e.g. username and password) upon request by inspectors from regulatory authorities. If a computerised system is decommissioned, direct access (with a unique identification method) to the data in a timely manner should still be ensured (see section 6.12.). Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 17/52 5. Computerised systems Requirements for validation are described in section 4.10. and Annex 2, the requirements for user management are described in Annex 3, while the requirements for information technology (IT) security are detailed in Annex 4 of this guideline. 5.1. Description of systems The responsible party should maintain a list of physical and logical locations of the data e.g. servers, functionality and operational responsibility for computerised systems and databases used in a clinical trial together with an assessment of their fitness for purpose. Where multiple computerised systems/databases are used, a clear overview should be available so the extent of computerisation can be understood. System interfaces should be described, defining how the systems interact, including validation status, methods used, and security measures implemented. 5.2. Documented procedures Documented procedures should be in place to ensure that computerised systems are used correctly. These procedures should be controlled and maintained by the responsible party. 5.3. Training Each individual involved in conducting a clinical trial should be qualified by education, training, and experience to perform their respective task(s). This also applies to training on computerised systems. Systems and training should be designed to meet the specific needs of the system users (e.g. sponsor, investigator or service provider). Special consideration should be given to the training of trial participants when they are users. There should be training on the relevant aspects of the legislation and guidelines for those involved in developing, coding, building, and managing trial specific computerised systems, for example, those employed at a service provider supplying eCRF, IRT, ePRO, trial specific configuration, customisation, and management of the system during the conduct of the clinical trial. All training should be documented, and the records retained and available for monitoring, auditing, and inspections. 5.4. Security and access control To maintain data integrity and the protection of the rights of trial participants, computerised systems used in clinical trials should have security processes and features to prevent unauthorised access and unwarranted data changes and should maintain blinding of the treatment allocation where applicable. Checks should be used to ensure that only authorised individuals have access to the system and that they are granted appropriate permissions (e.g. ability to enter or make changes to data). Records of authorisation of access to the systems, with the respective levels of access clearly documented, should be maintained. The system should record changes to user roles and thereby access rights and permissions. There should be documented training on the importance of security e.g. the need to protect passwords and to keep them confidential, enforcement of security systems and processes, identification and handling of security incidents, social engineering and the prevention of phishing. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 18/52 See Annexes 3 and 4 for further guidance on user management and IT security. 5.5. Timestamp Accurate and unambiguous date and time information given in coordinated universal time (UTC) or time and time zone (set by an external standard) should be automatically captured. Users should not be able to modify the date, time and time zone on the device used for data entry, when this information is captured by the computerised system and used as a timestamp. 6. Electronic data For each trial, it should be identified what electronic data and records will be collected, modified, imported and exported, archived and how they will be retrieved and transmitted. Electronic source data, including the audit trail should be directly accessible by investigators, monitors, auditors, and inspectors without compromising the confidentiality of participants’ identities. 6.1. Data capture and location The primary goal of data capture is to collect all data required by the protocol. All pertinent observations should be documented in a timely manner. The location of all source data should be specified prior to the start of the trial and updated during the conduct of the trial where applicable. 6.1.1. Transcription Source data collected on paper (e.g. worksheets, paper CRFs or paper diaries or questionnaires) need to be transcribed either manually or by a validated entry tool into the electronic data collection (EDC) system or database(s). In case of manual transcription, risk-based methods should be implemented to ensure the quality of the transcribed data (e.g. double data entry and/or data monitoring). 6.1.2. Transfer Trial data are transferred in and between systems on a regular basis. The process for file and data transfer needs to be validated and should ensure that data and file integrity are assured for all transfers. Data that is collected from external sources and transferred in open networks should be protected from unwarranted changes and secured/encrypted in a way that precludes disclosure of confidential information. All transfers that are needed during the conduct of a clinical trial need to be pre-specified. Validation of transfer should include appropriate challenging test sets and ensure that the process is available and functioning at clinical trial start (e.g. to enable ongoing sponsor review of diary data, lab data or adverse events by safety committees). Data transcribed or extracted and transferred from electronic sources and their associated audit trails should be continuously accessible (according to delegated roles and corresponding access rights). Transfer of source data and records when the original data or file are not maintained is a critical process and appropriate considerations are expected in order to prevent loss of data and metadata. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 19/52 6.1.3. Direct capture Direct data capture can be done by using electronic data input devices and applications such as electronic diaries, electronic questionnaires and eCRFs for direct data entry. Where treatment-related pertinent information is captured first in a direct data capture tool such as a trial participant diary, a PRO form or a special questionnaire, a documented procedure should exist to transfer or transcribe information into the medical record, when relevant. Direct data capture can also be done by automated devices such as wearables or laboratory or other technical equipment (e.g. medical imaging, electrocardiography equipment) that are directly linked to a data acquisition tool. Such data should be accompanied by metadata concerning the device used (e.g. device version, device identifiers, firmware version, last calibration, data originator, timestamp of events). 6.1.4. Edit checks Computerised systems should validate manual and automatic data inputs to ensure a predefined set of validation criteria is adhered to. Edit checks should be relevant to the protocol and developed and revised as needed. Edit checks should be validated and implementation of the individual edit checks should be controlled and documented. If edit checks are paused at any time during the trial, this should be documented and justified. Edit checks could either be run immediately at data entry or automatically during defined intervals (e.g. daily) or manually. Such approaches should be guided by necessity, should not cause bias and should be traceable e.g. when data are changed as a result of an edit check notification. The sponsor should not make automatic or manual changes to data entered by the investigator or trial participants unless authorised by the investigator. 6.2. Audit trail and audit trail review 6.2.1. Audit trail An audit trail should be enabled for the original creation and subsequent modification of all electronic data. In computerised systems, the audit trail should be secure, computer generated and timestamped. An audit trail is essential to ensure that changes to the data are traceable. Audit trails should be robust, and it should not be possible for 'normal' users to deactivate them. If possible, for an audit trail to be deactivated by 'admin users', this should automatically create an entry into a log file (e.g. audit trail). Entries in the audit trail should be protected against change, deletion, and access modification (e.g. edit rights, visibility rights). The audit trail should be stored within the system itself. The responsible investigator, sponsor, and inspector should be able to review and comprehend the audit trail and therefore audit trails should be in a human-readable format. Audit trails should be visible at data-point level in the live system, and it should be possible to export the entire audit trail as a dynamic data file to allow for the identification of systematic patterns or concerns in data across trial participants, sites, etc. The audit trail should show the initial entry and the changes (value - previous and current) specifying what was changed (field, data identifiers) by whom (username, role, organisation), when (date/timestamp) and, where applicable, why (reason for change). A procedure should be in place to address the situation when a data originator (e.g. investigator or trial participant) realises that she/he has submitted incorrect data by mistake and wants to correct the recorded data. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 20/52 It is important that original electronic entries are visible or accessible (e.g. in the audit trail) to ensure the changes are traceable. The audit trail should record all changes made as a result of data queries or a clarification process. The clarification process for data entered should be described and documented. Changes to data should only be performed when justified. Justification should be documented. In case the data originator is the trial participant, special considerations to data clarifications might be warranted. See Annex 5 section A5.1.1.4 for further details. For certain types of systems (e.g. ePRO) the data entered may not be uploaded immediately but may be temporarily stored in local memory. Such data should not be edited or changed without the knowledge of the data originator prior to saving. Any changes or edits should be acknowledged by the data originator, should be documented in an audit trail and should be part of validation procedures. The timestamp of data entry in the capture tool (e.g. eCRF) and timestamp of data saved to a hard drive should be recorded as part of the metadata. The duration between initial capture in local memory and upload to a central server should be short and traceable (i.e. transaction time), especially in case of direct data entry. Data extracts or database extracts for internal reporting and statistical analysis do not necessarily need to contain the audit trail information. However, the database audit trail should capture the generation of data extracts and exports. Audit trails should capture any changes in data entry per field and not per page (e.g. eCRF page). In addition to the audit trail, metadata could also include (among others) review of access logs, event logs, queries etc. Access logs, including username and user role, are in some cases considered to be important metadata and should consequently be available. This is considered necessary e.g. for systems that contain critical unblinded data. Care should be taken to ensure that information jeopardising the blinding does not appear in the audit trail accessible to blinded users. 6.2.2. Audit trail review Procedures for risk-based trial specific audit trail reviews should be in place and performance of data review should be generally documented. Data review should focus on critical data. Data review should be proactive and ongoing review is expected unless justified. Manual review as well as review by the use of technologies to facilitate the review of larger datasets should be considered. Data review can be used to (among others) identify missing data, detect signs of data manipulation, identify abnormal data/outliers and data entered at unexpected or inconsistent hours and dates (individual data points, trial participants, sites), identify incorrect processing of data (e.g. non-automatic calculations), detect unauthorised accesses, detect device or system malfunction and to detect if additional training is needed for trial participants /site staff etc. Audit trail review can also be used to detect situations where direct data capture has been defined in the protocol but where this is not taking place as described. In addition to audit trail review, metadata review could also include (among others) review of access logs, event logs, queries, etc. The investigator should receive an introduction on how to navigate the audit trail of their own data in order to be able to review changes. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 21/52 6.3. Sign-off of data The investigators are responsible for data entered into eCRFs and other data acquisition tools under their supervision (electronic records). The sponsor should seek investigator endorsement of their data at predetermined milestones. The signature of the investigator or authorised member of the investigator’s staff is considered as the documented confirmation that the data entered by the investigator and submitted to the sponsor are attributable, legible, original, accurate, and complete and contemporaneous. Any member of the staff authorised for sign-off should be qualified to do so in order to fulfil the purpose of the review as described below. National law could require specific responsibilities, which should then be followed. The acceptable timing and frequency for the sign-off needs to be defined and justified for each trial by the sponsor and should be determined by the sponsor in a risk-based manner. The sponsor should consider trial specific risks and provide a rationale for the risk-based approach. Points of consideration are types of data entered, non-routine data, importance of data, data for analysis, length of the trial and the decision made by the sponsor based on the entered data, including the timing of such decisions. It is essential that data are confirmed prior to interim analysis and the final analysis, and that important data related to e.g. reporting of serious adverse events (SAEs), adjudication of important events and endpoint data, data and safety monitoring board (DSMB) review, are signed off in a timely manner. In addition, a timely review and sign-off of data that are entered directly into the eCRF as source is particularly important. Therefore, it will rarely be sufficient to just provide one signature immediately prior to database lock. Signing of batches of workbooks is also not suited to ensure high data quality and undermines the purpose of timely and thorough data review. For planned interim analysis, e.g. when filing for a marketing authorisation application, all submitted data need to be signed off by the investigator or their designated and qualified representative before extracting data for analysis. The systems should be designed to support this functionality. To facilitate timely data review and signing by the investigator or their designated representative, the design of the data acquisition tool should be laid out to support the signing of the data at the defined time points. Furthermore, it is important that the investigator review the data on an ongoing basis in order to detect shortcomings and deficiencies in the trial conduct at an early stage, which is the precondition to undertake appropriate corrective and preventive actions. Adequate oversight by the investigator is a general requirement to ensure participant safety as well as data quality and integrity. Oversight can be demonstrated by various means, one of them being the review of reported data. Lack of investigator oversight may prevent incorrect data from being corrected in a timely manner and necessary corrective and preventive actions being implemented at the investigator site. 6.4. Copying data Data can be copied or transcribed for different purposes, either to replace source documents or essential documents or to be distributed amongst different stakeholders as working copies. If essential documents or source documents are irreversibly replaced by a copy, the copy should be certified (see section 6.5.). Copies should contain a faithful representation of the data and the contextual information. Source documents and data should allow accurate copies to be made. The method of copying should be practical and should ensure that the resulting copy is complete and accurate. It should include the relevant Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 22/52 metadata and such metadata should be complete and accurate. See also section 5 of the 'Guideline on the content, management and archiving of the clinical trial master file (paper and/or electronic)' (EMA/INS/GCP/856758/2018), for further details on definition. 6.5. Certified copies When creating a certified copy, the nature of the original document needs to be considered. For example, the content of the file is either static (e.g. a PDF document) or dynamic (e.g. a worksheet with automatic calculations) or the copy tries to capture the result of an interpreter (e.g. a web page, where a web- browser interprets written hypertext mark-up language (HTML), JavaScript (JS) among other programming languages). Either way, the result of the copy process should be verified either automatically by a validated process or manually to ensure that the same information is present — including data that describe the context, content, and structure — as in the original. In case of dynamic files e.g. when a database is decommissioned and copies of data and metadata are provided to sponsors, the resulting file should also capture the dynamic aspects of the original file. In case of files, which are the result of an interpreter, special care needs to be taken to not only consider the informative content of such a file, but also to capture and preserve aspects that are the result of the interactions of the used interpreter(s) and system settings during the display. For example, window size, browser type, operating system employed and the availability of software dependencies (e.g. enabled active web content) can influence the structure and content displayed. Special considerations should be taken whenever copies are to replace original source documents. 6.6. Control of data Data generated at the clinical trial site relating to the trial participants should be available to the investigator at all times during and after the trial to enable investigators to make decisions related to eligibility, treatment, care for the participants, etc. and to ensure that the investigator can fulfil their legal responsibility to retain an independent copy of the data for the required retention period. This includes data from external sources, such as central laboratory data, centrally read imaging data and ePRO data. Exceptions should be justified in the protocol e.g. if sharing this information with the investigator would jeopardise the blinding of the trial. The sponsor should not have exclusive control of the data entered in a computerised system at any point in time. All data held by the sponsor that has been generated in a clinical trial should be verifiable to a copy of these data that is not held (or that has not been held) by the sponsor. The requirements above are not met if data are captured in a computerised system and the data are stored on a central server under the sole control of the sponsor or under the control of a service provider that is not considered to be independent from the sponsor or if the sponsor (instead of the service provider) is distributing the data to the investigator. This is because the investigator does not hold an independent copy of the data and therefore the sponsor has exclusive control of the data. In order to meet the requirements, the investigator should be able to download a contemporaneous certified copy of the data. This is in addition to the record maintained at a service provider. Instead of a system maintained by an independent service provider, the sponsor may take other adequate technical measures that preclude sole control. E.g. the verifiability of data (transactions) by an independent (distributed) tamper-proof ledger may provide comparable security to a system maintained by an independent service provider. This should be justified and documented. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 23/52 Data entered to data acquisition tools by the investigator should be available to the investigator throughout the whole legally mandated duration and for the full duration of local legal requirements. This can be ensured either by contemporaneous local copies at the trial site or e.g. by the use of a service provider. Access to the data may be amended to read-only as part of the database lock process. Prior to read-only access to the investigator being revoked, a copy including the audit trail should be made available to the investigator in a complete and comprehensive way. In the situation where a service provider is hosting the data, the copy should not be provided via the sponsor, as this would temporarily provide the sponsor with exclusive control over the data and thereby jeopardise the investigator’s control. Copies should not be provided in a way that requires advanced technical skills from the investigators. The period between the provision of the copy to the investigator and the closure of the investigators’ read-only access to the database(s) should allow sufficient time for the investigator to review the copy and access should not be revoked until such a review has been performed. Any contractual agreements regarding hosting should ensure investigator control. If the sponsor is arranging hosting on behalf of the investigators through a service provider, agreements should ensure the level of investigator control mentioned above. Investigators delegating hosting of such data to service providers themselves should ensure that the intended use is covered by local legal requirements and the in-house rules of the institution. For investigator-initiated trials, where the data are hosted somewhere in the sponsor/institution organisation, the degree of independence should be justified and pre-specified in agreements e.g. that it is a central IT department, not otherwise involved in the operational aspects of the trial, hosting the data and providing copies to the participating investigators. 6.7. Cloud solutions Irrespective whether a computerised system is installed at the premises of the sponsor, investigator, another party involved in the trial or whether it is made available by a service provider as a cloud solution, the requirements in this guideline are applicable. There are, however, specific points to be considered as described below. Cloud solutions cover a wide variety of services related to the computerised systems used in clinical trials. These can range from Infrastructure as a Service (IaaS) over Platform as a Service (PaaS) to Software as a Service (SaaS). It is common for these services that they provide the responsible party on-demand availability of computerised system resources over the internet, without having the need or even the possibility to directly manage these services. If a cloud solution is used, the responsible party should ensure that the service provider providing the cloud is qualified. When using cloud computing, the responsible parties are at a certain risk, because many services are managed less visibly by the cloud provider. Contractual obligations with the cloud solution provider should be detailed and explicit and refer to all ICH E6 relevant topics and to all relevant legal requirements (see Annex 1). Data jurisdiction may be complex given the nature of cloud solutions and services being shared over several sites, countries, and continents; however, any uncertainties should be addressed and solved by contractual obligations prior to the use of a cloud solution. If the responsible party choses to perform their own validation of the computerised system, the cloud provider should make a test environment available that is identical to the production environment. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 24/52 6.8. Backup of data Data stored in a computerised system are susceptible to system malfunction, intended or unintended attempts to alter or destroy data and physical destruction of media and infrastructure and are therefore at risk of loss. Data and configurations should be regularly backed up. Please also refer to Annex 4 for further details on IT security. The use of replicated servers is strongly recommended. Backups should be stored in separate physical locations and logical networks and not behind the same firewall as the original data to avoid simultaneous destruction or alteration. Frequency of backups (e.g. hourly, daily, weekly) and their retention (e.g. a day, a week, a month) should be determined through a risk-based approach. Checks of accessibility to data, irrespective of format, including relevant metadata, should be undertaken to confirm that the data are enduring, continue to be available, readable and understandable by a human being. There should be procedures in place for risk-based (e.g. in connection with major updates) restore tests from the backup of the complete database(s) and configurations and the performed restore tests should be documented. Disaster mitigation and recovery plans should be in place to deal with events that endanger data security. Such plans should be regularly reviewed. Disaster mitigation and recovery plans should be part of the contractual agreement, if applicable. 6.9. Contingency plans Agreements and procedures should be in place to allow trial continuation and prevent loss of data critical to participant safety and trial results. 6.10. Migration of data Migration as opposed to the transfer of data (as described in section 6.1.2.) is the process of permanently moving existing data (including metadata) from one system into another system e.g. the migration of individual safety reports from one safety database to another. It should be ensured that the migration does not adversely affect existing data and metadata. In the course of the design or purchase of a new system and of subsequent data migration from an old system, validation of the data migration process should have no less focus than the validation of the system itself. The validation of data migration should take into consideration the complexity of the task and any foreseen possibilities that may exist to verify the migrated data (e.g. checksum, case counts, quality control of records). Prior to migration, the process should be planned in detail. A risk analysis identifying the most probable risks should take place and should yield appropriate mitigation strategies. After the planning, the intended procedure should be validated with mock data and results should be considered for risk- assessment and mitigation. A data verification focused on key data should be performed post migration. Verification of migrated data can be simple or complex, depending on the different platforms and systems involved. Regardless of the effort needed, the migration process should be documented in such detail that throughout all data operations/transformations data changes remain traceable. Mapping from the old system onto the new system should be retained. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 25/52 Data, contextual information, and the audit trail should not be separated. In case migration of data into a new system results in a loss of relevant data, adequate mitigating actions should be taken to establish a robust method to join the audit trail and the data for continuous access by all stakeholders. A detailed explanation is expected, if no such method has been established to allow the migration of data and the audit trail. Arrangements should ensure that the link between data and metadata can be established. If several parties are involved, agreements should be in place to ensure this. 6.11. Archiving The investigator and sponsor should be aware of the required retention periods for clinical trial data and essential documents, including metadata. Retention periods should respect the data protection principle of storage limitation. An inventory of all essential data and documents and corresponding retention periods should be maintained. It should be clearly defined which data are related to each clinical trial activity and where this record is located and who has access/edit rights to the document. Security controls should be in place to ensure data confidentiality, integrity, and availability. It should be ensured that the file and any software required (depending on the media used for storage) remain accessible, throughout the retention period. This could imply e.g. migration of data (see section 6.9.). Suitable archiving systems should be in place to safeguard data integrity for the periods established by the regulatory requirements including those in any of the regions where the data may be used for regulatory submissions, and not just those of the country where the data are generated. Source documents and data should always be available when needed to authorised individuals to meet their regulatory obligations. Please refer to section 4.11 direct access. Data should be maintained in a secure manner and should only be transferred between different (physical) locations in a validated process. Data should be archived in a read-only state. 6.12. Database decommissioning After the finalisation of the trial, database(s) might be decommissioned. It is recommended that the time of decommissioning is decided taking into consideration e.g. whether the clinical trial will be used for a marketing authorisation application in the near future in which case it is recommended to keep the database(s) live. Please refer to figure 2 for a proposed approach. A dated and certified copy of the database(s) and data should be archived and available on request. In case of decommissioning, the sponsor should ensure (contractually if done by a service provider) that archived formats provide the possibility to restore the database(s). This includes the restoration of dynamic functionality and all relevant metadata (audit trail, event logs, implemented edit checks, queries, user logs, etc.). Where recommissioning is no longer possible, the sponsor should ensure that all the data including metadata files (e.g. audit trails) are available in dynamic data files. The sponsor should review the system to determine the audit trails and logs available in the system and how these would be retained as dynamic files. Where a service provider is involved, this should be addressed in the contractual arrangements. Static formats of dynamic data will not be considered adequate. See definitions section on static and dynamic formats. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 26/52", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Figure 2", "definition": "Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 27/52", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Annex 1 Agreements", "definition": "The legally responsible parties are the sponsors and investigators. They contract/delegate an increasing number of tasks in clinical trials, contracting is frequent in the area of computerised systems where the responsible party might lack internal knowledge or resources or they wish to purchase a product or a service that has been developed by others. The responsible parties can delegate tasks to a service provider, but nevertheless the full responsibility for the data integrity, security and confidentiality resides with them. Agreements can cover a variety of tasks such as system and trial specific configuration and customisation, provision of a license to an application, full clinical trial service including data management tasks e.g. site contact, training, data clarification processes, etc., but could also be restricted to hosting services. A risk-based approach can be used in relation to agreements as well as for computerised systems in general. It is recognised that a trial specific agreement is not required, if a product is purchased and used as intended without the involvement of the manufacturer of the system; however, such use will require a risk assessment by the responsible party to assess whether such a non-trial specific system is fit for its intended use. The responsible party should ensure that the distribution of tasks in a trial is clearly documented and agreed on. It should be ensured that each party has the control of and access to data and information that their legal responsibilities require and that the ethics committees and regulatory authorities approving trials have been properly informed of distribution of activities as part of the clinical trial application process, where applicable. This should be carefully documented in the protocol and related documents, procedures, agreements, and other documents as relevant. It is important to consider who is providing and controlling the computerised system being used. Clear written agreements should be in place and appropriately signed by all involved parties prior to the provision of services or systems. Agreements should be maintained/updated as appropriate. Sub- contracting and conditions for sub-contracting and the responsible party’s oversight of sub-contracted activities should be specified. The responsible parties should ensure oversight of these trial-related duties e.g. by reviewing defined key performance indicators (KPIs) or reconciliations. If appropriate agreements cannot be put in place due to the inability or reluctance of a service provider to allow access to important documentation (e.g. system requirements specifications) or the service provider is unwilling to support pre-qualification audits or regulatory inspections, systems from such a service provider should not be used in clinical trials. The responsible party should ensure that service providers (including vendors of computerised systems) have the knowledge and the processes to ensure that they can perform their tasks in accordance with ICH E6, as appropriate to their tasks. Standards to be followed, e.g. clinical trial legislation and guidance should be specified in the agreement, where relevant. A number of tasks involve accessing, reviewing, collecting and/or analysing data, much of which is personal/pseudonymised data. In addition, in specific cases involving contact with (potential) trial participants, data protection legislation needs to be followed, in addition to the clinical trial legislation and guidance. The approved protocol, implicitly, defines part of the specification for system configuration or customisation (e.g. for interactive response technologies (IRT) systems and data acquisition tools) and there should be consistency between the protocol and the wording of the agreement. In addition, it should be clear how subsequent changes to the protocol are handled so that the vendor can implement changes to the computerised system, where relevant. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 28/52 It should be clear from agreements which tasks are delegated also in relation to retaining essential documentation for performed activities. In the context of clinical trials, system-documentation (including e.g. software/system validation documentation, vendor standard operating procedures (SOPs), training records, issues log/resolutions) as well as trial master file (TMF) documentation (e.g. emails on important decisions and meeting minutes) related to the individual clinical trial (including e.g. relevant helpdesk tickets or meeting minutes) should be retained for the full retention period. It should be clear from the agreement which party is retaining and maintaining which documentation and how and in what format that documentation is made available when needed e.g. for an audit or an inspection. There should be no difference in the availability of documentation irrespective of whether the documentation is held by the sponsor/investigator or a service provider or sub-contracted party. The responsible party is ultimately responsible for e.g. the validation and operation of the computerised system and for providing adequate documented evidence of applicable processes. The responsible party should be able to provide the GCP inspectors of the EU/EEA authorities with access to the requested documentation regarding the validation and operation of computerised systems irrespective of who performed these activities. It should be specified in agreements that the sponsor or the institution, as applicable, should have the right to conduct audits at the vendor site and that the vendor site could be subject to inspections (by national and/or international authorities) and that the vendor site shall accept these. The responsible party should also ensure that their service providers act on/respond appropriately to findings from audits and inspections. The sponsor has a legal responsibility under Regulation (EU) No 536/2014 to report serious breaches, including important data and security breaches, to authorities within seven days. To avoid undue delay in sponsor reporting from the time of discovery e.g. by a vendor, agreements and related documents should specify which information should be escalated immediately to ensure regulatory compliance. As set out in ICH E6, to ensure that the investigator, rather than the sponsor, maintains control over their data, it should be specified in agreements how investigators’ access to and control over data are ensured during and after the trial, and the revocation of investigator access to data in case of decommissioning should be described. It should also be specified which outputs the involved parties (e.g. sponsor and investigators) will receive during and after the clinical trial and in what formats. Types of output could include e.g. data collected via data acquisition tools including metadata, queries, history and status of changes to users and their access rights, and the description of format for delivery of the complete database to sponsors. Arrangements on the decommissioning of the database(s) should be clear, including the possibility to restore the database(s), for instance, for inspection purposes. The agreements should address expectations regarding potential system 'down-time' and the preparation of contingency plans. Tasks transferred/delegated could include hosting of data. If data are hosted by a vendor, location of data storage and control (e.g. use of cloud services) should be described. Agreements should ensure reliable, continued and timely access to the data in case of bankruptcy, shutdown, disaster of the vendor, discontinuation of service by the vendor or for reasons chosen by the sponsor/investigator (e.g. change of vendor). Special consideration should be given on training and quality systems. Vendors accepting tasks on computerised systems should not only be knowledgeable about computerised systems and data protection legislation, but also on GCP requirements, quality systems, etc. as appropriate to the tasks they perform. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 29/52 This guideline should be read together with the notice to sponsors regarding computerised systems (EMA/INS/GCP/467532/2019) published on the EMA website. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 30/52 Annex 2 Computerised systems validation A2.1 General principles The responsible party should ensure that systems used in clinical trials have been appropriately validated and demonstrated to meet the requirements defined in ICH E6 and in this guideline. Systems should be validated independently of whether they are developed on request by the responsible party, are commercially or freely available, or are provided as a service. The responsible party may rely on validation documentation provided by the vendor of a system if they have assessed the validation activities performed by the vendor and the associated documentation as adequate; however, they may also have to perform additional validation activities based on a documented assessment. In any case, the responsible party remains ultimately responsible for the validation of the computerised systems used in clinical trials. If the responsible party wants to use the vendor's validation documentation, the responsible party should ensure that it covers the responsible party's intended use as well as its defined needs and requirements. The responsible party should be thoroughly familiar with the vendor's quality system and validation activities, which can usually be obtained through an in-depth systematic examination (e.g. an audit). This examination should be performed by qualified staff with sufficient time spent on the activities and with cooperation from the vendor. It should go sufficiently deep into the actual activities, and a suitable number of relevant key requirements and corresponding test cases should be reviewed, and this review should be documented. The examination report should document that the vendor's validation process and documentation is satisfactory. Any shortcomings should be mitigated by the responsible party, e.g. by requesting or performing additional validation activities. Some service providers may release new or updated versions of a system at short notice, leaving insufficient time for the responsible party to validate it or to review any validation documentation supplied by the service provider. In such a situation, it is particularly important for the responsible party to evaluate the vendor's process for validation prior to release for production, and to strengthen their own periodic review and change control processes. New functionalities should not be used by the responsible party until they have validated them or reviewed and assessed the vendor's documentation. If the responsible party relies on the vendor's validation documentation, inspectors should be given access to the full documentation and reporting of the responsible party’s examination of the vendor. If this examination is documented in an audit report, this may require providing access to the report. The responsible party, or where applicable, the service provider performing the examination activities on their behalf, should have a detailed understanding of the validation documentation. As described in Annex 1 on agreements, the validation documentation should be made available to the inspectors in a timely manner, irrespective of whether it is provided by the responsible party or the vendor of the system. Contractual arrangements should be made to ensure continued access to this documentation for the legally defined retention period even if the sponsor discontinues the use of the system or if the vendor discontinues to support the system or ceases its activities. In case the vendor’s validation activities and documentation are insufficient, or if the responsible party cannot rely on the vendor to provide documentation, the responsible party should validate the system. Any difference between the test and the production configuration and environment should be documented and its significance assessed and justified. Interfaces between systems should be clearly defined and validated e.g. transfer of data from one system to another. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 31/52 A2.2 User requirements Critical system functionality implemented and used in a clinical trial should be described in a set of user requirements or use cases, e.g. in a user requirements specification (URS). This includes all functionalities, which ensure trial conduct in compliance with ICH E6 and which include capturing, analysing, reporting and archiving clinical trial data in a manner that ensures data integrity. User requirements should include, but may not be limited to operational, functional, data integrity, technical, interface, performance, availability, security, and regulatory requirements. The above applies independently of the sourcing strategy of the responsible party or the process used to develop the system. Where relevant, user requirements should form the basis for system design, purchase, configuration, and customisation; but in any case, they should constitute the basis for system validation. The responsible party should adopt and take full ownership of the user requirements, whether they are documented by the responsible party, by a vendor or by a service provider. The responsible party should review and approve the user requirements in order to verify that they describe the functionalities needed by users in their particular clinical trials. User requirements should be maintained and updated as applicable throughout a system’s lifecycle when system functionalities are changed. A2.3 Trial specific configuration and customisation The configuration and customisation of a system for use in a specific trial should be pre-specified, documented in detail and verified as consistent with the protocol, with the data management plan and other related documents. Trial specific configuration and customisation should be quality controlled and tested as applicable before release for production. It is recommended to involve users in the testing activities. The same process applies to modifications required by protocol amendments. If modifications to a system are introduced due to a protocol amendment, e.g. to collect additional information, it should be determined whether they should be applied to all trial participants or only to those concerned by the amendment. If new functionalities or interfaces need to be developed, or new code added, they should be validated before use. A2.4 Traceability of requirements Traceability should be established and maintained between each user requirement and test cases or other documents or activities, such as standard operating procedures, as applicable. This traceability may have many forms and the process may be automated by software. It should be continuously updated as requirements are changed to ensure that where applicable, for every requirement, there is a corresponding test case or action, in line with the risk evaluation. A2.5 Validation and test plans Validation activities should be planned, documented, and approved. The validation plan should include information on the validation methodology, the risk-based approach taken and if applicable, the division of tasks between the responsible party and a service provider. Prior to testing, the risk assessment should define which requirements and tests are related to critical system functionality. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 32/52 Test cases should be pre-approved. They may have many formats and while historically consisting of textual documents including tables with multiple columns corresponding to the elements below, they may also be designed and contained in dedicated test management systems, which may even allow automatic execution of test cases (e.g. regression testing). However, expectations to key elements are the same. Test cases should include: • the version of the software being tested; • any pre-requisites or conditions prior to conducting the test; • a description of the steps taken to test the functionality (input); • the expected result (acceptance criteria). Test cases should require the tester to document the actual result as seen in the test step, the evidence if relevant and, if applicable, the conclusion of the test step (pass/fail). Where possible, the tester should not be the author of the test case. In case of test failure, the potential impact should be assessed and subsequent decisions regarding the deviations should be documented. A2.6 Test execution and reporting Test execution should follow approved protocols and test cases (see section A2.5), the version of the software being tested should be documented, and where applicable and required by test cases and test procedures, evidence (e.g. screen shots) should be captured to document test steps and results. Where relevant, the access rights (role) and the identification of the person or automatic testing tool performing tests should be documented. Where previously passed scripts are not retested along with the testing of fixes for previous failing tests, this should be risk assessed and the rationale should be documented. Deviations encountered during system validation should be recorded and brought to closure. Any failure to meet requirements pre-defined to be critical should be solved or mitigating actions should be implemented prior to deployment. All open deviations and any known issues with the system at the time of release should be assessed and subsequent decisions should be documented in the validation report and, if applicable, in the release notes. The validation report should be approved by the responsible party before release for production. A2.7 Release for production The responsible party should sign off the release prior to initial use. Training materials, user guides and any other resources required for users should be available at the time of release. A2.8 User helpdesk There should be a mechanism to report, record, and solve defects and issues raised by the users e.g. via a helpdesk. Defects and issues should be fixed in a timely manner. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 33/52 A2.9 Periodic review Validation of a system should be maintained throughout the full system life cycle. Periodic system reviews should be conducted to assess and document whether the system can still be considered to be in a validated state, or whether individual parts or the whole system needs re-validation. Depending on the system type and application, the following elements (non-exhaustive list) should be evaluated and concluded, both individually and in combination: • changes to hardware/infrastructure; • changes to operating system/platform; • changes to the application; • changes to security procedures; • changes to backup and restore tools and procedures; • configurations or customisations; • deviations (or recurrence thereof); • performance incidents; • security incidents; • open and newly identified risks; • new regulation; • review of system accesses; • updates of agreements with the service provider. These elements should be reviewed whether the system is hosted by the responsible party or by a service provider. A2.10 Change control There should be a formal change control process. Requests for change should be documented and authorised and should include details of the change, risk-assessment (e.g. for data integrity, current functionalities and regulatory compliance), impact on the validated state and testing requirements. For trial specific configurations and customisations, the change request should include the details of the protocol amendment if applicable. As part of the change control process, all documentation should be updated as appropriate (e.g. requirements, test scripts, training materials, user guide) and a report of the validation activities prepared and approved prior to release for production. The system should be version controlled. The responsible party should ensure that any changes to the system do not result in data integrity or safety issues or interfere with the conduct of an ongoing trial. The investigator should be clearly informed of any change to a form (e.g. electronic case report form [eCRF] or electronic clinical outcome assessment [eCOA] page) and it should be clear when such changes were implemented. The documentation relating to the validation of previous or discontinued system versions used in a clinical trial should be retained (see 'Guideline on the content, management and archiving of the clinical trial master file (paper and/or electronic)' [EMA/INS/GCP/856758/2018], section 6.3). Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 34/52 Annex 3 User management A3.1 User management Organisations should have a documented process in place to grant, change and revoke system accesses in a timely manner as people start, change, and end their involvement/responsibility in the management and/or conduct of the clinical trial projects. Access to the system should only be granted to trained site users when all the necessary approvals for the clinical trial have been received and all documentation is in place (e.g. signed protocol and signed agreement with the investigator). This also applies to any updates to the system, e.g. changes resulting from a protocol amendment should only be made available to users once it is confirmed that the necessary approvals have been obtained, except where necessary to eliminate an immediate hazard to trial participants. A3.2 User reviews At any given time, an overview of current and previous access, roles and permissions should be available from the system. This information concerning actual users and their privileges to systems should be verified at suitable intervals to ensure that only necessary and approved users have access and that their roles and permissions are appropriate. There should be timely removal of access no longer required, or no longer permitted. A3.3 Segregation of duties System access should be granted based on a segregation of duties and also the responsibilities of the investigator and the sponsor, as outlined in ICH E6. Users with privileged or 'admin access' have extensive rights in the system (operating system or application), including but not limited to changing any system setting (e.g. system time), defining or deactivating users (incl. 'admin users'), activate or deactivate audit trail functionality (and sometimes even edit audit trail information) and making changes to data that are not captured in the audit trail [e.g. backend table changes in the database(s)]). There is a risk that these privileges can be misused. Consequently, users with privileged access should be sufficiently independent from and not be involved in the management and conduct of the clinical trial and in the generation, modification, and review of data. Users of computer clients [e.g. personal computer (PC)] which record or contain critical clinical trial data, should generally not have 'admin access' to the relevant equipment and when this is not the case, it needs to be justified. Unblinded information should only be accessible to pre-identified user roles. A3.4 Least-privilege rule System access should be assigned according to the least-privilege rule, i.e. users should have the fewest privileges and access rights for them to undertake their required duties for as short a time as necessary. A3.5 Individual accounts All system users should have individual accounts. Sharing of accounts (group accounts) is considered unacceptable and a violation of data integrity and ICH E6 principles as data should be attributable. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 35/52 A3.6 Unique usernames User access should be unique within the system and across the full life cycle of the system. User account names should be traceable to a named owner and accounts intended for interactive use and those assigned to human users should be readily distinguishable from machine accounts. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 36/52", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "Annex 4 Security", "definition": "A4.1 Ongoing security measures The responsible party should maintain a security system that prevents unauthorised access to the data. Threats and attacks on systems containing clinical trial data and corresponding measures to ensure security of such systems are constantly evolving, especially for systems and services being provided over or interfacing the internet. A4.2 Physical security Computerised systems, servers, communication infrastructure and media containing clinical trial data should be protected against physical damage, unauthorised physical access, and unavailability. The extent of security measures depends on the criticality of the data. The responsible party should ensure an adequate level of security for data centres as well as for local hardware such as universal serial bus (USB) drives, hard disks, tablets, or laptops. At a data centre hosting clinical trial data, physical access should be limited to the necessary minimum and should generally be controlled by means of two-factor authentication. The data centre should be constructed to minimise the risk of flooding, there should be pest control and effective measures against fire, i.e. cooling, and fire detection and suppression. There should be emergency generators and uninterruptable power supplies (UPS) together with redundant Internet protocol providers. In case of co-location (see section 6.7 Cloud solutions), the servers should be locked up and physically protected (e.g. in cages) to prevent access from other clients. Media (e.g. hard disks) should be securely erased or destroyed before disposal. Data should be replicated at an appropriate frequency from the primary data centre to a secondary failover site at an adequate physical distance to minimise the risk that the same fire or disaster destroys both data centres. A disaster recovery plan should be in place and tested. A4.3 Firewalls In order to provide a barrier between a trusted internal network and an untrusted external network and to control incoming and outgoing network traffic (from certain IP addresses, destinations, protocols, applications, or ports etc.), firewall rules should be defined. These should be defined as strict as practically feasible, only allowing necessary and permissible traffic. As firewall settings tend to change over time (e.g. as software vendors and technicians need certain ports to be opened due to installation or maintenance of applications), firewall rules and settings should be periodically reviewed. This should ensure that firewall settings match approved firewall rules and the continued effectiveness of a firewall. A4.4 Vulnerability management Vulnerabilities in computer systems can be exploited to perform unauthorised actions, such as modifying data or making data inaccessible to legitimate users. Such exploitations could occur in operating systems for servers, computer clients, tablets and mobile phones, routers and platforms (e.g. databases). Consequently, relevant security patches for platforms and operating systems should be applied in a timely manner, according to vendor recommendations. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 37/52 Systems, which are not security patched in a timely manner according to vendor recommendations, should be effectively isolated from computer networks and the internet, where relevant. A4.5 Platform management Platforms and operating systems for critical applications and components should be updated in a timely manner according to vendor recommendations, in order to prevent their use in an unsupported state. Unsupported platforms and operating systems, for which no security patches are available, are exposed to a higher risk of vulnerability. Validation of applications on the new platforms and operating systems and of the migration of data should be planned ahead and completed in due time prior to the expiry of the supported state. Unsupported platforms and operating systems should be effectively isolated from computer networks and the internet. It should be ensured that software used in clinical trials remains compatible with any changes to platforms/operating systems in order to avoid unintended impact on the conduct/management of the clinical trial due to interruption of functionality or requirements for alternative software and data migration. A4.6 Bi-directional devices The use of bi-directional devices (e.g. USB devices), which come from or have been used outside the organisation, should be strictly controlled as they may intentionally or unintentionally introduce malware and impact data integrity, data availability, and rights of trial participants. A4.7 Anti-virus software Anti-virus software should be installed and activated on systems used in clinical trials. The anti-virus software should be continuously updated with the most recent virus definitions in order to identify, quarantine, and remove known computer viruses. This should be monitored. A4.8 Penetration testing For systems facing the internet, penetration testing should be conducted at regular intervals in order to evaluate the adequacy of security measures and identify vulnerabilities in system security (e.g. code injection), including the potential for unauthorised parties to gain access to and control of the system and its data. Vulnerabilities identified, especially those related to a potential loss of data integrity, should be addressed and mitigated in a timely manner. A4.9 Intrusion detection and prevention An effective intrusion detection and prevention system should be implemented on systems facing the internet in order to monitor the network for successful or unsuccessful intrusion attempts from external parties and for the design and maintenance of adequate information technology (IT) security procedures. A4.10 Internal activity monitoring An effective system for detecting unusual or risky user activities (e.g. shift in activity pattern) should be in place. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 38/52 A4.11 Security incident management Organisations managing clinical trial data should have and work according to a procedure that defines and documents security incidents, rates the criticality of incidents, and where applicable, implements effective corrective and preventive actions to prevent recurrence. In cases where data have been, or may have been, compromised, the procedures should include ways to report incidents to relevant parties where applicable. When using a service provider, the agreement should ensure that incidents are escalated to the sponsor in a timely manner for the sponsor to be able to report serious breaches as applicable, in accordance with Regulation (EU) No 536/2014. A4.12 Authentication method The method of authentication in a system should positively identify users with a high degree of certainty. Methods should be determined based on the type of information in the system. A minimum acceptable method would be user identification and a password. The need for more stringent authentication methods should be determined based on a risk assessment of the criticality of the data and applicable legislation (including data protection legislation), and generally should include two-factor authentication. User accounts should be automatically locked after a pre-defined number of successive failed authentication attempts, either for a defined period of time, or until they are re-activated by a system administrator after appropriate security checks. Biometric approaches are currently not specifically addressed by ICH E6. If using biometrics to authenticate the creation of a signature, the investigator and sponsor should ensure that these fulfil the above-mentioned requirements and local legal requirements. A4.13 Remote authentication Remote access to clinical trial data, e.g. to cloud-based systems, raises specific challenges. The level of security should be proportionate to the sensitivity and confidentiality of the data (e.g. nominative data in electronic medical records are highly sensitive) and to the access rights to be granted (read-only, write or even 'admin' rights). A risk-based approach should be used to define the type of access control required. Depending on the level of risk, two-factor authentication may be appropriate or necessary. Two-factor authentication implies that two of the following three factors be used: • something you know, e.g. a user identification and password • something you have, e.g. a security token, a certificate or a mobile phone and an SMS pass code • something you are, e.g. a fingerprint or an iris scan (biometrics) A4.14 Password managers A secure and validated password manager, with a unique, robust user authentication each time it is used to log into a web site or system, can help to create and use different, complex passwords for each site or system. However, attention should be paid to insufficiently secured password managers. Password managers built into web browsers may save and automatically fill in user identification and passwords, regardless of whether an independent secure password manager is used or not. This poses a risk if uncontrolled equipment is used (e.g. personal equipment, shared equipment or user accounts), as user access control cannot be enforced; a risk that needs to be effectively mitigated. A policy or contractual arrangement would not be considered adequate to provide a sufficient level of security in such situations. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 39/52 The risk linked to the potential hacking of user equipment or to key loggers should also be considered. A4.15 Password policies Formal procedures for password policies should be implemented. The policies should include but not necessarily be limited to length, complexity, expiry, login attempts, and logout reset. The policies should be enforced by systems and verified during system validation. A4.16 Password confidentiality Passwords should be kept confidential, sharing of passwords is unacceptable and a violation of data integrity. Passwords initially received from the system or from a manager or system administrator should be changed by the user on their first connection to the system. This should be mandated by the system. A4.17 Inactivity logout Systems should include an automatic inactivity logout, which logs out a user after a defined period of inactivity. The user should not be able to set the inactivity logout time (outside defined and acceptable limits) or deactivate the functionality. Upon inactivity logout, a re-authentication should be required (e.g. password entry). A4.18 Remote connection When remotely connecting to systems over the internet, a secure and encrypted protocol (virtual private network (VPN) and/or hypertext transfer protocol secure (HTTPS)) should be used. A4.19 Protection against unauthorised back-end changes The integrity of data should be protected against unauthorised back-end changes made directly on a database by a database administrator. A method to prevent such changes could be by setting the application up to encrypt its data on the database or by storing data un-encrypted with an encrypted copy. In either case, the database administrator should not be identical to the administrator of the application. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 40/52 Annex 5 Additional consideration to specific systems All computerised systems used in clinical trials should fulfil the requirements and general principles described in the previous sections. The following sub-sections define more specific wording for selected types of systems where the GCP inspectors’ working group (GCP IWG) has found that supplemental guidance is needed. For electronic trial master files (eTMFs), please refer to the respective guideline1. A5.1 Electronic clinical outcome assessment Electronic clinical outcome assessment (eCOA) employs technology in addition to other data acquisition tools for the reporting of outcomes by investigators, trial participants, care givers and observers. This guideline does not address the clinical validation or appropriateness of particular eCOA systems. The guideline aims at addressing the topics specifically related to these eCOA systems and also to those related to the situation where bring-your-own-device (BYOD) solutions are used. Data can be collected by any of several technologies and will be transferred to a server. Data should be made available to involved/responsible parties such as the investigator e.g. via portals, display of source data on the server, generation of alerts and reports. These processes should be controlled and clearly described in the protocol (high-level) and protocol-related documents, and all parts of the processes should be validated. Collecting data electronically may offer more convenience to some trial participants and may increase participant compliance, data quality, reduce variability, reduce the amount of missing data (allowing automatic reminders) and potentially reduce data entry errors. Of importance, whilst use of such measures might be of benefit to some trial participants and patient groups, it may be inconvenient for or even result in the exclusion of others. This should be considered when using any data acquisition tool and the choice should be justified. A5.1.1 Electronic patient reported outcome A5.1.1.1 System design Electronic patient reported outcome (ePRO) should be designed to meet the specific needs of the end users. It is recommended to involve representatives of intended site staff and of the intended trial participant population, where relevant, in the development and testing. One of the advantages of using an ePRO system is that the timestamps of data entry are recorded. The timestamp should record the time of the data entry and not only the time of the data submission/transmission. Trial participants should be able to view their own previously entered data, unless justified and unless it is against the purpose of the clinical trial design or the protocol. Therefore, the period that data are viewable by the participant should be considered when designing/configuring the ePRO. Decisions about the 'view-period' should be based on considerations regarding risk for bias on data to be entered. If viewing of recently entered data is not possible by the participant, then there is a risk that the participant could forget if relevant data have been collected. This is especially the case if the planned entry is event- driven. In addition, this prevents an unnecessary burden to site staff, as they will be contacted by trial participants in case of doubt less often. Logical checks should be in place to prevent unreasonable data changes such as 'time travel' e.g. going back (months, years in time) or forward into the future based on the protocol design. 1 Guideline on the content, management and archiving of the clinical trial master file (paper and/or electronic) (EMA/INS/GCP/856758/2018). Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 41/52 It should be considered to include a scheduling/calendar component with alerts or reminders to assist compliance. A5.1.1.2 Data collection and data transfer The same ICH E6 standards apply to data collected via ePRO as to any other method of data collection, i.e. that there are processes in place to ensure the quality of the data, and that all clinical information is recorded, handled and stored in such a way as to be accurately reported, interpreted and verified. An ePRO system typically requires an entry device. Data saved on the device is the original record created by the trial participant. Since the data stored in a temporary memory are at higher risk of physical loss, it is necessary to transfer the data to a durable server at an early stage, by a validated procedure and with appropriate security methods during data transmission. Data should be transferred to the server according to a pre-defined procedure and at pre-defined times. The data saved on the device are considered source data. After the data are transferred to the server via a validated procedure, the original data can be removed from the device as the data on the server are considered certified copies. The sponsor should identify the source data in the protocol and protocol-related documents and should document the time and locations of source data storage. In addition to the general requirements on audit trails (please refer to section 6.2.), if an ePRO system is designed to allow data correction, the data corrections should be documented, and an audit trail should record if the data saved on the device are changed before the data are submitted. Data loss on devices should be avoided. Procedures should be in place to prevent data loss if web access to the trial participant reported data is interrupted, (e.g. server outage, device battery drained, loss of or unstable internet connection). There should be a procedure in place to handle failed or interrupted data transmission. It should be ensured/monitored that the transmission of data from ePRO devices is successfully completed. Important actions should be time-stamped in an unambiguous way, e.g. data entries, transfer times and volume (bytes). A5.1.1.3 Investigator access Unlike data collected in the electronic case report form (eCRF), ePRO data are not managed (although available for review) by the investigator and are often hosted by a service provider. The investigator is overall responsible for the trial participants’ data (including metadata). Those should consequently be made available to the investigator in a timely manner. This will allow the investigator to fulfil their responsibilities for oversight of safety and compliance and thereby minimise the risk of missed adverse events or missing data. A5.1.1.4 Data changes As stated in section 6.2.1. on audit trails, a procedure should be in place to address and document if a data originator (e.g. investigator or trial participant) realises that they have submitted incorrect data by mistake and want to correct the recorded data. Data changes for ePRO typically differ from that of other data acquisition tools because trial participants typically do not have the possibility to correct the data in the application. Hence, procedures need to be in place in order to implement changes when needed. This depends on the design of tools and processes and could be in the form of data clarification processes initiated by trial participants on their own reported data or initiated by investigators. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 42/52 Data reported should always be reliable. Data clarification procedures introduced by the sponsor or service provider, whether or not described in the protocol should not prohibit changes in trial participant data when justified e.g. if the trial participant realises that the data have not been entered correctly. It is expected that the possibility for changes is implemented based on a justified and trial specific risk- assessment and that any changes are initiated in a timely manner by the participant or site staff and in case of the latter is based on a solid source at investigator sites e.g. phone notes or emails from trial participants documenting the communication between sites and trial participants immediately after the error was made/discovered. One of the advantages of direct data entry by the trial participant is that recall bias is minimised as the data are entered contemporaneously. Consequently, corrections should not be done at a much later stage without good reason and justification. Whether collected on paper or by electronic means, the regulatory requirements are that all clinical data should be accurately reported and should be verifiable in relation to clinical trials. It is expected that the number of changes to ePRO data are limited; however, this requires both designs of ePROs that are appropriate to ensure proper understanding by trial participants and appropriate training of trial participants, thereby avoiding entry errors. A5.1.1.5 Accountability of devices There should be an accountability log of devices handed out to trial participants and this should include the device identification number in order to be reconciled to a particular trial participant. A5.1.1.6 Contingency processes Contingency processes should be in place to prevent loss of data critical for participant safety or trial results. In case of device malfunction or loss of devices, there should be a procedure in place to replace the device and to merge data from several devices of a trial participant without losing traceability. A5.1.1.7 Username and password The trial participant’s passwords should only be known to the trial participant. The username and password should not be used in a manner that would breach a trial participant’s confidentiality. In relation to BYOD, sponsors should ensure that basic user access controls are implemented. When mobile applications are used for data entry, access controls need to be in place to ensure attributability. See section A5.1.3 for further guidance on BYOD. A5.1.1.8 Training Training should be customised to meet the specific needs of the end users. A5.1.1.9 User support Support to the trial participant and the trial site staff should be readily available (e.g. support via phone or email) in order to ensure reliable data and minimise the risk of data loss. Trial participant confidentiality should be ensured at all times, including in the communication process. Procedures for service desk, user authentication and access restoration should be implemented. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 43/52 A5.1.2 Clinician reported outcome Tools to directly collect clinician reported outcomes should generally follow the same requirements as those described for systems in general and for ePROs. The main difference is the user (investigators, other clinicians, or independent assessors instead of trial participants), not the system requirements. Special attention should be given to access control in order to avoid jeopardising any blinding, when relevant. A5.1.3 Bring your own device Both ePRO data and clinician reported outcome data may be captured by privately owned devices such as mobile phones, tablets, computers and wearables, i.e. BYOD. This can either be achieved via a web- application with pre-installed browser applications or by installing an application on the device. Solutions can be either a combination of web and application (hybrid) or coded to the device operating system (native). It is necessary to provide alternative ways of data collection e.g. devices provided by the sponsor, as the trial participants should not be excluded from a trial if not capable of or willing to use BYOD. A5.1.3.1 Technical and operational considerations When using BYOD, a variety of devices, operating systems and where applicable web browsers commonly used, should be considered for the application. It should be ensured that it is not exclusive to one model or operating system. The sponsor should describe the minimum technical specifications for participants’ devices (e.g. operating system, web browser and storage capacity). These should take into account which operating systems are still supported by the manufacturer and if bug fixes and security patches have been released, when relevant. The sponsor should ensure the quality and integrity of the data across all accepted models and versions. The sponsor has no control over the implementation of updates to the operating system or over the applications on the trial participant’s device. These aspects should be taken into consideration in their risk evaluation and subsequent validation activities. The application should use an external source for date and time and should not rely on information from the user's device. Procedures and processes should be in place for when the trial participant discontinues the clinical trial or the clinical trial ends and access to applications and data collection should be terminated. A5.1.3.2 Considerations on security and trial participant confidentiality The confidentiality of data that could identify trial participants should be protected, respecting the privacy and confidentiality rules in accordance with the applicable regulatory requirements. A number of challenges for BYOD are related to security, and security should be ensured at all levels (mobile device security, data breach security, mobile application security, etc.). As mobile devices may be lost or stolen and it cannot be ensured that the trial participants use any authentication methods to secure their device, access control should be at the application level. Section A.4.14 on the use of password managers also applies. Risks linked to known application and operating system vulnerabilities should be minimised. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 44/52 The hardware, operating system and applications are all factors that affect the total security status of the device, and there should be procedures in place regarding e.g., when trial participants/clinicians use less secure devices. Data capture by BYOD may require the device to be identified to ensure data attributability. Only information that is needed for proper identification of and service to the user should be obtained. Trial participant confidentiality should be ensured if device identification information is stored. Access to the application and trial participant data may be protected with multiple barriers (e.g. unlock mobile phone, open application, access data). If the device’s built-in capabilities for auto fill formula data and/or using photo, video, and global positioning system (GPS) data, etc. are used, this should be described and justified in the protocol. Procedures and processes should ensure that only protocol mandated data are collected, and that the confidentiality of data is maintained. In accordance with the principle of 'data minimisation' mobile applications should only collect data that are necessary for the purposes of the data processing and not access any other information on the person’s device. For example, location data should only be collected if it is necessary for the clinical trial activities and the trial participant must be informed about it in the patient information and agree to it in the consent form. Providers may have end-user licensing agreements or terms of service that allow the sharing of data. This may be in conflict with ICH E6 and (local) legal requirements or require information to be provided to the participant and may require specific informed consent. In some cases, the application may not be suitable for use. If an application is to be installed on a BYOD, the privacy labels/practices (e.g. regarding tracking data, linked and not linked data) should be clearly communicated to the trial participant upfront. The sponsor should be aware that explicit consent may be required related to the above. The informed consent should describe the type of information that will be collected via ePRO and how that information will be used. A5.1.3.3 Installation and support When using an application, it is recommended that appropriately trained staff assist in the installation even if the application is available through an app-store or service provider platform. Independently of whether the BYOD solution is based on an application installed on the device or a website/web application, the software and the use should be explained thoroughly via targeted training, which may include user manuals, one-to-one training, and multimedia tools. Users of the system should have access to user support e.g. from a help desk. There should be a procedure in place in case an application cannot be installed, or the web service is unavailable on a device, if the device has malfunctioned or the participant has purchased a new device. Helpdesk contacts by users should be logged (participant or site staff study ID, purpose of contact, etc.) with due consideration of protecting participant information. The software and software installation should not limit or interfere with the normal operations of the device. Any unavoidable limitation to the device after installation should be part of the informed consent material. A5.1.3.4 Uninstallation It should be possible to uninstall software or applications without leaving residues on BYOD devices, e.g. entries in the registry, incorrect mappings or file fragments. The user should be able to uninstall at any time without expertise or assistance. The uninstallation process should not compromise the device. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 45/52 A5.2 Interactive response technology system A5.2.1 Testing of functionalities In addition to the content of the sections A2.6, A2.10, of this guideline, sponsors should also consider the issues mentioned below when writing test scripts for user acceptance tests (UAT). A5.2.1.1 Dosage calculations Where dosage calculations/assignments are made by the IRT system based on user entered data (e.g., trial participant body surface area or weight), and look-up tables (dosage assignment based on trial participant parameters), the tables should be verified against the approved protocol and input data used to test allocations, including test data that would be on a borderline between differing doses. Assigning the incorrect dosage to a trial participant is a significant risk to safety and well-being and such inaccurate assignments should be thoroughly mitigated. A5.2.1.2 Stratified randomisation Where the randomisation is stratified by factors inputted by the user, all the combinations of the strata should be tested to confirm that the allocation is occurring from the correct randomisation table. A5.2.1.3 Blinding and unblinding Unblinded information should only be provided and accessible to pre-identified user roles. A5.2.2 Emergency unblinding The process for emergency unblinding should be tested. A backup process should also be in place in case the online-technology emergency unblinding is unavailable. It should be verified that a site’s ability for emergency unblinding is effectively available before administering IMP to a trial participant. A5.2.3 IRT used for collection of clinical data from the trial site Where the IRT system is collecting clinical data, important data should be subject to source data verification and/or reconciliation with the same data collected in the data acquisition tool. For example, the data used for stratification may also be contained in the data acquisition tool. Where clinical data is entered into the IRT system and integrated in the electronic data collection (EDC) system (electronic data transfer to EDC) the additional functionality and ICH E6 requirement concerning data acquisition tools (eCRFs) should be addressed in the IRT system requirements and UAT e.g. investigator control of site entered data, authorisation of data changes by the investigator, authorisation of persons entering/editing data in the system by the investigator. A5.2.4 Web-based randomisation Where justified, sponsor or investigator/sponsor may also use a web-based application to create randomisation lists for clinical trials. When using a web-service, the process to evaluate the suitability of the system and GCP compliance as well as the fitness for purpose of the created randomization list should be documented. The version of the service used, and where applicable, the seed should be maintained. Ad hoc randomization via a web-service is not recommended as randomization distribution is unknown, the sponsor is not in control of the process e.g. the seed may vary. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 46/52 The sponsor should ensure that the process of randomisation can be reconstructed via retained documentation and data and that a final randomisation schedule is retained. A5.3 Electronic informed consent Ethics committees will review all material related to the informed consent process. Before the implementation of an electronic consent procedure is considered, the sponsor should ensure that the electronic consent procedure is GCP compliant and legally acceptable in accordance with the requirements of the independent ethics committees concerned and of the national regulatory authorities. The principles of consent as set out in legislation and guidance should be the same regardless of whether the process involves a computerised system. A hybrid approach could be considered, where national requirements preclude cern parts of an electronic informed consent procedure. At present, in some countries failure to provide 'written on paper' proof of a trial participant’s informed consent is considered a legal offense. An electronic informed consent refers to the use of any digital media (e.g. text, graphics, audio, video, podcasts or websites) firstly to convey information related to the clinical trial to the trial participant and secondly to document informed consent via an electronic device (e.g. mobile phones, tablets or computers). The electronic informed consent process involves electronic provision of information, the procedure for providing the opportunity to inquire about details of the clinical trial including the answering of questions and/or electronic signing of informed consent. For example, it would be possible for the trial participant to sign informed consent on a paper form following provision of the information electronically or the information and informed consent could be entirely electronic. If using a ‘wet ink’ signature together with an electronic informed consent document (a hybrid approach), the patient information, the informed consent document and the signature should be indisputably linked. The method of obtaining an informed consent should ensure the broadest possible access to clinical trials. Alternative methods for provision of information and documentation of informed consent should be available for those unable or unwilling to use electronic methods. Any sole use of electronic informed consent should be justified and described in the protocol. A5.3.1 Provision of information about the clinical trial The trial participants should have been informed of the nature, objectives, significance, implications, the expected benefit, risks, and inconveniences of the clinical trial in an interview with the investigator, or another member of the investigating team delegated by the principal investigator. The interview should take into account the individual disposition (e.g. comorbidities, patient references, etc.) of the potential participant (or legal representative). This interview should allow interaction, the asking of questions and allow confirmation of the trial participant’s identity and not just simply the provision of information. The interview should be conducted in person or, it could be done remotely where this can be justified and is allowed nationally and if approved by an ethics committee using electronic methods that allow for two- way communication in real time. Whichever method is used it is important that confidentiality is maintained, and therefore communication methods should be private/secure. Consideration should be given as to how the system would be presented to the ethics committee for approval so that it captures the functionality of the system and the experience of the potential trial participant using it. Direct system access should be provided to the ethics committee upon request in a timely manner. Provision of the information electronically may improve the trial participants’ understanding of what taking part in the clinical trial will involve. Computerised systems could facilitate features to assess the Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 47/52 participant’s understanding e.g. via questions at key points, which self-evaluate trial participants' understanding as they work their way through the information. This, in turn, can be used to highlight areas of uncertainty to the person seeking consent so that they can cover this area in more detail with the trial participant. A5.3.2 Written informed consent The informed consent of the trial participant should be in writing and electronic methods for documenting the trial participant’s informed consent should ensure that the informed consent form is signed and personally dated by at least two (natural) persons; the trial participant or the trial participant’s legal representative, and the person who conducted the informed consent discussion. The identity of the persons signing should be ensured. The method used to document consent should follow national legislation with regard to e.g. acceptability of electronic signatures (see section 4.8.), and in some countries 'wet ink' signature will be required. There should be no ambiguity about the time of signature. The system should use timestamps for the audit trail for the action of signing and dating by the trial participant and investigator or qualified person who conducted the informed consent interview, which cannot be manipulated by system settings. Any alterations of the document should invalidate the electronic signature. If an electronic signature is used, it should be possible for monitors, auditors, and inspectors to access the signed informed consent forms and all information regarding the signatures, including the audit trail. Secure archiving should ensure availability and legibility for the required retention period. A5.3.3 Trial participant identity It should always be possible to verify the identity of a trial participant with documentation available to the investigator. Documentation which makes it possible to demonstrate that the person entering the electronic 'signature' was indeed the signatory, is required. The electronic signing should be captured by the audit trail. Where consent is given remotely, and the trial participant is required at some point to visit a clinical trial site for the purposes of the trial, verification should be done in person e.g. by using information from an official photo identification if such an ID document is required in the trial site country. A5.3.4 Sponsor notification on the consent process Notification to the sponsor should only contain essential, non-personal identifiable information to allow the sponsor to have an overview of how many trial participants have been enrolled in a clinical trial so far and which versions of the electronic informed consent form have been used. Remote access to personal identifiable information in the electronic system should only be permitted for the corresponding participant, legal representative, investigator, monitor, auditor, or inspector. Any unjustified accesses, which lead to the disclosure of non-pseudonymised information, are likely to be viewed as an infringement of data privacy laws. A5.3.5 Trial participant confidentiality As for all other computerised systems in clinical trials, the confidentiality of data that could identify trial participants should be protected, respecting the privacy and confidentiality rules in accordance with applicable national and EU regulatory requirements. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 48/52 A5.3.6 Trial participant access Potential trial participants (or, where applicable, their legal representative) should be provided with access to written information about the clinical trial prior to seeking their informed consent. The trial participant should be provided with their own copy of the informed consent documentation (including all accompanying information and all linked information) once their consent has been obtained. This includes any changes to the data (documents) made during the process. The information about the clinical trial should be a physical hard copy or electronic copy in a format that can be downloaded. The copy should be available immediately to the trial participant. A5.3.7 Investigator responsibilities The investigator should take appropriate measures to verify the identity of the potential trial participant (see section A5.3.3) and ensure that the participant has understood the information given. The informed consent documents are essential documents that should be available at the trial site in the investigator TMF for the required retention period (see section A5.3.9). The investigator should retain control of the informed consent process and documentation (e.g. signed informed consent forms) and ensure that personal identifiable data are not inappropriately disclosed beyond the site. The system used should not limit the investigator’s ability to ensure that trial participants’ confidentiality is protected with appropriate access and retention controls in the system. The investigator should ensure an appropriate process for the copy of the informed consent documentation (information sheet and signed consent form) to be provided to the trial participant. All versions of signed and dated electronic consents should be available to the trial participant for the duration of and after the trial. The system used should ensure that the investigator can grant and revoke access to the electronic informed consent system to monitors, auditors and regulatory authority inspectors. A5.3.8 Version control and availability to sites The electronic informed consent information (electronic trial participant information and informed consent form) may be subject to updates and changes during the course of the trial. Regardless of the nature of the change or update, the new version containing relevant information has to receive the favourable opinion/approval of the ethics committee(s) prior to its use. Additional information should be made available to the ethics committee(s) concerning technical aspects of the electronic informed consent procedure to ensure continued understanding of the informed consent processes. Only versions approved by the ethics committee(s) should be enabled and used for the informed consent process and documentation. Release of electronic trial participant information and informed consent forms to the sites prior to IRB/IEC approval should be prevented. The system should prevent the use of obsolete versions of the information and informed consent document. A5.3.9 Availability in the investigator’s part of the trial master file All documents of the informed consent procedure (including all accompanying information and all linked information) are considered to be essential documents and should be archived as such. Replacement of the documents with copies is only acceptable if the copies are certified copies (see section 6.5.). A5.3.10 Withdrawal from the trial There should be procedures and processes in place for a trial participant to be able to withdraw their consent. If there is a possibility for the trial participant to withdraw from the trial through the computerised system, it should be ensured that such a withdrawal of consent generates an alert to the investigator in order to initiate the relevant steps as per protocol and according to the extent of Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 49/52 withdrawal. Any withdrawal of informed consent should not affect the results of activities already carried out, such as the storage and use of data obtained on the basis of informed consent before withdrawal. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 50/52 Annex 6 Clinical systems As stated in sections 2. and 4.6., computerised systems implemented at the trial site are also within the scope of this guideline, and the general approach towards computerised systems used in clinical practice is that the decision to use a system in a clinical trial should be risk proportionate and justified pre-trial. This section is dedicated to specific and additional considerations regarding electronic medical records and other systems implemented at sites, which are primarily used in clinical practice but are also generating clinical trial data. For computerised systems built specifically for data collection in clinical trials please refer to the relevant sections of this guideline. A6.1 Purchasing, developing, or updating computerised systems by sites The investigator/institution should have adequate facilities for a clinical trial. This also applies to the computerised systems of the institution if considered to be used for clinical trial purposes. It is recommended that institutions planning to perform clinical trials consider whether system functionality is fit for the clinical trial purpose. This should also be considered prior to the introduction of a new electronic medical record or equipment planned to be used in clinical trials (e.g. scanners, X-ray, electrocardiograms), or prior to changes to existing systems. To ensure that system requirements related to GCP compliance (e.g. audit trail for an electronic medical record) are addressed, experienced clinical trial practitioners should be involved by the institution in the relevant steps of the procurement and validation processes. As many systems are designed with different configuration options, it should be ensured that the systems are configured in a GCP compliant manner. A6.2 Site qualification by the sponsor As part of the site qualification, the sponsor should assess the systems in use by the investigator/institution to determine whether the systems are fit for their intended use in the clinical trial (e.g. include an audit trail). The assessment should cover all computerised systems used in the clinical trial and should include consideration of the rights, safety, dignity and wellbeing of trial participants and the quality and integrity of the trial data. If the systems do not fulfil the requirements, the sponsor should consider whether to select the investigator/institution. The use of systems not fulfilling requirements should be justified, either based on planned implementation of effective mitigating actions or a documented impact assessment of residual risks. A6.3 Training If the use of the systems in the context of a specific trial is different from the use in clinical practice e.g. different scanning procedures, different location of files, different requirements regarding documentation etc., trial specific training is required. A6.4 Documentation of medical oversight The investigator should be able to demonstrate their medical oversight of the clinical trial when electronic medical records are used. Where all or part of the entries into the medical records are made by a research nurse/dedicated data entry staff it can be difficult to reconstruct the investigator's input. The system Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 51/52 should allow the investigator to document the assessment and acknowledgement of information entered into the system by others. A6.5 Confidentiality Pseudonymised copies of electronic medical records may be provided to sponsors, or service providers working on their behalf, outside the clinical environment e.g. if needed for endpoint adjudication or safety assessments according to the protocol. National regulations need to be followed by the sites. In such cases there should be: • procedures in place at the site to redact copies of medical records, in order to protect the trial participants' identity, before transfer; • security measures in place, which are relevant to the process, including pseudonymisation and redaction; • a copy of the pseudonymised records and a proof of the transfer made at the site; • organisational and technical procedures in place on the receiving side to ensure that the requirements of the data protection regulation are met. Due to the sensitive nature of information documented in medical records, the extent to which sponsors request these data should be ethically and scientifically justified and limited to specific critical information. Any planned collection of redacted copies of medical records by the sponsor should be described in the protocol, or related documents, and should be explicit in the patient information. A6.6 Security Security measures that prevent unauthorised access to data and documents should be maintained. Please refer to section 5.4. regarding more details on the general requirements for security systems, which are equally applicable to research institutions. A6.7 User management Robust procedures on user management should be implemented (see Annex 3). For systems deployed by the investigator/institution, the investigator should ensure that individuals have secure and attributable access appropriate to the tasks they are delegated to in the trial. Robust processes for access rights are particularly important in trials where parts of the information could unblind the treatment. Such information should only be accessible to unblinded staff. A6.8 Direct access Sponsor representatives (monitors and auditors) and inspectors should have direct, read-only access to all relevant data for all trial participants as determined by the monitors, auditors or inspectors while taking the collected data and the clinical trial protocol into account. This may require access to several different sections or modules of the respective (medical) record e.g. imaging. This requires the use of a unique identification method e.g. username and password. The access of monitors, auditors and inspectors should be restricted to the trial participants (including potential participants screened but not enrolled in the trial) and should include access to audit trails. Guideline on computerised systems and electronic data in clinical trials EMA/INS/GCP/112288/2023 Page 52/52 If the site has accepted to provide remote access, appropriate security measures and procedures should be in place to support such access without jeopardising patient rights and data integrity and national legislation. A6.9 Trial specific data acquisition tools The electronic medical record contains information, which is crucial for the management of patients and are designed to fulfil legal requirements. Any trial specific data acquisition tools implemented cannot replace the medical record and their use should not result in a depletion of relevant information in the medical record. Monitoring activities should not be limited to information in the data acquisition tools and should also consider relevant information in the medical record. Please also refer to the published qualification opinion on eSource Direct Data Capture (DDC) EMA/CHMP/SAWP/483349/2019. A6.10 Archiving Appropriate archiving should be in place to ensure long term readability, reliability, retrievability of electronic data (and metadata), in line with regulatory retention requirements. Please also refer to section 6.11. Requirements for the retention of clinical trial data and documents are frequently different from requirements for other data and documents held by the investigators. It should be ensured that there is no premature destruction of clinical trial data in case of e.g. institution relocation or closure. It is the responsibility of the sponsor to inform the hospital, institution or practice as to when these documents will no longer need to be retained. There are specific requirements for backup, etc. of electronic data, which can be seen in section 6.8 and which are equally applicable to research institutions. Please also refer to the guideline on the content,", "sources": [ "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf" ], "file": "guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf", "type": "pdf" }, { "term": "additive effect", "definition": "Glossary Definition: The combined effect when two or more things are used together.
Use in Context: The additive effect of a combination drug is the sum of the effects of each drug acting alone.
More Info: For example, two vaccines may be combined into one shot because they do not interfere with each other and will still each have the same effect as if they were given as two separate shots.\n\nSo an additive effect means that 1+1 = 2
Other Info to Think About When Joining a Study: You may hear the words “additive effect” being used if you are part of a study that is studying two or more treatments being taken together.\n\nYou may want to find out more about why the two treatments are being given together and what the hoped effect could be.
Related Terms: synergistic effect", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "adherence", "definition": "Glossary Definition: Following the study directions and requirements.
Use in Context: Adherence to the study instructions is important so reliable information about the study treatment can be collected.
More Info: Adherence to the study protocol helps ensure the data from all study participants can be evaluated appropriately. \n\nReliable research results depend on both researchers and participants carefully following the protocol and study instructions.\n\nThe words 'adherence' and 'compliance' are often used in the same way.
Other Info to Think About When Joining a Study: During study visits, someone from the study team may ask about your adherence to the study directions. That could mean following the exact directions for taking the investigational medicine or writing journal entries at specific time points.\n\nWhen you are reading through the consent form you may see that it says you could be removed from the study by the investigator if you are unable to follow the study procedures.\n\nYou may wish to ask what you should do if you were unable to follow the study directions on a particular day. For example, if you forgot to take the study treatment at the right time.\n\nIf you have a hard time following the study procedures, let the study team know so they can help you.
Related Terms: compliance, following", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "adverse event", "definition": "Glossary Definition: Any health problem that happens during the study.
Use in Context: The study team needs to know about all adverse events that happen during the study.
More Info: Researchers track adverse events for safety reasons. They also want to find out whether any issues could be related to the study treatment. \n\nParticipants should tell the study team about any health problem that happens while they are in a study. Any event such as a fever, headache, cold, a mood change, or falling, should be reported.
Other Info to Think About When Joining a Study: You may see references to “adverse event” during the consent process when discussing procedures since many studies include specific time points to ask participants if they have experienced any health problems.\n\nYou may also see references to “adverse event” when discussing the risks of the study. Depending on the type of study you join, the risks you learn about are based on information participants in previous studies reported. It is important that you tell the study team about any health problem that happens during the study, even if you don’t think it’s related to the research.
Related Terms: adverse reaction, side effect", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "adverse reaction", "definition": "Glossary Definition: A health problem that happens during the study and is reported as possibly caused by the study treatment.
Use in Context: Adverse reactions are important to track so that the effects of the study treatment are known.
More Info: Adverse reactions are health problems that are related to the study treatment. A rash that develops only after taking a drug is an adverse reaction.
Other Info to Think About When Joining a Study: While participating in a study, the study team may discuss adverse reactions with you.\n\nAdverse reactions are health issues that have been found to be related to the study treatment. It is important for you to report any health problem or issue that happens while you are in a study, even if you don’t think it’s related.
Related Terms: adverse event, side effect", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "analyze", "definition": "Glossary Definition: To examine study data to answer a question and help reach conclusions.
Use in Context: Researchers analyze data to find out the results of a study.
More Info: In a research study, data are collected and then analyzed to answer the study questions. \n\nA study statistician helps analyze the data. When they analyze the data, they interpret the information and try to come to conclusions about what the study results mean.
Other Info to Think About When Joining a Study: You may often hear the term “analyze” used by the study team in the context of data. Someone on the research team will need to analyze the data collected in the research study.\n\nYou may want to ask how the data will be analyzed and what the researchers want to learn from the study.
Related Terms: evaluate, investigate, interpret, data analysis", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "anonymize", "definition": "Glossary Definition: Remove, change, or hide personal details to protect participant privacy.
Use in Context: When researchers anonymize data they remove all personal identifiers, so that the participant cannot be linked back to that data by anyone.
More Info: When data are anonymized, details such as name, birthdate, and address are removed so that any personal information is no longer available to anyone.
Other Info to Think About When Joining a Study: You may hear the term “anonymize” when the study team talks about the data they collect from you during the study and how that data will be protected. There are different ways to protect data. When data are anonymized, they cannot be linked back to any individual.\n\nAs a participant, you can ask the study team for more information about what data they will collect and how they will protect your personal information. You may also want to clarify if the information they collect from you will be anonymized.
Related Terms: unlink, mask", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "antibody", "definition": "Glossary Definition: A protein made by the body to fight an illness or infection.
Use in Context: Antibodies develop after an infection to help clear the infection and to prevent the infection from coming back.
More Info: An antibody is a protein that is produced by the body's immune system and causes an immune response.\n\nAntibodies can be found in various areas of the body, including the blood, skin, lungs, tears, saliva, and even breast milk. \n\nAntibodies can be produced in response to viruses, bacteria, parasites and even medications. Antibodies can sometimes recognize someone’s own body tissues and cause disease. \n\nIn some cases, antibodies can also be used as a treatment/therapy. For example, antibodies are sometimes used to fight cancer cells.
Other Info to Think About When Joining a Study: A research study may test whether a study treatment causes a person to develop antibodies to a disease. It may also test an antibody to see if it can treat a disease. \n\nIf you have any questions about antibodies or immune reactions, you can ask the study team.
Related Terms: immune system, immunoglobulin, protein, antigen, immunotherapy", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "antigen", "definition": "Glossary Definition: A substance that causes the body's immune system to react.
Use in Context: An antigen is something that your body does not recognize and tries to fight.
More Info: The body reacts to an antigen like a virus, bacteria, parasite, or tumor.\n\nImmune reactions include getting a fever, a rash or hives, or feeling sick.\n\nOne of the ways that the immune system protects itself from antigens is to produce antibodies.
Other Info to Think About When Joining a Study: You may see the term “antigen” in different research study situations. For example you may see it in the title of a research study, in the consent form, or in other study information. During the Covid pandemic, there was a lot of information about antigen tests.\n\nFeel free to ask a member of the study team for more information if you have questions about the term “antigen” being used in a research study.
Related Terms: antibody, immune response", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "arm", "definition": "Glossary Definition: A group of participants in a research study who all receive the same study treatment.
Use in Context: If a study has two arms, one group will receive one study treatment while a second group will receive a different study treatment such as a placebo or the standard of care.
More Info: Studies that compare two or more study treatments divide participants into separate arms to compare the effects of the different treatments.
Other Info to Think About When Joining a Study: You may see the term “arm” in the consent form or when the study team is explaining the research study to you. They may mention that there will be different arms in the study you are joining. You may be randomized to one arm or another\n\nYou can ask how many arms will be in a study. If there are arms, you can clarify how participants will be assigned to receive the different study treatments.
Related Terms: study arm, \ngroup, study assignment, randomization, cohort,\n treatment group", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "assent", "definition": "Glossary Definition: Willingness to take part in a research study by someone who is not able to give legal consent.
Use in Context: Some studies ask children, or people who have a guardian, to decide whether they agree to participate in research by giving their assent.
More Info: Assent can apply both to children and to adults who can't give legal informed consent. For example, an adult with severe dementia may no longer be able give consent but could be asked to assent\n\nConfirming the assent of children and adults who are unable to give legal consent treats them with respect, even if not legally necessary. \n\nFailing to object to being in a study is not considered assent. Assent can apply to children and adults who can't give legal informed consent. For example, in the case of an adult with dementia. \n\n To leave a study, a participant may take back their assent, or the legal guardian or authorized decision maker may take back their consent.
Other Info to Think About When Joining a Study: The word “assent” may come up if minors are joining a study. When applicable, the parent or guardian may be signing to give consent, but the young person should be given the opportunity to assent to become a participant. The study team may also say that even if the guardian provides consent, the young person joining the study will need to give their assent before enrolling.\n\nA minor who is enrolling in a study should feel free to ask as many questions about the research as necessary to feel comfortable becoming a participant.
Related Terms: agreement, consent", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "assent form", "definition": "Glossary Definition: A document used to explain the details of a research study to children or people who are unable to give legal consent.
Use in Context: An assent form provides information about the research in a way that is easy to understand.
More Info: An assent form provides research study information in a way that children and others who may have impaired decision-making can understand. \n\n Giving children and people with impaired decision-making an assent form that is designed for them helps them to understand the research and decide how they feel about the study.
Other Info to Think About When Joining a Study: If you are the parent or guardian of a child, you will be deciding about the study and signing the consent form. You can ask the study team if there is an assent form for the child to understand the study.\n\nA similar process can be followed if a study is enrolling adults who are not able to make decisions on their own about being in a research study.\n\nYou can use the assent form to guide a conversation to make sure the potential participant agrees with being in the research study.
Related Terms: assent, minor, guardian, consent, consent form", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "assessment", "definition": "Glossary Definition: Information that is collected and analyzed from a study participant.
Use in Context: A study often includes assessments, like surveys or medical tests.
More Info: An assessment is used in research to collect data that helps the researchers answer the study questions.
Other Info to Think About When Joining a Study: You may hear the word \"assessment\" when the study team tells you about the study procedures that will be done during the study. An assessment could be a questionnaire you do on your own or a procedure that is done by someone on the study team (like getting a blood pressure reading).\n\nAsk if you are not sure what the study assessment is for and what data are being collected. If the assessment involves any medical tests, you could ask if you need to do anything special to prepare.
Related Terms: test, questionnaire, survey, baseline assessment", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "baseline assessment", "definition": "Glossary Definition: Information that is collected and analyzed from a study participant at the start of a study.
Use in Context: A baseline assessment collects data about the participant’s health status at the start of a study before any study treatment is given.
More Info: A baseline assessment is used to compare how the participant's health status changes during the study. \n \nFor example, if a study is measuring weight loss, the participant’s weight must be taken at the start of the study to see if the participant loses weight while they are in the study.\n \n A baseline assessment could include questionnaires, lab tests, or other medical information for the study.
Other Info to Think About When Joining a Study: You may hear thie term \"baseline assessment\" when the study team tells you about what you need to do for the research study. They may ask you to complete a \"baseline assessment\" to collect information at the start of the study. Some baseline assessments are done by the participant (like a survey) while others might be done with a person from the study team (like a blood draw).\n\nIf you do not understand the baseline assessment at the start of the study, please ask the study team to clarify.
Related Terms: assessment, questionnaire, survey", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "basket trial", "definition": "Glossary Definition: A research study that tests one study treatment for different diseases and conditions.
Use in Context: A basket trial is done to see whether a study treatment can work for multiple different conditions that have something in common.
More Info: A basket trial is a type of platform or master protocol study.\n\nA basket trial is done to find out whether one drug can treat multiple diseases.\n\nA basket trial enrolls patients with different diseases that have something in common.
Other Info to Think About When Joining a Study: You may hear about basket trials when you are learning about different types of study designs.\n\nIf you are thinking of joining a \"basket trial\" it means the study will be trying to find out if one treatment could help with a few different diseases or conditions. \n\n\nIf you are unclear what it means for a research study to be a basket trial, you should ask a member of the study team to clarify any of your questions.", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "benefits of a research study", "definition": "Glossary Definition: The ways a research study might help the participant and others.
Use in Context: Learning about the possible benefits of a research study can help someone decide whether or not to enroll.
More Info: Research studies may have benefits for individuals and/or society. For example, a personal benefit of being in a research study might be regular health checks. A benefit to society may be helping future patients or the public, even if an individual participant does not directly benefit. Participants may not have any benefits from being in a research study.
Other Info to Think About When Joining a Study: The consent form will include information about whether or not there will be direct benefits to you if you participate in a research study. \n\nYou should ask about more details of the benefits of the research, if there are any. You can also ask about the risks of participating in the study.
Related Terms: benefits of a clinical trial, benefits, advantages, pros", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "bias (research)", "definition": "Glossary Definition: Flaws in the way a study is designed, done, or analyzed that lead to one conclusion being favored over another.
Use in Context: Research bias can affect the results and outcomes of the research.
More Info: Bias in research can occur either on purpose or accidentally. Bias may cause false conclusions or misleading results. \n\nAll research study staff should be aware of, and reduce, potential sources of bias.\n\nResearch bias can occur when a study is being planned, conducted, or analyzed. Bias can happen based on data selection, study methods, individual experiences, or personal opinions. \n\nStatistical or personal factors and can cause results or findings to lean one way over another. For example, if a researcher only recruits participants who speak English, people who speak another language would not be represented.
Other Info to Think About When Joining a Study: The concept of \"research bias\" may come up when reading results and trying to interpret the way the study was conducted. \n\nWhen you think about a study's results, you may have questions about whether there was any potential bias in the study, and how the researchers tried to avoid bias. \n\nBias is also something that you can discuss with the research team if you are considering being in a study or are currently a participant in a study.
Related Terms: prejudice, tendency, preference, predisposition, favoritism", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "biomarker", "definition": "Glossary Definition: Something in the body that is measured as an indicator of personal health or disease.
Use in Context: Many different types of biomarkers can be measured in the body.
More Info: Biomarkers can be found in blood, body fluids, or tissues. They are sometimes related to a particular disease or condition.\n\nA biomarker can show how the body is working, and provide information about health. \n\nUnderstanding biomarkers is important for developing new drugs and medical devices. Biomarkers are one way to figure out whether the drug or device is working as intended.\n\nFor example, one biomarker is cholesterol. Cholesterol levels are a useful biomarker for heart disease. A research study might try to find out if a medication is helping lower cholesterol to prevent heart disease.
Other Info to Think About When Joining a Study: A study that collects samples like blood or saliva from your body might be looking biomarkers. \n\nYou can ask the study team any questions you have about the kinds of biomarkers that might be studied and whether any of the results will be returned to you.
Related Terms: biological marker", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "birth control", "definition": "Glossary Definition: A way to prevent pregnancy
Use in Context: Using birth control may be required in some drug studies.
More Info: Birth control when any method, medicine, device, procedure, or behavior is used to prevent pregnancy.\n\nContraception is a form of birth control.\n\nSometimes birth control is needed in clinical trials if there is a possible risk to sperm or pregnancy development.
Other Info to Think About When Joining a Study: A study you are thinking about joining may say that you need to use a birth control method. Some studies require a sexual partner to also use birth control. \n\nYou can ask the study team to explain why birth control is needed. You could ask what kind of birth control the study will want you to be on. You could also ask what will happen if you do get pregnant while on the research study.
Related Terms: contraception, pregnancy prevention, condoms, birth control pill, abstinence, hysterectomy, vasectomy, intrauterine device (IUD)", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "blood draw", "definition": "Glossary Definition: Taking a sample of blood by using a needle.
Use in Context: A blood draw from a vein is often needed for lab tests.
More Info: If a study includes a blood draw, it means one (or more) samples of the participant’s blood will be taken for the research.
Other Info to Think About When Joining a Study: Some research studies require one or more blood draws. The blood draw could happen at any study visit depending on the study you join. \n\nYou can clarify if and when you need a blood draw during the study. You may also want to ask if you need to do anything to prepare, like skip a meal if it is a fasting blood draw.
Related Terms: phlebotomy, blood sample", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "clinical benefit", "definition": "Glossary Definition: A health change that researchers measure to show that the study treatment helps the study participants.
Use in Context: A study treatment may have clinical benefit if participants have some kind of improvement.
More Info: For example, the clinical benefit of a drug used in a diabetes study might be lowering and better controlling the blood sugar of participants.
Other Info to Think About When Joining a Study: You may see the term \"clinical benefit\" when researchers describe what the study is trying to find out. In many cases what matters most to participants is understanding whether a study treatment will lead to having a clinical benefit like improved quality of life. You might want to ask if the study is measuring the clinical benefit of the study treatment.
Related Terms: benefits of a research study, benefits, advantages, pros", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "clinical research", "definition": "Glossary Definition: A type of science that uses people's data to study health, illness, behaviors, or conditions in careful and defined ways.
Use in Context: Clinical research is an organized way to find out about a condition or disease, how the condition progresses, how a treatment is given, or which treatments are safe and work best.
More Info: Clinical research includes many different types of studies, such as clinical trials, observational studies, and survey studies. Clinical research can be about individuals, populations, or public health.
Other Info to Think About When Joining a Study: The term \"clinical research\" refers to any systematic way of studying health and illness. Clinical research is the way to learn more and find new medicines and treatments.\n\nYou may hear this term if a researcher asks you to take part in a clinical research study. For example, your doctor may ask if you want to be involved in research and consent to join a study.
Related Terms: health research, medical research, clinical trial, clinical study, research study, observational study, preclinical study", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "Clinical Research Coordinator (CRC)", "definition": "Glossary Definition: A research staff member who helps manage studies.
Use in Context: A Clinical Research Coordinator works with the study doctor to help conduct the study.
More Info: One or more CRCs is assigned by the Principal Investigator who is leading the research to take care of specific study tasks. Tasks include preparing study documents, scheduling study visits, and collecting data.
Other Info to Think About When Joining a Study: When enrolling in the study and going to study visits, you may meet with the clinical research coordinator. They may be the person explaining the study and consent process to you.\n\nIt may be useful to ask whether the clinical research coordinator is the person to contact if you have any questions about the study or any issues come up.
Related Terms: project manager, study coordinator, research coordinator, research nurse", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "clinical trial", "definition": "Glossary Definition: A research study that tests drugs, devices and treatments to see if they are safe and work in people.
Use in Context: Participants in a clinical trial help the study doctor learn more about a new treatment.
More Info: A clinical trial could study ways to diagnose, treat, or even prevent illness. Some clinical trials look at just one study treatment. Others might compare the study treatment to another treatment, a placebo, or even to a group that is taking nothing in order to measure how well the study treatment works.
Other Info to Think About When Joining a Study: The term \"clinical trial\" is used for studies of people with various diseases and conditions. \n\nYou might be asked if you want to take part in a clinical trial. You may also read it on flyers asking for study volunteers. \n\nIt is important to understand the risks and benefits of the clinical trial before you enroll.
Related Terms: research study, trial, clinical study, study, interventional study, clinical research study, clinical research, control, placebo", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "clinician", "definition": "Glossary Definition: A health care provider.
Use in Context: A clinician has special medical training to care for patients.
More Info: Clinicians include people who are doctors, medics, nurses, pharmacists, psychologists, physical therapists, occupational therapists, psychiatrists, and others.
Other Info to Think About When Joining a Study: Any health care provider that you see regularly is a clinician. In health-related research studies, it may be a clinician who recruits and enrolls you. Additionally, a clinician may be in charge of a research study you join. \n\nYou can ask for the name and contact information of the clinician(s) running the research study. You may also want to discuss being in a research study with your own clinician before consenting to join a study.
Related Terms: allied health professional, healthcare provider", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "cohort", "definition": "Glossary Definition: A group of study participants that are similar in some way.
Use in Context: Data were collected from a cohort of people over the age of 65 to see if the participants developed health problems.
More Info: A cohort is usually a group of people who are in an observational study to see how a disease or condition develops. A cohort is also the group of people in a clinical trial testing a study treatment for a specific disease or condition.
Other Info to Think About When Joining a Study: You may hear the term \"cohort\" when the study team is describing the research study to you or when you are reading the study consent form. This term may come up when learning about the study groups and the different study treatments they may take or what different groups of participants may have to do differently.\n\nIt can be helpful to ask why a specific cohort was selected for the study you are joining and what the researchers would like to learn.
Related Terms: study cohort, study group, arm, observational study", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "Comparative Effectiveness Research (CER)", "definition": "Glossary Definition: A study comparing two or more treatments.
Use in Context: A comparative effectiveness research study compares at least two treatments to determine differences in outcomes.
More Info: Comparative effectiveness research compares treatments with drugs or devices, different ways to diagnose a condition, or how best to provide health care and services.\n\nAn example of a comparative effectiveness research study would be comparing Advil, Aleve, Tylenol, and Aspirin to see which is better for treating headaches. Generally, a placebo is not used in comparative effectiveness research.
Other Info to Think About When Joining a Study: If you are thinking about joining a comparative effectiveness research study, you should understand what study treatments are being compared and why.
Related Terms: superiority trial, inferiority trial", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "comparator", "definition": "Glossary Definition: Something that is compared to the study treatment.
Use in Context: Studies with a comparator are usually trying to find out if there is a different outcome in the comparator group versus the group that gets the study treatment.
More Info: A comparator is also called a \"control.\" The group that gets the comparator is the \"control group.\"\n\nThe comparator can be an investigational or approved medicine, a placebo, or a different procedure or intervention.\n\nFor example, in a study of acupuncture for back pain, a comparator might be physical therapy or yoga.
Other Info to Think About When Joining a Study: You may see the word \"comparator\" to describe another arm in the study. A comparator is often a treatment or intervention in common use and is used to see if the study treatment is worse, better, or somehow different. Feel free to ask any questions you might have about the comparator that is being used in a study.
Related Terms: Control, Reference, Benchmark, Comparison, Study treatment, Test article", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "compensation (study)", "definition": "Glossary Definition: Money and other forms of payment that may be given to participants for completing study activities.
Use in Context: Participants can ask the study team if compensation will be offered.
More Info: Some studies offer payment to participants. Study team members should explain any likely costs to participants for being in the research, and whether compensation or payment will be offered. This information may also be in the research consent form. \n\nCompensation in research is meant to help cover out-of-pocket costs to study participants, such as transportation, parking, meals, childcare, and to offset time out of work. If the study is unable to provide compensation, study team members should explain why.
Other Info to Think About When Joining a Study: As a participant, you may hear or read about \"compensation\" when thinking about joining a study. This information about payments might be in the consent form, and a study team member may discuss this with you during the consent conversation. \n\nIf compensation is not mentioned, you should feel free to ask the study team if compensation or payment is provided. You may also ask how much compensation is provided, how the amount is determined, and what you need to do to ensure you can receive the compensation.
Related Terms: Reimbursement, Payment, Incentives", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "compliance", "definition": "Glossary Definition: Following research requirements.
Use in Context: Compliance with research rules and instructions improves the quality of a study.
More Info: Compliance in research refers to following regulations and guidelines about research. Researchers must be in compliance with research requirements and follow the approved protocol.\n\nCompliance also applies to participants who should follow study procedures.\n\nThe words 'adherence' and 'compliance' are often used in the same way.
Other Info to Think About When Joining a Study: The consent form may say that the study team could remove you from the study if they notice your participation does not meet compliance requirements. Feel free to ask the study team for assistance is you are struggling with compliance due to issues such as lack of transportation, a reading disability, or just using new technology as part of the study. Keep the study team updated so they are aware of your situation. \n\nBoth the researchers and participants need to be compliant with guidelines and protocols when they are taking part in the research. The study team will tell you that there are certain things you must do to be part of the research study. Following these rules means you are compliant with the study procedure. \n\nIf you are unsure of what you should be doing during the study, ask the study team for more information.
Related Terms: adherence, following the rules or the law", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "Computerized Tomography (CT) scan", "definition": "Glossary Definition: A way to take pictures of the inside of a person's body using a type of radiation and a computer.
Use in Context: A CT scan is able to show specific changes in a person's body (like changes in the brain, other tissues, organs or bones).
More Info: A CT scan is a type of imaging study.
Other Info to Think About When Joining a Study: A CT scan may be needed in a research study. \n \nIf you know you have to get a CT scan for the research study, you can ask how the information will help the study. Also, you can ask if the CT scan results will be shared with you or your regular doctor, and how much radiation you will receive. This information could help you decide if you want to receive radiation from a CT scan done for research purposes.
Related Terms: imaging study, X-ray, MRI", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "concomitant medications", "definition": "Glossary Definition: Non-study medicines that are allowed to be taken at the same time as the study treatment.
Use in Context: Researchers need to know about any concomitant medications a participant is taking while in a research study.
More Info: In research, concomitant medications are medicines that a participant takes at the same time as taking the study treatment. \n\nConcomitant medications are not the study treatment. \n\nThe study team needs to know about all the medicines that are being taken to make sure that they do not interfere with the research. \n\nFor example, if a participant takes a blood pressure medication, they should tell the study team and ask it is ok to keep taking it in the research study.
Other Info to Think About When Joining a Study: Concomitant medications are reviewed by the study team to ensure that they are safe to take during the study \n\nIt is important to tell the study team about your other medications to ensure it is safe to take them while on the study. If you join a research study and need to start a new medication, contact the study team first. If any new health issues arise, be sure to discuss them with the study team.
Related Terms: usual medications, con-meds", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "conduct", "definition": "Glossary Definition: To do a study or procedure.
Use in Context: The study team helps conduct the research.
More Info: Depending on the study, the researcher may conduct a physical exam, survey, or interview to collect data for the research.
Other Info to Think About When Joining a Study: The term \"conduct\" may come up in the consent form you are given when you're thinking about joining a study. It may mention that the study team is conducting this research or that your participation may help them conduct the research.
Related Terms: perform, run, execute, implement, carry out", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "confidence interval", "definition": "Glossary Definition: The defined range of numbers used to describe where the results are expected to fall.
Use in Context: A confidence interval is the range of values that a result is expected to fall in if the test is done again.
More Info: A confidence interval is a measure of variability. It is the likelihood that a measurement, when repeated, will fall within a given range. It can help researchers know how much to trust that a result can be repeated. \n\nThe smaller the confidence interval, the more certain the results are.\n \nThe term \"confidence interval\" is often abbreviated as \"CI.\"
Other Info to Think About When Joining a Study: The term \"confidence interval\" will usually appear in publications about research studies when the article discusses the statistics and results.\n\nThe results section may provide more description about the confidence interval as well as define what it is for the specific study written about in the publication.\n\nThe confidence interval could also be included in the Plain Language Summary explaining the study results.
Related Terms: margin of error, probability, estimate", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "confidentiality", "definition": "Glossary Definition: Protecting personal information from people who should not have access.
Use in Context: Confidentiality in research means researchers keep participant information private.
More Info: Researchers protect confidentiality by not sharing personal details about study participants with people who do not need to know as part of their work on the research.
Other Info to Think About When Joining a Study: Confidentiality is very important in healthcare and clinical research. When talking or reading about the data that you will provide during the study, the study team may mention how they will protect your confidentiality. \n\nYou can always ask more about how the study team plans to protect your data and who will have access to it. Before you join a study, make sure you feel comfortable with how your data will be used and protected.
Related Terms: privacy, non-disclosure, data", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "confounding", "definition": "Glossary Definition: When the study outcome is influenced by outside conditions that were not expected by the study researchers.
Use in Context: Researchers try to be aware of possible confounding factors that can affect their study results.
More Info: Confounding suggests there is a correlation between two or more things when really there is none. \n\nFor example, alcohol use is associated with lung cancer. Alcohol is not known to be related to lung cancer, but people who smoke often consume alcohol. Smoking is the confounder here, related to lung cancer and associated with alcohol use.
Other Info to Think About When Joining a Study: Researchers design studies to try to avoid the amount of confounding that could impact the study results. If you are thinking about enrolling in a new study, you might ask the study team how the study was designed to try to prevent confounding.\n\nPregnancy is often considered a confounding factor for research. Some studies are designed to not allow pregnant people to participate because they aren't sure how the pregnancy would impact the results. If you are planning to become pregnant and are thinking about joining a study that does not allow pregnancy you should discuss this with the study team.
Related Terms: correlation; causation; cause and effect; dependent and independent variables", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "consent form", "definition": "Glossary Definition: A document used to explain the planned research before a person decides whether or not to join a study.
Use in Context: A person signs the consent form when they choose to take part in a study but only after they understand the information.
More Info: A consent form for a research study explains the research, potential risks and benefits, and all the details of a study. The consent form also includes information about other treatment options, the rights of participants, and the rules that the researchers need to follow. \n \n A consent form can be on paper or an electronic document.
Other Info to Think About When Joining a Study: Most clinical research studies require a person to read and sign a consent form. A member of the study team should explain the study in detail and answer any questions you have. \n\nYou can ask for and keep a copy of the consent form from the study. Take the time to get your questions answered if anything is unclear so you understand what you will have to do during the study and how long the study is. Even after signing a consent form to join a study, remember you can withdraw from the study too. Just make sure to discuss with the study team so you can leave the study safely.
Related Terms: informed consent form, informed consent", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "Contract Research Organization (CRO)", "definition": "Glossary Definition: A group that is paid by the study sponsor to support research studies.
Use in Context: A Contract Research Organization helps the sponsor run a study.
More Info: A Contract Research Organization (CRO) can be a commercial, academic, or other group that is contracted by the sponsor to perform one or more research functions. \n\nA CRO works with the study teams and often helps coordinate multiple sites.
Other Info to Think About When Joining a Study: When reviewing the consent form, it may say that there is a Contract Research Organization (CRO) involved. \n\nYou could ask for more details about what the CRO is doing and what part they play in the study. \n\nYou can also ask whether any participant information is shared with the CRO.
Related Terms: sponsor, clinical research coordinator", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "contraindicated", "definition": "Glossary Definition: When things should not be used or done together because of possible harm.
Use in Context: Researchers make sure that study procedures are not contraindicated before a participant enrolls in a study.
More Info: Researchers carefully check whether participants are receiving treatment or have a condition that would be contraindicated for the study intervention.\n\nFor example, a study of high-protein diets is contraindicated in people with kidney failure because high protein might harm the participant's kidneys.\n\nAn action or procedure could also be contraindicated in certain situations. For example, an MRI is contraindicated in someone who has anything metal in their body.
Other Info to Think About When Joining a Study: The study team may tell you there are certain medications you cannot take or foods you cannot eat while you are participating in the study because they are contraindicated. For your safety you may not be allowed to join a study because you have a condition that may be contraindicated. \n\nBe sure to ask for clarification if you are unsure about what may be contraindicated. See if there is a list that you can keep that reminds you of what is contraindicated
Related Terms: harmful", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "control group", "definition": "Glossary Definition: The people in a study who do not receive the study treatment or do not have the condition being studied.
Use in Context: A control group is used as a comparison to see the effect of the study treatment.
More Info: Participants in the control group receive something different during the research study compared to the intervention group. The control group might receive a different dose, standard of care, a placebo or no treatment at all.
Other Info to Think About When Joining a Study: Whether a research study has a control group is important to understand. You could ask if the study you are thinking about joining will have a control group. If there is, you could ask what being in the control group will involve. For example, being in the control group could mean getting a placebo or the standard of care for the disease the study is looking at. You can also ask how participants will be assigned to the control group. This is often decided randomly, using randomization.
Related Terms: control arm, comparison group", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "correlation", "definition": "Glossary Definition: When two or more measures are linked.
Use in Context: There is a correlation between height and weight in that taller people tend to be heavier.
More Info: A correlation means that two things are associated. It does not mean that the one of the two things is caused by the other.\n\nA strong correlation means that two things are highly related\n\nA weak correlation means that two things are not very related.\n\nAn inverse correlation means that as one thing increases, the other decreases.
Other Info to Think About When Joining a Study: The term \"correlation\" may be used to discuss how certain conditions or situations relate to each other. You may also hear about correlations in the context of how study results are presented and discussed. For example, a study may try to find out whether the study intervention is correlated with some specific outcomes.
Related Terms: relationship, association", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "data", "definition": "Glossary Definition: Information collected from or about people taking part in a research study.
Use in Context: Researchers use data to answer study questions.
More Info: There are many different types of data including:\n personal information like age and date of birth, questionnaires, blood test results, imaging scans and their interpretations, health insurance status and so on. \n \n The types of data collected depend on the study.
Other Info to Think About When Joining a Study: Analyzing data is the way research questions are answered. You will usually hear the term \"data\" when researchers talk about the information they will be collecting about you during the study\n\nYou may want to clarify what data the study team will collect from you and how the data will be used for the research. You can also ask how the data will be protected and whether the data could be used for any other future uses.
Related Terms: information, questionnaire, assessment", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "Data Monitoring Committee/Data and Safety Monitoring Board (DMC/DSMB)", "definition": "Glossary Definition: An independent group of experts that reviews study data to make sure that patient safety is protected.
Use in Context: A Data Monitoring Committee advises the study sponsor if any concerns about participant safety are found.
More Info: Data Monitoring Committees (DMCs) are also called Data and Safety Monitoring Committees (DSMC) or Data and Safety Monitoring Boards (DSMB). \n\nStudies that are randomized controlled trials, higher risk, or enrolling vulnerable populations (eg. children) usually include DMCs to review the data at specific timepoints, especially for adverse events that can affect participant safety.\n\nThe DMC reviews unblinded data on a regular schedule and as needed until the end of the study and advises on next steps in the event of adverse event(s).\n\nThe DMC also looks at whether a study should be stopped early (for example, for safety reasons or if there is no benefit to the study treatment)
Other Info to Think About When Joining a Study: You may see or hear about a Data Monitoring Committee (DMC) if you are enrolled in a study that has one. The DMC looks at all study data and may request that the study team give you important new information that is learned. If there are any safety concerns, the DMC may advise that the study should be ended early.
Related Terms: DSMB, DSMC", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "database (research)", "definition": "Glossary Definition: Information that has been collected and organized to be used for research.
Use in Context: A research database stores study information so that researchers can use it to answer study questions
More Info: Participants are usually given a code during the study so their identifiers are not saved in the same place as the other study data. A research database usually has coded information about the participants to protect their privacy. A database can be used in the present or in the future to answer new questions.
Other Info to Think About When Joining a Study: You will hear the term \"database\" used in many different ways during the research process. \n \nIn research studies you may hear or read about the term \"database\" when the study team or consent form is describing how they will store data that is collected from the study. A database may also store personal information, like participant contact information. This information should be stored in a secure, protected way.\n \nYou may want to ask how your data will be stored if you join the study. You can also ask the study team who has access to the database and what information they will keep in the database. Additionally, you may want to ask how they will keep the database safe and secure.
Related Terms: data bank, registry, repository", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "discontinue (participant)", "definition": "Glossary Definition: To remove a study participant from a study.
Use in Context: If a participant wants to stop being in a study, they should have a conversation with the study team about how to discontinue safely.
More Info: A participant can decide to discontinue being in a study. Sometimes a participant is discontinued by the researchers for safety or other reasons. Reasons for discontinuing and the transition from the study should be discussed with the participant before they leave the study.
Other Info to Think About When Joining a Study: The term \"discontinue\" can be used in research to describe either leaving the study or stopping the study treatment. If you decide to discontinue any part of being in a study, please discuss with the study team how to do so safely first. This is to make sure that there are no likely health risks from ending study participation.
Related Terms: withdraw", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "discontinue (study treatment)", "definition": "Glossary Definition: To stop a study treatment in a participant.
Use in Context: If a participant wants to stop the study treatment, they should first have a conversation with the study team about how to discontinue safely.
More Info: A participant can decide to discontinue a study treatment. Sometimes a study treatment can be discontinued by the researchers for safety or other reasons. Reasons for discontinuing should be discussed before the study treatment is stopped.
Other Info to Think About When Joining a Study: The term \"discontinue\" can be used in research to describe leaving the study or stopping the study treatment. If you decide to stop taking the study treatment, please discuss this with the study team first. This is to make sure that there are no likely health risks from stopping the study treatment. Please also discuss how stopping the study treatment affects your participation in the study itself.
Related Terms: withdraw", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "disease progression", "definition": "Glossary Definition: An illness getting worse over time.
Use in Context: Disease progression refers to a disease or condition getting worse for a patient.
More Info: Disease progression can refer to a disease or symptoms getting worse. It can also refer to a person's functional abilities declining.
Other Info to Think About When Joining a Study: Depending on the kind of study you are considering or reading about, you may see or hear references to \"disease progression\" during the informed consent process or other informational study materials. Disease progression may be used to explain the purpose of a study or explain the reason for particular procedures and tests. For example, a study could be collecting data on whether the disease progresses after taking a treatment. \n\nIf you have any questions about what it means for a study to look at disease progression, you should ask the study team.", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "disease-free survival", "definition": "Glossary Definition: The length of time after treatment that a person lives without the illness coming back.
Use in Context: Some studies look at disease-free survival to see whether the drug works to keep a disease from coming back.
More Info: Disease-free survival can be used to describe the length of time an individual or a group of participants within a study are free of their disease.\n\nNot every person in a study will necessarily have the same response to the study treatment.
Other Info to Think About When Joining a Study: Depending on the kind of study you are considering or reading about, you may see or hear references to \"disease-free survival\" during the informed consent process or other informational study materials. \n\n\"Disease-free survival\" may be used to explain the purpose of a study or explain the reason for particular procedures and tests. For example, a study could be collecting data on disease-free survival after participants take a certain treatment. \n\nIf you have any questions about what it means for a study to look at disease-free survival, you should ask the study team.
Related Terms: relapse-free survival, remission", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "dissent", "definition": "Glossary Definition: Refusing to be part of a research study.
Use in Context: A child can dissent or refuse to participate even if their parent or guardian has consented for them to participate in a study.
More Info: Children and adults who are unable to give legal consent may dissent to participate at any point during the research. Researchers should honor dissent whether it is communicated in a verbal or non-verbal way. \n\nA participant can dissent and withdraw from most research at any time. Sometimes, when the research is likely to provide benefit to the individual receiving the intervention, dissent may not be honored, but every effort should be made to explain the reason to the participant.
Other Info to Think About When Joining a Study: The term \"dissent\" may be discussed when a parent or guardian reviews the consent form. You should know that even if a guardian provides consent, they have to ask the person who will be in the study if they want to be in the study or not. The person will either want to be in the study and assent or they may not want to be and dissent. \n\nYou can ask if there is an assent process and what that process will be like. You may want to also ask what happens if the guardian gives consent but the potential participant dissents.
Related Terms: object, say no, refuse, decline, disagree", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "dose escalation study", "definition": "Glossary Definition: A kind of study where increasing amounts of a study treatment are given to different groups to find the best dose.
Use in Context: In a dose-escalation study, the dose of a study treatment is increased one group at a time to find the best dose.
More Info: In a dose-escalation study, the first participant (or group of participants) gets the lowest dose of study medication. \n\nThe amount is increased with each participant or group to find the dose that gives the greatest benefit with the fewest side effects. \n\nThis process can help researchers choose the best dose for future studies.
Other Info to Think About When Joining a Study: If you are considering participating in a dose escalation study, you may have questions about how the dose levels were determined and what safety information already exists.
Related Terms: Therapeutic Index", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "double-blind study", "definition": "Glossary Definition: A study that is set up so that the study treatment that each participant receives is not known by the participants or the researchers
Use in Context: In a double-blind study, the study participants and the study doctor don't know which treatment each participant is getting.
More Info: Double blind studies are done to minimize bias that can affect the study results.\n\nBias can occur when participants or researchers know which study treatment participants are getting\n \nParticipants can ask to find out which study treatment they received after the study ends.
Other Info to Think About When Joining a Study: If you are considering joining a double-blind study you can ask questions about how your safety will be monitored. If there is an emergency, it is possible to find out what study treatment you are taking so you can receive the medical care you need.
Related Terms: masked study, blinded study, masking, blinding, bias, single-blind study, randomization", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "drug holiday", "definition": "Glossary Definition: A time period decided between the participant and study team when a medication is stopped and then re-started.
Use in Context: A person should always check with the study team and their doctor before stopping any medicine or taking a drug holiday.
More Info: A drug holiday is a planned break from taking a medicine or study treatment. \n\nPeople may take a drug holiday for study reasons, a health issue, or personal reasons. For example, having surgery may mean that all current medications, including any study treatments, need to be paused.\n\nA person should not stop taking any prescription or study medications without first discussing with their doctor and the study team first.
Other Info to Think About When Joining a Study: If you are in a research study and would like to take a planned \"drug holiday\" to stop taking the study treatment you should first discuss it with the study team.\n\nAny time you plan to pause any of your prescribed medications you should ensure this is safe with your regular doctor and discuss how to stop taking your medications safely.\n\nFeel free to ask any questions you have about taking a drug holiday.
Related Terms: Medication vacation", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "drug therapy", "definition": "Glossary Definition: The use of medicine to treat a disease, condition, or symptom.
Use in Context: Clinical trials are done to learn more about the risks and benefits of a drug therapy.
More Info: There are many different types of drug therapy that can have multiples different purposes. \n\nA drug therapy could be used to:\n- make symptoms better, \n- treat a condition or disease, \n- lower the risk of getting a disease in the future, \n- or destroy harmful cells, like cancer cells.
Other Info to Think About When Joining a Study: If a research study is testing a drug therapy it is important to understand the possible risks and benefits.\n\nYou can ask questions during the consent process, but also anytime during the study if any new questions come up. The study team is there to make sure you stay safe while you are taking a drug therapy.
Related Terms: Pharmacotherapy, Pharmaceutical therapy, Biologic, drug", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "e-consent (form)", "definition": "Glossary Definition: An electronic version of an informed consent form.
Use in Context: An e-consent form can be useful in studies that are being conducted remotely.
More Info: E-consent is a method of obtaining informed consent through the use of an electronic system instead of a paper consent form.\n\nParticipants may sign the form electronically and then they may be able to get a copy emailed to them or download it themselves.
Other Info to Think About When Joining a Study: A study may have an e-consent form to join a study. \n\nYou could ask if you get a copy of the e-consent form and how you will get that copy. You could also ask if there is a paper version of the consent form for you if you want one.
Related Terms: electronic informed consent, digital consent, consent form, decentralized clinical trials", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "effectiveness", "definition": "Glossary Definition: How well a treatment works.
Use in Context: Effectiveness refers to how well a treatment, medicine or vaccine works in people who use it after it has been approved.
More Info: Effectiveness refers to how well a treatment works in the real world, not in a planned clinical trial. Usually effectiveness is assessed after approval by health or government authorities.
Other Info to Think About When Joining a Study: The word effectiveness commonly comes up in the context of already approved medications. In contrast, efficacy, refers to how well a study treatment works in a study. The words \"effectiveness\" and \"efficacy\" are sometimes used to mean the same thing even though they have slightly different meanings.
Related Terms: Efficacy", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "efficacy", "definition": "Glossary Definition: How well a study treatment works in the study.
Use in Context: A study tests efficacy to see how well a new treatment works.
More Info: Efficacy is different from \"effectiveness\" which refers to how well the treatment works in the real world outside a study.
Other Info to Think About When Joining a Study: The word \"efficacy\" can be used to describe how well the investigational intervention works in the controlled setting of a study. This term may be used when researchers describe an objective in the study. For example, a study could be testing the efficacy of a new vaccine.
Related Terms: effectiveness", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "eligibility criteria", "definition": "Glossary Definition: The reasons a person can be included in, or excluded from, a study.
Use in Context: A study has eligibility criteria to make sure only people who fit the study requirements join as participants.
More Info: The eligibility criteria for a study are made up of inclusion criteria and exclusion criteria. For example, a study may be looking to include only people of a certain age or with a certain health condition.
Other Info to Think About When Joining a Study: If you are interested in joining a clinical trial, the study team will ask you some questions and possibly do some medical tests to make sure you meet the eligibility criteria. This is for your safety and for scientific reasons so participants all meet specific criteria.\n\nIf you do not meet the eligibility criteria for one study, you can ask the study team if there are other studies that you could be a good fit for.
Related Terms: inclusion criteria, exclusion criteria", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "Emergency Use Authorization (EUA)", "definition": "Glossary Definition: A process to make a treatment or vaccine available during a public health emergency, before all research is complete, and before full approval is granted.
Use in Context: Regulators decide whether to allow an Emergency Use Authorization.
More Info: An Emergency Use Authorization (EUA) makes medical treatments and vaccines available when the public's health is at risk, such as during a pandemic.\n\nUnder an EUA, medicines and vaccines are still being tested but the approval process is fast-tracked in order to make interventions available more quickly.\n\nAn EUA applies to the USA only. Other countries have different rules and regulations.
Other Info to Think About When Joining a Study: The term \"Emergency Use Authorization\" may be something you heard during the Covid pandemic. If a treatment or vaccine is made available under an EUA, you may hear that there are still research studies to collect more data about the product.", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "endpoint", "definition": "Glossary Definition: A measure of the expected effect of the study treatment.
Use in Context: An endpoint is one of the main questions the study is trying to answer.
More Info: A study could have more than one endpoint. Some examples of endpoints that are used in studies include finding out more about about a disease or condition, participant quality of life, or symptoms.
Other Info to Think About When Joining a Study: The term \"endpoint\" is used to describe what the study will be looking at to see if the study treatment had an effect. You could see this term in consent documents or other descriptions of the study, including study results reports. \n\nIn general, the endpoint is reported as an average of all the data collected in the study. You may not have the same results as the overall study found. For example, a study treatment may have lowered blood pressure overall for study participants, but your blood pressure may not have changed while taking the study treatment.\n\nIf you have any questions about the study endpoint, you can ask the study team.
Related Terms: clinical endpoint, outcome, primary endpoint, secondary endpoint, surrogate", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "enroll", "definition": "Glossary Definition: The action of a participant joining the study after providing informed consent.
Use in Context: If you enroll in a study, it means you decided to volunteer to be a participant.
More Info: In order to enroll in a study, a person must meet the eligibility criteria. \n \nStudy participation is your choice. It is voluntary. \n \nIf the study includes randomization, this would be occur after the participant enrolls.
Other Info to Think About When Joining a Study: You may hear the term \"enroll\" when the study team is asking if you want to join the research study. You may need to provide your informed consent by signing a consent form before you can enroll in the study.\n\nDo your best to understand everything you will be asked to do if you enroll in a study. Feel free to ask the study team any questions you have about the study. In many cases, you will be able to take the time to go home and think about it before deciding to enroll in a study.
Related Terms: join, informed consent, consent form, eligibility criteria, inclusion criteria, exclusion criteria, randomization", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "epidemiologist", "definition": "Glossary Definition: A person who studies where, why, how often, and to what populations health concerns and diseases happen.
Use in Context: An epidemiologist analyzes data to look for patterns and causes of diseases or other health conditions in large groups of people.
More Info: Some studies include epidemiologists because they help search for the cause of a disease and identify who might be at risk. \n\nEpidemiologists also help figure out how to control or stop the spread of a disease.
Other Info to Think About When Joining a Study: An epidemiologist tends to look at population health rather than individual health. If you learn that an epidpemiologist is a member of the study team, this means there is someone looking at how the disease or condition being studied affects large groups of people.
Related Terms: Disease detective", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "equivalence", "definition": "Glossary Definition: When two or more things in a study are about the same.
Use in Context: In clinical research, equivalence often refers to whether two study treatments are almost the same.
More Info: Treatments, therapies, vaccines, evaluation methods and study groups do not have to be exactly equal in order to have equivalence. \n\nInstead, equivalence means that the treatments are about the same in terms of how they work for patients.
Other Info to Think About When Joining a Study: You may see the word \"equivalence\" when a study is trying to see if two or more study treatments have the same effect, or if a study has shown there is equivalence between study treatments.\n\nYou may want to ask about whether there are any known possible differences between study treatments that would be important for you to understand given your own personal health concerns and issues.
Related Terms: similarity, resemblance, equivalence study, non-inferiority study, equal", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "equivalent (effect)", "definition": "Glossary Definition: The same or almost the same result.
Use in Context: In research, an equivalent effect means that different study treatments or medication doses have about the same effect on patients.
More Info: An equivalent effect doesn't mean two treatments are exactly equal. Two treatments are equivalent if they have about the same risks and benefits.
Other Info to Think About When Joining a Study: The word \"equivalent effect\" could be used to explain that one study treatment has a similar effect as another one.\n\nYou may want to ask about whether there are any known possible differences between study treatments that would be important for you to understand given your own personal health concerns and issues.
Related Terms: same, similar, equal outcome", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "evaluate", "definition": "Glossary Definition: To examine, review, and understand.
Use in Context: A study team member will evaluate the effect of the study treatment at participant study visits.
More Info: The study doctor evaluates the participant's response and safety in order to decide whether to continue the participation in a research study. The researchers also evaluate the quality of the data from a study to analyze the outcome.
Other Info to Think About When Joining a Study: You may see the term \"evaluate\" if you look up your study on clinicaltrials.gov or in the consent form. You may hear about participants being evaluated during the study based on certain measurements. This term may also come up when talking about evaluating the safety or efficacy of a new therapy or device \n\nIf the consent form talks about evaluating the participant, you may want to ask how they will evaluate you. Additionally, if the researchers are evaluating the efficacy or safety of a study treatment or device, you may want to know more about how they are going to do this and how the data will answer the study questions.
Related Terms: assess, check, check out, learn, judge, appraise", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "exclusion criteria", "definition": "Glossary Definition: A list of reasons a person cannot be included in a study.
Use in Context: If someone wants to join a study, they can not have any of the exclusion criteria.
More Info: Exclusion criteria are reasons that researchers cannot include a person in a research study. For example, if a study is only enrolling adults with diabetes, a person who does not have the condition could not take part.
Other Info to Think About When Joining a Study: You may see the term \"exclusion criteria\" when learning about reasons why you might not be able to be included in a research study. \n\nThe study team will ask you some questions and possibly do some medical tests to confirm you can be included. This is for your safety and for scientific reasons to make sure you do not meet the exclusion criteria.\n\nIf you are unable to join one study, you can ask the study team if there are other studies that could be a better fit for you.
Related Terms: eligibility criteria, inclusion criteria", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "expanded access", "definition": "Glossary Definition: A process for a doctor to request an unapproved treatment for a seriously ill patient.
Use in Context: Doctors can seek approval to use a study treatment outside of research via an Expanded Access application.
More Info: Expanded Access is a path for a patient with an immediately life-threatening or serious disease or condition to access an investigational medical product (like a drug, biologic, or medical device). Expanded Access can make treatment outside of clinical trials possible when no other treatments are available and there is no clinical trial for the patient to join.\n\nExpanded Access is also called \"compassionate use.\" Expanded Access is a program for doctors to request treatments that are not yet approved for seriously ill patients. This request is made by a patient's doctors using the Expanded Access application. The company that makes the experimental drug also has to approve the request.
Other Info to Think About When Joining a Study: The concept of \"Expanded Access\" may be discussed if you are seriously ill or have a life-threatening condition, and the available treatments do not work for you. \n\n\"Expanded Access\" may also be discussed with you if there is no clinical trial for you to enroll in. \n\nYou can always discuss with your doctor if there is a new unapproved treatment that could be requested via expanded access. It is important to note that Expanded Access is for treatment; it is not considered research.
Related Terms: compassionate use, emergency use, pre-approval access", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "experimental", "definition": "Glossary Definition: Something that is being tested in research but not yet proven.
Use in Context: An experimental medicine is studied before it is approved.
More Info: Experimental treatments go through research studies to make sure the risks and benefits to participants are better understood.\n\nBefore a drug, vaccine, or device is approved by regulators for a particular use, disease, or group of patients it is considered to be experimental.
Other Info to Think About When Joining a Study: Clinical research is designed to find out more about health, disease, prevention and treatment. \n\nThe word \"experimental\" may be used to describe what a research study is testing, for example an experimental treatment. This means that the treatment has not yet been proven or approved but there is reason to believe that it might work well. \n\nThrough research, experimental treatments can become approved treatments. If you have any questions about anything experimental in a research study, you should ask the study team.
Related Terms: investigational", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "exploratory research", "definition": "Glossary Definition: A process to find facts that can guide the design of future studies.
Use in Context: Exploratory research is helpful to find out how to approach future research questions.
More Info: Exploratory research clarifies the question to be solved. It does not result in final conclusions or solutions.\n\nExploratory research can test whether a method or study design can be used, or whether an outcome can be measured in a reliable way.\n\nSometimes exploratory research is done using samples stored in biobanks.
Other Info to Think About When Joining a Study: You may see the term \"exploratory research\" to describe early research that was done to figure out processes and inform the next study. \n\nIf you are thinking about joining a research study, you can always ask what exploratory research informed the design of the study, or what exploratory research might be conducted with the data that is collected during the .
Related Terms: pilot, preliminary studies", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "focus group", "definition": "Glossary Definition: A group interview to learn what people think about a topic.
Use in Context: A focus group offers participants a chance to discuss their experiences with each other and the researcher.
More Info: A focus group is a way to get many different people together to hear about what they think about a certain topic. A focus group is usually led by a member of the study team. Since focus groups collect information from a few people at the same time it is important to respect everyone's privacy when participating and not share details about the other people outside the study.
Other Info to Think About When Joining a Study: You may be asked to participate in a \"focus group\" as part of a study. You may ask any questions about the focus group such as what information will be collected, how data will be collected, whether the focus group will be recorded, whether there will be a note taker, and how many people will be in the room.\n\nYou can also ask how the data will be used and/or shared, and how your privacy will be protected. Feel free to ask any questions you have about being in a focus group.
Related Terms: group interview", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "frequency", "definition": "Glossary Definition: How often something happens over a period of time.
Use in Context: The frequency of study visits should be clear to anyone joining a research study.
More Info: Frequency refers to how many times something will happen. It also describes the number of times something occurs in a specific period of time.
Other Info to Think About When Joining a Study: The word \"frequency\" can be used in many different situations. For example, this term could be used to talk about the \"frequency of study visits\" you may have to make if you participate in the study. This term could also be used to talk about how often you have to take the study treatment or how often you need to fill out a questionnaire. If you experience some type of symptom while taking the investigational product, the study team may ask about the frequency of these symptoms.\n\nIf you have any questions about how often you have to do something in order to participate in the study, please ask with the study team.
Related Terms: number, prevalence, recurrence, repeat, incidence", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "generalizability", "definition": "Glossary Definition: How research results can apply to people who were not part of the study.
Use in Context: A study has generalizability if the results are useful and can apply beyond the original study participants.
More Info: Ideally, research findings should have generalizability to people outside of the study.\n\nGood generalizability means research results can be broadly applied to a large number of people who are similar in some way . \n\nPoor generalizability means that the results can only be applied to the study population or very specific situation.
Other Info to Think About When Joining a Study: You may hear the term \"generalizability\" when the study team is describing what they hope the outcome of the research study will be. \n\nYou could ask the study team at the beginning of the study if they expect the research results to have generazability to larger groups. You can also ask how generalizable the results are likely to be. \n\nThe word may also be used in publications about research, especially in the \"Discussion\" section.
Related Terms: usefulness, meaning", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "genetic testing", "definition": "Glossary Definition: A medical test that could identify a health risk to a person or their biological family members by looking at their genes (DNA).
Use in Context: Genetic testing is done on cells from blood, tissues, and other fluids from the body.
More Info: In clinical research, genetic testing looks for changes in DNA that are different from what is most common.\n\nGenetic testing is done on DNA, which is the genetic material found in cells. DNA provides instructions for how the body grows and develops. Changes or differences in DNA are called variants. Some variants may be linked to increased risk for a disease and affect personal health. However, not every variant is understood or has a known health effect, and more could be learned about its effect in the future.
Other Info to Think About When Joining a Study: You may hear the term \"genetic testing\" from both your regular doctors and researchers involved in studies. You may even hear about genetic testing kits that you can buy yourself. \n\nSome research studies may have genetic testing as part of the study or offer it to participants if they are interested. If genetic testing is part of a research study, you may want to ask more questions, including what information could be learned during testing, what different results could mean for you or your biological family members, and if there are any risks associated with the testing. Sometimes differences are found but whehter those differences are important is unclear. Discussing these possibilities with the study team could be helpful.\n\nYou may want to ask if you will receive the results from genetic testing and if these results will be shared with anyone else besides the study team. You may also want to ask if there will be a genetic counselor to explain the results to you if the study team shares this information with you.
Related Terms: variant, SNP, mutation", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "hazard ratio", "definition": "Glossary Definition: A measure of risk that compares two treatments in the same study.
Use in Context: Studies with more than one group use hazard ratios to compare whether one group has more adverse events than the other.
More Info: The hazard ratio is the relative risk of an event happening in one group compared to another. \n\nFor example, in a drug study, the group getting the study treatment may have headaches two times more than the control population. The hazard ratio would be 2, meaning that the study treatment group has twice the chance of getting headaches compared to the comparison group.
Other Info to Think About When Joining a Study: You might see the word \"hazard ratio\" in research reports and articles that describe the results of research studies. This is a technical math term and will not usually be used in materials designed especially for patients and participants. \n\nIf you see this word in a study document for a study you are considering, enrolled in, or completed, you can ask the researcher or study team any questions you might have.
Related Terms: progression-free survival, relative risk", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "healthy volunteer", "definition": "Glossary Definition: A study participant who does not have a disease or condition, including the one being studied.
Use in Context: A healthy volunteer should not have any known diseases or conditions.
More Info: A healthy volunteer may test the study treatment's safety \n or be the comparison (control) during the study.
Other Info to Think About When Joining a Study: Some studies recruit healthy volunteers. You may see the term \"healthy volunteer\" in the consent form or other information about the study.\n\n If you are healthy and wish to volunteer for a study, you will be screened to make sure you meet the study criteria. Feel free to ask the researchers any questions you might have about what the study is about and what you will be asked to do if you join.
Related Terms: participant, subject, study participant, research participant, research subject, study subject, healthy control", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "hereditary", "definition": "Glossary Definition: A parent's features and traits being passed to their biological children before birth.
Use in Context: Traits that are hereditary include eye color and sometimes an increased risk of a certain disease.
More Info: Some physical or behavioral traits are hereditary. This means the traits are transferred from the parents to their biological children through genes and before the children are born.
Other Info to Think About When Joining a Study: You may see the term \"hereditary\" used in a variety of different situations. It could be used when explaining why a disease might develop or a study to look at hereditary condition. \n\nFor example, hereditary may be used in a consent form to describe a type of disease that is being studied.\n\nIf you are confused or have questions, you should feel free to ask your study team for more information.
Related Terms: genotypic phenotypic, mental health, physical info, traits (the manifestation of genetic information)", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "hypothesis", "definition": "Glossary Definition: An idea that is tested in a research study.
Use in Context: The hypothesis in a research study is tested to see if it is true or not.
More Info: A hypothesis is sometimes described as an educated guess, idea, or question that is a starting point for research. The hypothesis is usually presented as a statement, that is tested during the research.\n\nFor example, a research study may have the hypothesis that one treatment causes fewer side effects than another. The study would be designed to test whether that is true.
Other Info to Think About When Joining a Study: The word \"hypothesis\" may be used in discussions about what researchers are trying to learn through the study. You may see this word in the title of the study you are thinking of participating in or in the background information\n\nYou may also hear the study team talking about testing their hypothesis through the research. The word \"hypothesis\" can also appear in publications about the statistics and data collected from the study. \n\nYou can always ask the study team about the hypothesis that is being tested through the research.
Related Terms: premise, supposition, thesis, theory", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "immune response", "definition": "Glossary Definition: The body’s reaction to a substance, illness, or infection.
Use in Context: An immune response is the body's way to fight something it thinks might be harmful.
More Info: The immune response is how the body identifies and defends itself against germs like bacteria, viruses, and substances that seem to be harmful. An immune response helps the body stay safe. \n\nFor example, a vaccine works by causing an immune response so that the body can fight off the germ, and remember to attack it if the germ ever comes back.
Other Info to Think About When Joining a Study: You may see the term \"immune response\" used in a variety of ways in the research study context. For example, a study may be researching participants' immune responses to certain things or how different investigational products may affect a person's immune response.\n\nIf you have questions about a study that is looking at immune response, be sure to ask your study team for more information.
Related Terms: Immune system, antigen, antibody", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "incentive", "definition": "Glossary Definition: Something that supports or encourages research participation.
Use in Context: An incentive to join research might be to help researchers learn more about a condition or find a new treatment.
More Info: Incentives for research participation can be feeling good personally, receiving a gift card or payment, or getting entered into a raffle. \n\nIncentives can also include payment beyond the costs of participating or access to free medication. Incentives often help enrollment. \n\nA participant should never feel that an incentive pressures them to join the study or remain in the study.
Other Info to Think About When Joining a Study: You may hear the term \"incentive\" before you enroll in a study. The study team may provide incentives for you showing up to study visits or when you complete the study. \n\nYou may want to ask if study incentives could impact your eligibility for any social benefits if you are using things like SNAP or are on disability. Incentives can be in the form of money and may need to be reported on your taxes.\n\nYou should feel free to discuss with the study team whether any kind of incentive will be offered and in what form.
Related Terms: offer, motivation", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "incidence", "definition": "Glossary Definition: Number of new cases or events during a period of time.
Use in Context: The incidence rate tells us how many new cases of a specific disease or condition develop during a certain period of time.
More Info: Measuring the incidence is a way to keep track of how many new cases or events happen in a population at risk during a given time period.\n\nThe incidence is generally reported as a rate. \n\nFor example, in a study with 10 participants, if 3 people report headaches after taking the study medication and 7 do not, the incidence of headaches is 30% (3/10 = .3 or 30%)
Other Info to Think About When Joining a Study: You might see the word \"incidence\" used to describe the frequency of a disease or condition. \n\nThe word can also be used to describe the expected or actual number of adverse events in a study.
Related Terms: frequency, rate, prevalence", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "inclusion criteria", "definition": "Glossary Definition: A list of requirements a person must meet to take part in a study.
Use in Context: If someone wants to join a study, they must meet all the inclusion criteria.
More Info: For example, if a study is for adults only, a person can only consider joining if they are 18 years of age or older.
Other Info to Think About When Joining a Study: You may hear the term \"inclusion criteria\" when researchers talk about who can join a study. \n\nThere may be reasons why a person cannot be included in a research study. \n\nBefore you join a study, the study team will ask you some questions and possibly do some medical tests. This is for your safety and for scientific reasons to confirm that you meet the requirements of the study.
Related Terms: eligibility criteria, exclusion criteria", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "informed consent", "definition": "Glossary Definition: The process of learning and discussing the details of a research study before deciding whether to take part.
Use in Context: Informed consent is required for most research studies before a person can join as a participant.
More Info: Informed consent is an ongoing conversation that occurs before someone can participate in a study and whenever information about the study changes.\n \nA consent form is used as part of the informed consent process.
Other Info to Think About When Joining a Study: You may hear about \"informed consent\" often before you join a research study, and throughout your participation. A member of the study team will explain the research and answer any questions you have.\n\nBefore you agree to join a study, you should understand what the research is about and what you will need to do if you enroll. \n\nDo not be afraid to ask as many questions as you need to. Someone from the study team should answer and clarify anything that is confusing. You can also take time to think about whether you want to join a study or not.
Related Terms: consent, consent form", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "infusion", "definition": "Glossary Definition: A way to give a fluid to the study participant, usually through a vein.
Use in Context: A study treatment given to a participant through their vein is an infusion.
More Info: In an infusion, the fluid could be a study treatment or other liquids, like ones that are given for hydration.
Other Info to Think About When Joining a Study: The term \"infusion\" will be used if there is a study treatment that is given through an infusion. If you are thinking about joining a study that has one or more infusions, you can ask what the infusion is for and what it contains. \n\nYou can always ask the study team to clarify any study procedures.
Related Terms: intravenous infusion, intervention, intravenous injection", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "Institutional Review Board (IRB)", "definition": "Glossary Definition: A team of people who review studies to protect the rights and welfare of study participants.
Use in Context: Participants can only enroll in a study after an IRB reviews and approves the study protocol.
More Info: A research study must be approved by an IRB before it starts.\n \n The IRB members are not part of the study team. IRB members come from many different backgrounds and can be medical, scientific or non-scientific experts.
Other Info to Think About When Joining a Study: You may see the term \"institutional review board\" in the consent form in a section about which group approved the study.\n\nYou may also hear members of the study team talk about the IRB and getting the IRB's approval before any study activities can take place.\n\nThe contact information of the IRB that is overseeing a study should be included in the consent form. You can reach out to the study's IRB if you have any concerns or complaints about your experience being in a research study.
Related Terms: committee for the protection of human subjects, independent ethics committee, independent review board, research ethics committee, ethics committee", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "intermittent", "definition": "Glossary Definition: Not regular or predictable.
Use in Context: The participant reported intermittent dizzy spells since the last study visit.
More Info: When something is intermittent, it means that it happens more than once, but does not happen on a schedule, is not planned, and is not predictable.
Other Info to Think About When Joining a Study: The term \"intermittent\" may come up when discussing adverse events that could happen during a research study. You can always discuss with the study team any questions you have about the possible adverse events in a study.
Related Terms: time to time, randomly, occasionally, on and off, every so often, Infrequently", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "investigational medicine", "definition": "Glossary Definition: A treatment or drug that is not yet approved for the condition being studied.
Use in Context: Being in a research study is one way a patient might have access to an investigational medicine.
More Info: Every country has health authorities or government agencies that review and approve studies of investigational medicines. They then use the study data on risks and benefits to decide whether the investigational medicine is safe and effective and whether it can be approved for the condition or purpose.
Other Info to Think About When Joining a Study: You may learn about an \"investigational medicine\" if you are thinking about joining a study that is looking at a treatment that is not yet approved. Your doctor or the study team may mention the investigational medicine in discussions or in the consent form. \n\nYou could ask the study team to explain more about why the investigational medicine is being studied. You may also want to ask about how the investigational medicine has been studied before and what the study objectves are.
Related Terms: investigational use, experimental drug/medicine, study treatment, intervention, investigational product, investigational drug, study medication, study medicine, drug candidate", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "Investigational New Drug (IND) application", "definition": "Glossary Definition: An application to the United States Food & Drug Administration (FDA) to get permission to use a drug in a research study that enrolls people.
Use in Context: Researchers submit an Investigational New Drug application to the FDA to get permission to study a new use for a drug.
More Info: The Investigational New Drug (IND) application is specific to the United States (US). Researchers testing a new drug, or an approved drug for a new use, must get approval of an IND from the US Food and Drug Administration (FDA). The FDA is a government agency that regulates drugs and devices that are used in patients. Once approval is obtained, the research is often said to be conducted \"under an IND.\"\n\nSimilar processes exist elsewhere in the world. For example, in Europe approval is obtained via a Clinical Trial Application (CTA).
Other Info to Think About When Joining a Study: If you join a study that is testing a medicine under an Investigational New Drug (IND) application you should feel free to ask about what safety information about the study treatment is already known, and why this particular medicine is being tested. You can also find out more about the risks and what kind of care you can expect to receive if there are any adverse events.
Related Terms: investigational medicine", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "investigational product", "definition": "Glossary Definition: A drug, device, vaccine, or other treatment being tested in a study.
Use in Context: The investigational product is what is studied in a clinical trial.
More Info: The Investigational New Drug (IND) application is specific to the United States (US). Researchers testing a new drug, or an approved drug for a new use, must get approval of an IND from the US Food and Drug Administration (FDA). The FDA is a government agency that regulates drugs and devices that are used in patients. Once approval is obtained, the research is often said to be conducted \"under an IND.\"\n\nSimilar processes exist elsewhere in the world. For example, in Europe approval is obtained via a Clinical Trial Application (CTA).
Other Info to Think About When Joining a Study: If you join a study that is testing a medicine under an Investigational New Drug (IND) application you should feel free to ask about what safety information about the study treatment is already known, and why this particular medicine is being tested. You can also find out more about the risks and what kind of care you can expect to receive if there are any adverse events.
Related Terms: investigational treatment, study treatment, intervention", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "investigator", "definition": "Glossary Definition: A person who leads a research study.
Use in Context: The investigator is responsible for making sure the study is carried out as planned.
More Info: A study can have many investigators. An investigator could be a doctor, scientist, or other health professional. \n\nA principal investigator is in charge of making sure the whole research study is being conducted correctly.
Other Info to Think About When Joining a Study: You may hear the term \"investigator\" when you learn about the different people involved in doing a study. The investigator who is running the study may be listed in the consent form.. \n\nYou can ask for the investigator's name and also find out who you should contact in case you have any problems or questions during the study. It may be someone other than the investigator.
Related Terms: researcher, study doctor, principal investigator, sub-investigator, co-investigator, coordinating investigator, site investigator", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "longitudinal study", "definition": "Glossary Definition: Research that collects data from the same participants over a long time.
Use in Context: A longitudinal study collects information from a group of participants over a given period of time.
More Info: Longitudinal studies help researchers see how specific factors about participants change over time.\n\nLongitudinal studies can be many weeks, months or years, depending on the topic being studied. \n\nFor example, enrolling 3 year old children to see whether a specific early childhood education program affects later learning is a longitudinal study. Another example is finding out whether a study treatment prevents a disease from getting worse over time.
Other Info to Think About When Joining a Study: If you are considering joining a longitudinal study you should ask questions about how long the commitment is and what will be expected. A research study may have the term \"longitudinal\" in its title or used to describe follow-up study visits.\n\nIf you have questions about how this term is used, ask the study team before deciding to join this study.", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "Magnetic Resonance Imaging (MRI)", "definition": "Glossary Definition: A way to take pictures of the inside of a person’s body with a machine that uses strong magnets and radio waves.
Use in Context: Magnetic Resonance Imaging (MRI)I is most often used to take pictures of bones, tissues, organs or the brain.
More Info: During an MRI, powerful magnets and radio waves are used to create very detailed pictures of the inside of a person's body. MRIs do not involve radiation.\n\nAn MRI may be done as part of screening, or as a way to collect data about a participant throughout the study.
Other Info to Think About When Joining a Study: You may have heard the term \"MRI\" when talking to your regular doctor. Some research studies may involve getting an MRI. If you have an MRI you will not be exposed to any radiation.\n\nYou can ask why a study is using MRIs. You may also want to know if the study team will share your MRI pictures with you or your regular doctors. In general, MRI research scans are done for the research and not to identify specific health problems. If you have concerns about your health, please discuss them with your regular doctor.
Related Terms: imaging study, CT scan, X-ray", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "master protocol", "definition": "Glossary Definition: An overall research plan that guides sub-studies that have their own research questions
Use in Context: A master protocol includes information about more than one study and allows the research to happen more quickly.
More Info: A Master Protocol is the main protocol that guides sub-studies that can each be done at the same time.\n\nMaster Protocols are used because they can help get answers more quickly and efficiently. They generally need fewer participants and offer more study options. \n\nBasket Trials, Umbrella Trials, and Platform Trials are all types of Master Protocols.
Other Info to Think About When Joining a Study: You may see studies refer to a \"Master Protocol.\"\n\nThese studies will have sub-studies that participants join. You can ask all about how the studies are designed, whether you can choose which sub-study you will be in, and how your experience in one sub-study may be different from other sub-studies.
Related Terms: basket trial, umbrella trial, platform trial", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "maximum", "definition": "Glossary Definition: The most or largest amount.
Use in Context: A study treatment should have the maximum benefit without causing serious side effects.
More Info: The word \"maximum\" can refer to anything that is measured. In a study, it may be the \"maximum age\" of participants eligible for the study, or the \"maximum dose\" permitted, or the \"maximum amount of blood\" that may be taken at any one time.
Other Info to Think About When Joining a Study: You may see the term \"maximum\" used in a variety of different ways. For example, the study team may talk about the maximum dose of a medication you can take. Or they may talk about the maximum number in a range of results from some type of research test.\n\nIf you are unclear about how the study team is using the word, please ask them to explain more.
Related Terms: most, largest, greatest", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "mean", "definition": "Glossary Definition: The average.
Use in Context: In math, the mean is the average value of a set of numbers.
More Info: The mean is calculated by adding all the numbers in a set together, and dividing by the number of values. \n\nFor example, in a group of four people who are aged 10, 20, 30, and 40, the mean (or average) age of the group members is 25. \n\nThis is calculated by first adding all four numbers together, and then dividing by 4. \n\nSo, 10 + 20 + 30 + 40 = 100. And 100/4 = 25. Thus, the mean, or average age, is 25.
Other Info to Think About When Joining a Study: You may see the term \"mean\" used to describe an average of all the study data using numbers. You may see this term used in study results reports. \n\nIf you receive study results that report the mean of any data and you have questions about that information, you can reach out to the study team to learn more.
Related Terms: average, median", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "median", "definition": "Glossary Definition: The middle number in a set of numbers when listed in order from lowest to highest.
Use in Context: In math, the median is the middle number in a set of numbers.
More Info: The median is the middle, and it is not the same as the mean or average.\n\nFor example, 5 is the median in the set of numbers 2, 3, 5, 30, and 50, because 5 is in the middle when the numbers are ordered from lowest to highest.\n\nFinding out the median can be useful if there are data that are outside the expected range. In the example above, 5 is the median, and that is far lower than the mean of 18 (that is, 2+3+5+30+50= 90; 90/5=18), showing that the data are not evenly spread out.
Other Info to Think About When Joining a Study: You might see the term \"median\" used to describe the the middle number in a set of data. You may see this term used in study results reports. \n\nIf you receive study results that report the median of a data set and you have questions about that information, you can reach out to the study team to learn more.
Related Terms: middle", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "minimal", "definition": "Glossary Definition: Very small.
Use in Context: A participant must report every adverse reaction even if the impact on their lives seems minimal.
More Info: The risks of a study are minimal or very small if they are about the same as the risks of daily living. Adverse events may be described as minimal if they don't last very long or are mild.\n\nSome research is considered to be minimal risk, meaning the risk to participants should not be very high.
Other Info to Think About When Joining a Study: You may hear the term \"minimal\" when discussing whether a study is minimal risk or not. \n\nIf you have any questions about the risk of the study or otherwise how the word \"minimal\" is being used, you should feel free to discuss it with the study team.
Related Terms: limited, negligible", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "minimum", "definition": "Glossary Definition: The smallest or least amount.
Use in Context: A research study needs a minimum number of participants so enough data can be collected to answer the study questions.
More Info: The word \"minimum\" can refer to anything that can be measured. \n\nFor example, in a research study the \"minimum age\" in eligibility criteria is the youngest age that is allowed for a person to be enrolled.
Other Info to Think About When Joining a Study: The term \"minimum\" may be used in a variety of contexts. For example, the study team share information about the minimum amount of time you have to do a study task. They might also talk about the minimum age for study participants. \n\nIf you are unclear about how the study team is using the word, please ask them to explain more.
Related Terms: least, lowest", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "minor", "definition": "Glossary Definition: Someone considered too young to give legal consent.
Use in Context: A minor is a person who is under the legal age of majority in a specific place.
More Info: Studies that enroll children need to have processes in place to ensure a parent or a guardian gives consent. The minor, when appropriate, is asked for their agreement before they are enrolled in the study. This is called assent.
Other Info to Think About When Joining a Study: “Minor” is another word for child or pediatric participant. If you are a parent or guardian with a child who may join a research study, you can ask any questions, such as whether if there is a separate assent process for the child and whether there are additional protections for children.
Related Terms: assent, child, young person", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "monitor", "definition": "Glossary Definition: To observe, check or evaluate something in a study over time.
Use in Context: In ongoing studies, researchers have a responsibility to monitor the study participants.
More Info: Participants might have their health and safety monitored depending on what type of study they are in.\n\nData can be monitored as well to make sure they are being collected and stored correctly.
Other Info to Think About When Joining a Study: The word \"monitor\" is often used to describe the steps and processes that will be followed to make sure participant safety is being protected and the study is being conducted properly.\n\nYou can always ask the study team about how your safety and well-being will be monitored and how the study team will monitor the overall study.
Related Terms: audit, investigate, assess, watch, oversee, observe, evaluate", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "morbidity (rate)", "definition": "Glossary Definition: The number of people who develop a disease or illness in a group over time.
Use in Context: The morbidity rate in a study refers to how many people have or develop a condition.
More Info: The morbidity rate is calculated by counting how many new cases or illnesses occur in a given number of people in a certain amount of time.\n\nMorbidity can also refer to medical problems caused by a treatment. \n\n For example, if 100 people get a rash from a new medication in a study of 1000 people, the morbidity rate of rash is 10% (e.g., 100/1000=1/10 or 10%).
Other Info to Think About When Joining a Study: In the clinical research context, the term \"morbidity rate\" can be found in study descriptions. A study could be looking at ways to decrease the number of people developing a disease or illness.\n\nSometimes you might see the term \"morbidity rate\" used in study results as well to describe how many participants develop new diseases or illnesses, or even how the morbidity rate of the study compares with the general public or other groups.\n\nIf you see this term when you are reviewing a research document, you can always ask the study team about how it might be important for your participation in the study.
Related Terms: illness rate, mortality", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "mortality (rate)", "definition": "Glossary Definition: The number of deaths in a group of people over time.
Use in Context: The mortality rate in a study refers to how many people died over the course of the study.
More Info: The mortality rate is calculated by counting how many deaths occur in a group of people during an amount of time.\n\nFor example, if 6 people died in a study of 200 people, the mortality rate would be 3% (e.g., 6/200=3/100=3%)
Other Info to Think About When Joining a Study: In the clinical research context, the term \"mortality rate\" can be found in study descriptions. A study may look at ways to decrease the number of deaths (\"mortality rate\") in a group of people or from a specific cause. The term may also be used to describe how many participants died over the course of the study.\n\nIf you see this term when you are reviewing a research document, you can always ask the study team about how it might be important for your participation in the study.
Related Terms: death rate, morbidity", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "multicenter trial", "definition": "Glossary Definition: A study that takes place at more than one research center.
Use in Context: A multicenter trial can be done in many locations in a country or even around the world.
More Info: A multicenter study is a way to conduct research at more than one research center or site to make sure there are enough participants and people from many different backgrounds. A research center can be hospital, clinic, or research institution.
Other Info to Think About When Joining a Study: You may hear the term \"multicenter study\" when the study team describes what kind of research study you could join or when you are reading the consent form. \n\nIf a study is multi-center, you could ask what the other study locations there are. You may want to clarify who the investigator is at your research center and who to contact if you have questions.
Related Terms: multi-site study, investigator", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "negative test result", "definition": "Glossary Definition: A test result that shows a person does not have what was tested for.
Use in Context: A negative COVID test means that the person most likely does not have COVID.
More Info: A negative test result for a disease, condition, genetic marker, or biomarker means that a person likely does not have the condition being tested for. \n\nA \"false negative\" means that the test incorrectly found someone to be negative when they are actually positive. Tests try to reduce the number of false negatives.
Other Info to Think About When Joining a Study: You may see the term \"negative test result\" in the context of study screening or a study procedure. You may want to ask if you will get the test result and if yes, how long it will take for the results to be ready. If you have any questions about this test result you should discuss with the study team.
Related Terms: sensitivity, specificity, false negative", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "negligible", "definition": "Glossary Definition: So small that it has little to no impact.
Use in Context: Some research results are negligible in terms of what they mean for patient care.
More Info: If an event, effect, or result is negligible, it is not considered important or impactful.
Other Info to Think About When Joining a Study: The word \"negligible\" could be seen in the context of clinical research study results to describe that a difference between data or outcomes is so small that it is unlikely to have an impact on patient care.
Related Terms: unimportant, insignificant, inconsequential, minor, minimal", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "non-compliance", "definition": "Glossary Definition: Not following research requirements.
Use in Context: Non-compliance with the research protocol can impact the outcomes of the research.
More Info: Non-compliance can apply to either researchers or participants not following the study requirements. Researchers must comply with the laws and regulations of research and the protocol as written. Participants must comply with study procedures. \n\nParticipant non-compliance could result in the principal investigator removing a participant from the study for not following the study instructions.\n\nRegulators, sponsors or the IRB could check a research team for non-compliance with research laws and regulations.
Other Info to Think About When Joining a Study: You might hear the term \"non-compliance\" when study team tells you about things you should do while in the study in order for the data to be complete. If you do not do these things, non-compliance could be an issue. For example, the study team may say you have to take an investigational medicine 3 times a day. If you only take it twice a day, it would be considered non-compliance with the study procedures. There may also be things you cannot do while you are in the study and if you do them, that is also considered non-compliance. \n\nBefore signing up for a study, be sure to ask for clarification if you are unsure about what you need to do while you are in the study and what you cannot do. You may also want to ask what will happen if you are non-compliant. For example, if you have to take the investigational medicine once a day but you forget to, you can ask the study team what you should do in that situation.", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "non-inferiority trial", "definition": "Glossary Definition: A study to test if a study treatment works about as well as another treatment for the same condition.
Use in Context: One example of a non-inferiority trial would be to test whether a new medicine for asthma works as well as one that some patients already use.
More Info: Non-inferiority trials are done to find more treatment options that work as well as ones that are already approved for a specific disease or condition.
Other Info to Think About When Joining a Study: If you are considering joining a non-inferiority trial this means that the study will be looking at whether one treatment is as good as another. You should ask the study team any questions you have about the treatments being studied or how you will be assigned to a study arm.
Related Terms: non-inferiority study, superiority trial, equivalence trial, control", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "objective", "definition": "Glossary Definition: A purpose or goal of a study.
Use in Context: The research objective is the scientific question to be answered by the study.
More Info: The study protocol always includes the objective(s) of the research. \n\nFor example, an objective could be to find out whether a study treatment causes a certain symptom to get better.
Other Info to Think About When Joining a Study: You might see the word \"objective\" in the consent form or study protocol. You could also hear about the objectives when speaking with the study team. \n\n The objective of the study refers to what the study is trying to find out. If you participate in a study you should undertsand the objective and ask the study team any questions you might have.
Related Terms: study objective aim, goal, purpose", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "observational study", "definition": "Glossary Definition: A study that collects health information about study participants without giving a treatment.
Use in Context: In an observational study, data will be collected about each participant but no one will be assigned to get a study treatment.
More Info: For example, a study to see whether those who smoke cigarettes report higher rates of lung cancer than those that do not would be an observational study.\n\nData are collected using methods like surveys and lab tests, as well as from other sources like medical records and historical datasets.
Other Info to Think About When Joining a Study: If you enroll in an observational study this means data will be collected about you but no study treatment will be assigned.\n\nYou may want to ask how the research team will collect data from you during your time in the observational study.
Related Terms: natural history study, cohort study, case control study, empirical study", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "observe", "definition": "Glossary Definition: To watch or see how participants are doing in a study.
Use in Context: Participants may be observed for a few minutes after taking the study treatment to check for any early adverse reactions.
More Info: To observe participants is one way to collect and document data for a study in a planned way.
Other Info to Think About When Joining a Study: The study consent form or any information the study team gives you about the study you are participating in may mention that you will be observed. This could happen the first time you take an investigational medicine or after you take part in some type of research test. \n\nYou can ask how long you will need to be observed if that is something that will happen to you. For example, after getting a vaccine, you may have to be monitored for some time to make sure you have no reactions.", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "occasionally", "definition": "Glossary Definition: Once in a while.
Use in Context: Occasionally there are changes in the study that require participants to re-consent to continue.
More Info: The protocol and consent form will discuss how often certain procedures or possble harms will happen. If the frequency is described as being ocassionally, it means an event won't happen that often or predictably.
Other Info to Think About When Joining a Study: The word \"occasionally\" can be used to describe how often an adverse event occurred in a research study.\n\nYou might want to know more about what that frequency could mean if you take a particular study treatment.
Related Terms: Sometimes, infrequent, rare", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "odds ratio", "definition": "Glossary Definition: The chance of a health event happening in one group compared with the chance of the same event happening in another group.
Use in Context: An odds ratio is a measure of the link between an exposure and an event.
More Info: An odds ratio compares the odds of two different groups. It is used to describe the association of exposure to one variable of interest (e.g. health characteristic, aspect of medical history) to a disease or disorder, compared with the lack of the variable with the disease or disorder. It also looks at the strength of that association. \n\nThe odds ratio can also be used to determine whether a specific exposure is a risk factor for a particular outcome. For instance, the ratio of the odds of lung cancer in smokers divided by the odds of lung cancer in non-smokers is 14.\n\nTwo events are not related if the odds ratio equals 1, i.e., the odds of event are the same in either the presence or absence of the other event.\n\nIf the odds ratio is greater than 1, thentwo events are positively associated (correlated) i.e. The presence of one event increases the chance of the other one being present.
Other Info to Think About When Joining a Study: You may see the term \"odds ratio\" when reading results information of a research study. The results section of a publication will talk about the data and statistics. \"Odds Ratio\" is a technical math term and will not usually be used in materials designed especially for patients and participants. \n\nIf you see this word in a study document for a study you are thinking about joining, enrolled in, or completed, you can ask the researcher or study team any questions you might have.
Related Terms: relatedness, relationship", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "off-label", "definition": "Glossary Definition: The use of a treatment in a different way or for a condition other than what it is approved for.
Use in Context: Doctors can prescribe a medicine or other treatments off-label.
More Info: The \"label\" in \"off-label\"refers to the specific, intended use that the medicine or product has been approved for. This approval is given by regulators, health authorities or government agencies. This applies to drugs and devices.\n\nA doctor may prescribe a drug off-label when there is reason to believe that the drug could be helpful when used in a new or different way or for a different condition, as in a different age group, dosage, way to take it, or condition.
Other Info to Think About When Joining a Study: Some research studies are investigating a treatment that is being given as an \"off-label\" use.\n\nYou can ask the study team what the treatment is approved for and what its intended use is. You may also want to ask why they want to use this treatment off-label and what information they may have that could suggest it would work off-label.
Related Terms: off-label use, unapproved, unapproved use, intended use", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "open-label", "definition": "Glossary Definition: A type of study where participants and research staff know which treatment participants are being given.
Use in Context: When a study treatment is open-label, participants know what they are taking.
More Info: Open-label studies are used in a number of settings, including to learn about the long-term effects of study treatments. Sometimes it is impossible to mask which treatment a person will receive. Sometimes participants who have finished the treatment part of the stud keep sharing data so researchers can see how long the effects of treatment last.
Other Info to Think About When Joining a Study: Some research studies are investigating a treatment that is being given open-label, without masking what the participant is receiving.\n\nIf you are participating in a research study that is testing a study treatment, you can ask the study team whether you will be able to continue taking the treatment as an open-label use.
Related Terms: unblinded, unmasked", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "outcome (of study)", "definition": "Glossary Definition: A description of the overall results of the study.
Use in Context: The study outcome describes what the researchers learned from the research.
More Info: The study outcome will report the study results, such as whether or not a study treatment helped participants.
Other Info to Think About When Joining a Study: You might see the word \"outcome\" when you read about a research study's results and the overall conclusions that researchers came to based on the study data.\n\nIf you have any questions about the outcome of the study and what a study's results mean for you, you can ask the study team or discuss with your regular doctor.
Related Terms: result, endpoint, primary endpoint, surrogate, secondary endpoint, clinical endpoint", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "outcome measure", "definition": "Glossary Definition: The way that a study endpoint is measured.
Use in Context: An outcome measure is used to collect data for the study.
More Info: A study will use one or more outcome measures to collect the data that is needed to answer the research questions. For example, an outcome measure could be a blood test to find out how well the study treatment works to lower cholesterol.
Other Info to Think About When Joining a Study: An outcome measure can be a questionnaire, survey, or any kind of assessment that is done to see any changes over the course of the study. Example of assessments include blood test, blood pressure reading, MRI, etc.\n\n If you have any questions about the outcome measures being used in a study, feel free to ask the study team.
Related Terms: questionnaire, assessment, survey, data, endpoint", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "p-value (probability value)", "definition": "Glossary Definition: A number that researchers use to show that a result did not occur by chance.
Use in Context: The p-value is used in research to show whether a difference in effect between treatments is due to chance.
More Info: The p-value is part of the scientific process. It is a number used when analyzing research data and reporting research results. \n\nThe p-value shows whether the results could have occurred by chance.\n\nWhen a p-value is very small, it means that it is less likely to have occurred by chance.\n\nFor example, if a study has a p-value of 0.05, this means that if you did the study 100 times, the results would likely be the same 95 times. \n\nIt is important to note that even if something has a small p-value and is statistically significant, the result may not make a big difference to patients. For example, a drug may shrink a tumor but not extend a person's life.
Other Info to Think About When Joining a Study: You might see the term \"p value\" in a publication about research where the study results and statistics are reported.\n\nThe article may include a results section that has more information about what the p-value for the study is and what that means for the results.\n\nThe p-value could also come up in Plain Language Summaries of a study's results\n\nIf you have any questions about how the p-value is being reported, feel free to talk to the study team.
Related Terms: statistically significant", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "participate", "definition": "Glossary Definition: To take part in a study.
Use in Context: By signing the consent form, a person agrees to participate in the study.
More Info: To participate in a study is voluntary. \n\nBefore agreeing to participate in a study, a person should know what the study procedures will be. \n\nA person can always withdraw from a study if they decide that they no longer want to participate. \n\nBefore doing so, the participant should have a conversation with the study team.
Other Info to Think About When Joining a Study: Your doctor may ask you if you want to participate in a study. Additionally, during the consent process, a person from the study team will make sure you want to participate in the study before joining. \n\nYou could ask about the benefits and risks if you participate in the study. It will also be important for you to know what the study procedures are when you participate in this study.
Related Terms: join, be a part of, volunteer", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "Patient Reported Outcomes (PROs)", "definition": "Glossary Definition: The information that patients share about their own health or well-being to answer questions in a study.
Use in Context: Patient Reported Outcomes are a way to hear directly from patients about their health or study experience.
More Info: Measures to collect Patient Reported Outcomes (PROs) includes how a participant feels during the study, such as mood, sleepiness, amount of pain, and adverse events. Participants can explain how the disease or the study treatment is affecting their ability to do things like exercising, sleeping, going to work, etc.\n\nPROs might be collected with surveys, questionnaires, diaries, or interviews. \n\nPROs are important because they allow patients to report directly how they feel, and not as observed by the doctor, researcher, or someone else. \n\nPROs can often measure what is important to participants. Analyzing the data allows researchers to draw some conclusions about the outcome.
Other Info to Think About When Joining a Study: A study you decide to participate in may involve collecting patient reported outcomes (PROs). This may be listed in the consent form as something you need to do if you enroll in a study. \n\nYou may wish to ask how PROs will be collected because it could be from surveys, interviews, diary entries or another way not mentioned here. You may also want to clarify how much detail they want when you provide these answers. \n\nMost PROs are not reviewed in real time so participants should not expect to hear back from the study staff about what was entered.
Related Terms: Patient Reported Outcome Measure (PROM)", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "peer review", "definition": "Glossary Definition: Evaluation by independent experts.
Use in Context: Medical journal articles and grant applications often go through a peer review process.
More Info: Peer review involves a critical assessment of the ethics, methods, conduct, analysis, and reporting of research by other experts in the field. Peer review is done by people who are independent and not involved in the research study conduct.\n\nA peer review process helps maintain rigor, independence, validity, standards and integrity of research studies. \n\nA peer review process asks \"Does the content we are reviewing meet the expected quality and standard?\"
Other Info to Think About When Joining a Study: You could hear about scientific journal articles going through a \"peer review\" process before they can be published. Many scientific journals that report research results include a peer review step to make sure the information is shared publicly.", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "periodically", "definition": "Glossary Definition: At regular or expected times.
Use in Context: The study staff checks in with participants periodically to see how they are feeling.
More Info: When something happens periodically, it usually means that it happens on a schedule and is expected.
Other Info to Think About When Joining a Study: The word \"periodically\" could be used to describe how the study is monitored or how often some sort of event could happen.\n\nIf something in the study materials is described as \"periodically,\" you may want to ask the study team what that timing will mean for you as a participant in the study.
Related Terms: scheduled, regularly, repeatedly", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "Pharmacodynamic (PD) study", "definition": "Glossary Definition: A study that measures the effects of a drug on the human body.
Use in Context: During a pharmacodynamic study a participant will have their blood and other body fluids collected a few times over a few hours to see how the dose of the study treatment affects their body.
More Info: A pharmacodynamic (PD) study is the study of the effects of the drug on the human body. A PD study helps researchers learn whether the study treatment is having the desired effect on the body, and how the dose of the drug affects the response. For example, a PD study could try to find out if a drug for cancer will attach to a cancer cell and lead to the cell's death.
Other Info to Think About When Joining a Study: You might see the term \"pharmacodynamic study\", when researchers want to find out what effect the drug has on participants' bodies and how their bodies react. This is different from a pharmacokinetic study where the effect of the body on the drug is measured. Researchers use this information to design clinical trials, for example, what doses to give to participants. Because of this, pharmacodynamic studies are usually conducted early in the research process to help guide what is the best dosage for humans to take and to also look at safety in general. \n\nIf you are considering participating in a pharmacodynamic study you can ask the researchers about what is already known about the drug, and how the study team will be monitoring the safety of the participants.
Related Terms: Pharmacokinetic (PK) study", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "Pharmacokinetic (PK) study", "definition": "Glossary Definition: A study that measures what happens to a drug in a person’s body over time.
Use in Context: A pharmacokinetic study often enrolls healthy volunteers first before other participants.
More Info: A pharmacokinetic study means that a participant will have a blood draw and possibly other body fluids taken a few times over a few hours to see how the study treatment was used by the body. \n \nThe PK study is done to find out how a drug is absorbed, moves through, is broken down, and exits the body.
Other Info to Think About When Joining a Study: If you enroll in a pharmacokinetic study a drug or medicine will be given to you and then blood samples will be taken several times over several hours to see how the study treatment was processed by your body. \n\nIf you have any questions about the drug or medicine being tested or how long the pharmacokinetic study will take, you could discuss them with the study team.
Related Terms: Pharmacodynamic (PD) studies", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "pharmacovigilance", "definition": "Glossary Definition: A process to detect, review, and make decisions about drug safety to protect patients.
Use in Context: Scientific drug safety monitoring is called pharmacovigilance.
More Info: Pharmacovigilance is the science of monitoring the effects of drugs and vaccines. It involves detecting, understanding, and preventing adverse events and determining whether observed events are caused by the drug or vaccine.\n\nPharmacovigilance happens during and after a research study, and after a drug is approved
Other Info to Think About When Joining a Study: The word \"pharmacovigilance\" is sometimes used in conversations about the risk and safety monitoring of a drug.\n\nIf you see this word and have any questions about how it is being used, you should ask the study team.
Related Terms: drug safety", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "phase", "definition": "Glossary Definition: A step in the overall clinical research process to test a new drug, device, or treatment.
Use in Context: Research is done in phases to make sure a study treatment is safe and then whether it works before it is approved.
More Info: A phase is a step in the research process. Phases of research studies build on each other and each phase has a separate goal. \n \nPhase 1 studies are usually the first to enroll humans and test for safety. \n \n Phase 2 studies test if the drug, device or treatment works. \n \n Phase 3 studies compare the study treatment to the usual, standard treatment. \n \nPhase 4 studies continue to collect data after a study treatment is approved. These are sometimes called post-marketing studies.
Other Info to Think About When Joining a Study: You may see the term \"phase\" when you are reading about clinical trials. \n\nBefore you enroll in a clinical trial you may want to ask about what phase the study is in. You may also want to know more about the information the study team already has about the risks and benefits of the study treatment that is being tested.
Related Terms: clinical research, clinical trial, preclinical study", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "pilot study", "definition": "Glossary Definition: A small study that is done to test a process before starting a larger study.
Use in Context: A pilot study is a way to detect and fix problems in the study process or to test a specific idea.
More Info: A pilot study enrolls a limited number of participants to discover the best ways to conduct a larger study.
Other Info to Think About When Joining a Study: A \"pilot study\" may test whether an intervention works well enough to continue studying it. Other times, a pilot study could be testing how the study treatment works in the body or the safety and participant experience of a product. You may want to ask if other studies have been done and their results, and about the risks and safety of the study treatment.
Related Terms: Proof of principle; Proof of concept; Exploratory; First-in-Human", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "placebo", "definition": "Glossary Definition: Something that looks like the treatment being studied, but doesn't contain any medicine
Use in Context: Using a placebo in a research study keeps the participant and study doctor from knowing who is receiving the active treatment.
More Info: A placebo is made to look, taste and smell like the active treatment being studied. Depending on the study, a placebo can also refer to a device or sham surgery. A placebo helps the researcher see whether the active study treatment really works. Using a placebo reduces bias. \n\nUsing a placebo in a research study is accepted when the risk of not treating a condition is small or when there is no effective standard of care to compare to.
Other Info to Think About When Joining a Study: When describing the plan of the study, the study team or consent form will say whether or not therea placebo is being used in the study. When a placebo is included in a study, the participants will usually be assigned the placebo through \"randomization.\" This means that whether or not you get the placebo will happen by chance, like flipping a coin. \n\nYou should feel free to ask if there is a chance you could be taking a placebo in the study. You can also ask if you will find out if you are on the placebo at the end of the study. It may also be important for you to ask how they will notify your regular doctors if you are on the placebo or taking an active study treatment if there is a medical need to know.
Related Terms: sham substance, sham surgery, control group, sugar pill", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "placebo-controlled study", "definition": "Glossary Definition: A study with two or more groups where one group is given a placebo.
Use in Context: Placebo-controlled trials are done to show how the study treatment performs compared to those not receiving the study treatment
More Info: A placebo-controlled study compares an active treatment to something that made to look, taste and smell like the active treatment. Using a placebo helps the researcher see whether the active study treatment really works. \n\nPlacebo-controlled trials are usually only done when the risk of not offering an active treatment for a condition is small or when there is no effective standard of care to use as the comparison.
Other Info to Think About When Joining a Study: You will see the term \"placebo-controlled study\" if the research is using a placebo as a control group.\n\nIf you are considering joining a study that has a placebo, you can ask how the researchers decide who gets the placebo. You can also ask if the study team will tell you what you were taking at the end of the study. You can also ask how the researchers will notify your own doctor about what you are taking in the study if there is a medical reason to know.", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "platform trial", "definition": "Glossary Definition: A research study that tests and compares two or more study treatments for a disease or condition, with study treatment groups being added or removed during the study period.
Use in Context: A platform trial is an efficient way to compare many study treatments at the same time.
More Info: A platform trial is a type of randomized controlled trial (RCT). It is sometimes called a Master Protocol. This is because a platform trial uses Master Protocol to test the different study treatments in the same way, using the same design. \n\nA platform trial is done to compare multiple treatments or interventions by comparing them against each other and a control.\n\nThis is a way to do research more efficiently with fewer patients to find an answer. As a platform trial progresses, the research team may add new study treatments to compare and remove ones that are not working or have too many adverse events.
Other Info to Think About When Joining a Study: You may hear about platform trials when you are learning about different types of study designs.\n\nIf you are unsure about what it means for a research study to be a platform trial, you should ask a member of the study team to clarify any of your questions.
Related Terms: master protocol, basket trial, umbrella trial", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "positive test result", "definition": "Glossary Definition: A test result that shows a person has what was tested for.
Use in Context: A positive COVID test means that the person most likely has COVID.
More Info: A positive test result for a disease, condition, genetic marker, or biomarker means that a person is likely to have or to have had the condition.\n\nA \"false positive\" means that the test incorrectly found someone to be positive when they are actually negative. Tests try to reduce the number of false positives.
Other Info to Think About When Joining a Study: You may see the term \"positive test result\" in the context of study screening or a study procedure. You may want to ask if you will get the test result and if yes, how long it will take for the results to be ready. If you have any questions about the test result you should discuss with the study team.
Related Terms: sensitivity, specificity, false positive", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "post-market surveillance", "definition": "Glossary Definition: Continuing to collect and analyze information about the risks and benefits of medicine and devices after they have been approved for patient use.
Use in Context: Post-market surveillance means that the risks and benefits of medicines and devices are being reviewed after the product has been approved for use.
More Info: Post-market surveillance happens after a drug or device has received approval by a goverment health authority.\n\nIt can be done by safety reporting, regular monitoring, or a research study.
Other Info to Think About When Joining a Study: All prescribed and marketed drugs and devices are monitored for safety, termed \"post-market surveillance.\" If you have questions about their safety, you should speak with a member of your usual healthcare team. You may also want to ask how you will be notified if any issues are identified in a drug or device you are using.
Related Terms: pharmacovigilance, monitoring, phase 4 studies, post-marketing studies", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "post-trial access", "definition": "Glossary Definition: When participants can still receive a study treatment after their participation has ended.
Use in Context: Participants should find out if they will have post-trial access to the study treatment.
More Info: Post-trial access applies to drugs and devices.\n\nWhether and how participants will be able to receive a study treatment usually comes up when the treatment has not yet been approved/certified and there are few or no alternatives.
Other Info to Think About When Joining a Study: You might see the term \"post-trial access\" while reading a research consent form. It could be helpful to know whether or not you will be able to still take the study treatment after your time in the study is over.\n\nIf you are confused about what is being offered post-trial, be sure to ask the study team. If there is no mention of post-trial access, you should still ask the study team if there are plans to give you the investigational product after your participation in the study has ended.
Related Terms: continued access", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "preclinical study", "definition": "Glossary Definition: A study to test a treatment in the lab or in animals before testing it in people.
Use in Context: A preclinical study is done to make sure the study treatment is safe enough to give to people.
More Info: If a study treatment has gone through preclinical studies, it means there was lab or animal testing before it was found to be safe enough to give to humans in a clinical trial.
Other Info to Think About When Joining a Study: Most studies involving investigational products are designed based on data from other studies, including preclinical studies.\n\nIf you are thinking about joining a clinical trial, you can ask about the results of the preclinical studies and why the research team thinks this investigational product can be used in humans.", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "prevalence", "definition": "Glossary Definition: Number of known cases or events in a group.
Use in Context: The prevalence tells us how many people in a population have a specific disease or condition.
More Info: Measuring the prevalence of a health issue is a way to understand how many people are affected in a given time period. It measures how common the condition or disease is, regardless of when the person developed the condition or disease.\n\nFor example, in 2021, the prevalence of diabetes in the US was 11.6% of the population, and 14.7% of all adults (i.e., people aged 18 and older).
Other Info to Think About When Joining a Study: The term \"prevalence\" is used to describe how many people are known to have a particular disease or condition. \n\nYou may want to ask the study team what the prevalence of adverse events in past studies was.
Related Terms: frequency, rate, see also incidence", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "primary endpoint", "definition": "Glossary Definition: A study measure that is used to answer the main research question.
Use in Context: The primary endpoint is the main purpose of the study.
More Info: A primary endpoint is how the main research question will be answered. A study is designed to assess the primary endpoint. A study may also have secondary endpoints.
Other Info to Think About When Joining a Study: You may see the concept of the \"primary endpoint\" written about in the consent form or hear about it from the study team. Additionally, if you visit a research study registry (like www.clinicaltrials.gov) you may see studies describe primary and secondary endpoints. The primary endpoint is the main thing the study is measuring. \n\nIf you are enrolling in a study and have any questions about what the main goal of the study is, please ask the study team.\n\nAfter a research study is done and publications are released, you may also read about different types of endpoints.
Related Terms: primary aim, outcome, outcome measure", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "probability", "definition": "Glossary Definition: The likelihood or chance that something might happen.
Use in Context: The informed consent process should include information about the probability of adverse events.
More Info: Probability is also used to describe the likelihood of a risk factor or exposure leading to a condition or disease, for example, what the probability of developing lung cancer is after smoking cigarettes.
Other Info to Think About When Joining a Study: You may see the term \"probability\" used to describe the chance that you will be assigned into one group or another in the study (randomization). You may also see this term when discussing how likely a risk or adverse event might happen.\n\nWhen you see this term, it might be helpful to ask what the probability means for you as a participant in the study or if you are taking a particular treatment. For example: What is the probability that you will get one study treatment over another? What is the probability of a particular risk happening to you?
Related Terms: chance, odds, likelihood, possibility", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "procedures (for participants)", "definition": "Glossary Definition: The activities that participants will be asked to do during the research study.
Use in Context: The procedures listed in the consent form include all of the activities that participants will do while they are in the study.
More Info: Study procedures are all the ways that the researchers collect data and samples from participants. \n\nCommon procedures include interviews, surveys, blood draws, X-rays and scans, and taking a study treatment. \n\nThe procedures that impact participants will all be listed in the consent form (for example, study visits, blood draws, filling out questionnaires, etc). Other study procedures that the study team has to do are listed in the protocol (for example, data collection and entry).
Other Info to Think About When Joining a Study: The consent form will list the \"procedures required for a study.\" The procedures describes how many visits there are and what study activities will happen at each visit. The study team should review these with you and answer any questions you have.
Related Terms: schedule of assessments, schedule of activities", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "progression-free survival", "definition": "Glossary Definition: The length of time without a person's illness getting worse.
Use in Context: Some studies look at progression-free survival to see whether a drug helps keep the disease from getting worse.
More Info: Progression-free survival is often used to assess the treatment of diseases that are slow-growing and difficult to cure.\n\nWhether a disease is getting worse is measured via procedures such as scans, test results, biomarkers, self-report, etc.
Other Info to Think About When Joining a Study: Depending on the kind of study you are considering or reading about, you may see or hear references to \"progression-free survival\" during the informed consent process or other informational study materials. \n\n\"Progression-free survival\" may be used to explain the purpose of a study or explain the reason for particular procedures and tests. For example, a study could be collecting data on how long a person lives without the illness getting worse.\n\nIf you have any questions about what it means for a study to look at progression-free survival, you should ask the study team.
Related Terms: disease-free survival", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "prospective study", "definition": "Glossary Definition: Research that uses new data collected from participants.
Use in Context: Prospective studies actively collect new data from participants.
More Info: A prospective study collects new data over time. \n\nFor example, a lung cancer study might compare two treatments to see if one works better than the other to shrink a tumor.
Other Info to Think About When Joining a Study: The word \"prospective\" is often used to describe the timing of when a study's data are being collected. A prospective study collects data moving forward. It can be helpful to confirm the schedule with the study team.
Related Terms: forward-looking study, real time study", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "protocol", "definition": "Glossary Definition: A complete description of the research plan and procedures.
Use in Context: The protocol is like a recipe to make sure the research study is done in the same way by all of the study team members.
More Info: A protocol is shared among study team members and approved by an institutional review board (IRB) to ensure that the study procedures are conducted consistently. Participants do not usually get to review the complete protocol but its content will be discussed during the informed consent conversation and at study visits.
Other Info to Think About When Joining a Study: You may learn about the term \"protocol\" from the study team or in the consent form. \n\nThe protocol could be discussed when the study team talks about the instructions they have to to follow to run the study. \n\nAlthough the protocol is not always shared direvtly with participants, may the study protocol on trial registration sites like www.clinicaltrials.gov. \n\n Feel free to ask the study team any questions you have about the study protocol.
Related Terms: study protocol, research protocol, consent form, informed consent", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "proxy", "definition": "Glossary Definition: A person who is legally allowed to make research decisions for someone else.
Use in Context: A proxy has legal permission to choose whether someone who cannot give informed consent on their own should be in a study.
More Info: A proxy can be a legal guardian or legally authorized representative (LAR). \n\nA proxy can make decisions based on the wishes or best interests of the potential participant.\n\nA proxy is permitted to sign a consent form on behalf of the study participant.
Other Info to Think About When Joining a Study: You may see or hear the word \"proxy\" used in cases when participants who are legally not able to make research decisions for themselves are being recruited into a study. For example, a proxy might be needed in a situation where someone has a head injury or is so sick that that they are not able to talk or sign the consent form.\n\nIf you are a proxy for someone else, feel free to ask the study team any questions you have about the research study.
Related Terms: guardian, legally authorized representative", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "pseudonymized", "definition": "Glossary Definition: Replace personal details with a code so that data are protected.
Use in Context: To pseudonymize data means that all direct identifiers such as name, birthdate, or address have been replaced with a code.
More Info: Researchers pseudonymize data to mask the identify of a specific participant. This also protects participants' personal information from anyone who does not have a research-related reason to know. \n\nWhen data are pseudonymized, identifiable information is replaced with a code to protect their identity. For example, a person's name might be changed to a code like 14252. \n\nIn some cases, researchers keep the code linking back to the participant for returning results or seeking additional information, but they do not share that code.
Other Info to Think About When Joining a Study: You may see the word “pseudonymize” used in the consent form to describe how data collected in the study will be protected. Researchers are required to make sure that no personally identifiable information is ever released outside of the study to people who should not have access. If you have any questions about how your data will be protected, please feel free to ask the study team.
Related Terms: coded, anonymized", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "purpose", "definition": "Glossary Definition: What the study is testing.
Use in Context: The purpose of research is to answer a scientific question.
More Info: The consent form always includes a description of the purpose of the study.\n\nThe purpose is also described in the protocol.
Other Info to Think About When Joining a Study: If you are a proxy for someone else, you should consider what would be in their best interest and what they would do if they were able to make the decision on their own.
Related Terms: study purpose, objective, aim, goal, rationale, hypothesis, intention", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "Quality of Life (QOL)", "definition": "Glossary Definition: How someone feels and functions day to day.
Use in Context: The goal of measuring participant Quality of Life is to understand how they feel and their mental, physical, and social well-being.
More Info: Quality of Life (QOL) questions look at how someone feels about their life in the context of their culture and values, QOL is also measured in relation to their own goals, expectations, standards, and concerns.\n\nQuality of Life is often based on the person's ability to do or enjoy daily living activities. \n\nFor example, if a person used to take daily walks but no longer is able to, this person's Quality of Life might be impacted negatively.
Other Info to Think About When Joining a Study: The study team may collect information about your Quality of Life during the study. They may want to compare your Quality of Life before the study and your Quality of Life after you start the study treatment. \n\nQuality of Life may be something you write down yourself using study surveys. This information could also be collected through interviews. \n\nYou could ask the study team to list out specific things that you may want to consider when thinking about your own Quality of Life.
Related Terms: assessment of daily living, Patient Reported Outcome (PRO), Quality Adjusted Life Year (QALY)", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "questionnaire", "definition": "Glossary Definition: A list of questions for study participants to answer as part of the study.
Use in Context: A questionnaire is one way researchers can collect data.
More Info: A questionnaire could be used to find out if a person is eligible to take part in the study, to see how someone is feeling, or to measure the effects of an intervention. \n \n A questionnaire could be online or on paper.
Other Info to Think About When Joining a Study: A questionnaire or multiple questionnaires may need to be completed over the course of your participation in the study. This may be something you do by yourself or someone on the research team may do it with you. \n\nAs with any study procedure, you can ask what the purpose of the questionnaire is and if it has to be completed all at once.
Related Terms: survey, assessment, data", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "randomization", "definition": "Glossary Definition: A way to use chance to place study participants into different study treatment groups.
Use in Context: Study randomization is often done using a computer program to decide which group a participant is put into.
More Info: Randomization helps make sure the study groups are similar so they can be compared against each other at the end of the study. This is a way to avoid bias.\n \nEvery participant has a chance to be put into one of the study groups. No one can choose which group a participant is placed in, because it is done by a computer program.
Other Info to Think About When Joining a Study: Randomization is a common way that is used for participants to be assigned to different treatment groups. You might see the term \"randomization\" in the consent form or other study materials. Randomization means that you can't chooses which study treatment you will get. The study treatment is chosen by chance, like pulling names out of a hat.\n\nIf you are unsure, you should ask if randomization will happen in your study. You can also ask for more information about how the randomization will be completed.
Related Terms: random assignment, randomize, randomly assigned, blinded, study arm, bias", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "randomized controlled trial", "definition": "Glossary Definition: Research that uses chance to assign participants into study groups.
Use in Context: A randomized controlled trial is used to compare two or more groups.
More Info: In a randomized controlled trial (RCT), the researchers use a computer program to randomly assign which study treatment each participant receives. The computer program sometimes also randomly chooses the order of the study treatment, depending on the study. \n\nThe process of being randomized is sometimes described like \"flipping a coin\" or \"pulling name out of a hat.\" Randomization ensures that participants are put into different study groups fairly and without bias.
Other Info to Think About When Joining a Study: A randomized controlled trial is considered one of the best study designs to find out how well a study treatment works against one or more comparison groups.\n\nIf you are asked to participate in a randomized controlled trial, you may want to find out more about the different study groups, what the control group will be, and how participants will be randomized.
Related Terms: randomization, control, control group, research bias", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "rationale", "definition": "Glossary Definition: The reason why a study, or something in a study, is being done.
Use in Context: A well-planned research study includes a clear rationale for why the study is necessary and important.
More Info: The study rationale explains why the study is important and why the study question must be answered. It can include background information, study details, and relevant justifications. \n\nA study without a strong rationale might not be worth doing.
Other Info to Think About When Joining a Study: You might see the rationale for why a research study is done a certain way being described in the study protocol or consent form. \n\nIt can be helpful to learn more about the rationale of the study before agreeing to participate. Feel free to ask the study team questions about the background of the study and why it is being conducted.
Related Terms: reason, explanation, purpose", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "Real World Data (RWD)", "definition": "Glossary Definition: Information from many different sources used for health research purposes.
Use in Context: Real World Data comes from sources like medical records, insurance claims, pharmacies, and wearables such as smart phones or heart monitors set up for that purpose.
More Info: Real World Data are routinely collected and not specifically for research. \n\nSome studies use Real World Data such as health information in electronic medical records or insurance claims to learn more about side effects of medicines.
Other Info to Think About When Joining a Study: Some research studies use Real World Data. You may see the term \"Real World Data\" mentioned in a consent form or described in study results.\n\nIf a study uses Real World Data, you can also ask how these data are collected and protected.
Related Terms: data", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "Real World Evidence (RWE)", "definition": "Glossary Definition: Findings from analyzing Real World Data.
Use in Context: Real World Evidence comes from analyzing data that are collected from routine sources like medical records and health insurance claims.
More Info: Real World Evidence is often used to understand how medicines work in the real world, not in a clinical trial.\n\nReal World Evidence uses routinely collected Real World Data to answer a study question. For example, Real World Evidence can show whether a drug is effective for a given population or subgroup of people based on the number of doctor's visits they need after taking the drug.
Other Info to Think About When Joining a Study: You may see the term \"real world evidence\" in research results or study summaries, to describe how the data collected from \"real world\" sources prove a particular point or supports a certain conclusion. For example, data taken from insurance claims may provide evidence that one kind of treatment reduces hospital readmissions more than a different treatment.
Related Terms: Real World Data (RWD)", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "registry (study)", "definition": "Glossary Definition: An organized list of research information.
Use in Context: A clinical trial registry is a place to search for research studies.
More Info: Organized lists of information are valuable to research so many types of registries exist. \n\nA medicine or research registry has many different types of information for different uses. \n\nOne example is a clinical trial registry which can include information about research studies that are planned, enrolling, or completed. Some registries also include research results.\n\nA patient registry might be set up by a hospital or health system to allow their community members to express interest in volunteering for research so they can be contacted for participation when a new study is being offered. \n\nA disease registry, like a cancer registry, is a resource to hold specific information about people with a certain disease. Some disease registries allow those who are registered to indicate they are interested in research. Some disease registries just allow researchers to conduct research using the information in the registry.
Other Info to Think About When Joining a Study: Before you consent to join a research-related registry, you may want to find out more about what information about you will be collected, how your information will be used, and how your privacy will be protected.\n\nThere are also other types of registries too that don't include any of your personal information \n\nFor example, some studies that are funded with money from the USA government are required to post information about the study and its results on a research registry called www.clinicaltrials.gov. You may see a reference to www.clinicaltrials.gov in the consent form if the study team plans to release the overall study results there.
Related Terms: clinical trial registry, Patient registry, Participant registry, disease registry", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "reimburse", "definition": "Glossary Definition: Pay money back to participants for their out-of-pocket study costs.
Use in Context: Some studies will reimburse for parking, transportation, and other costs of participation.
More Info: Participants in research should find out whether the study team will reimburse any personal costs that might arise because of being in the study.
Other Info to Think About When Joining a Study: When the study team is providing information about what will happen if you participate in the study, they may say if they will reimburse you.\n\nYou can ask if the study team will reimburse you for out-of-pocket costs related to participating in the study. For example, if you have to pay for parking at the study site, you can ask if the study team pay you back for that cost. Find out what out-of-pocket costs will be reimbursed can be important when deciding whether or not to join a study.
Related Terms: repay, compensate, refund, remunerate, to pay someone back", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "relative risk", "definition": "Glossary Definition: The chance of a harmful event happening in one study group compared with another.
Use in Context: If a relative risk is 1 the chance of an adverse event happening is the same across study groups.
More Info: For example, if a study finds that 20% of smokers develop lung cancer and 5% of non-smokers develop lung cancer, then we can calculate the relative risk of lung cancer in smokers versus non-smokers as:\n\nRelative Risk = 20%/ 5% = 4\n\nThus, in this example, smokers are 4 times more likely to develop lung cancer than non-smokers.
Other Info to Think About When Joining a Study: You may see the term \"relative risk\" used in publications about the data and statistics of a research study. The results section of a publication will report the findings which will often include information about how many participants in one arm experienced a health event or problem versus particpants in a comparison group. In general, however, \"relative risk\" is a technical math term and will not usually be used in materials designed especially for patients and participants. \n\nIf you see this word in a study document for a study you are considering, enrolled in, or completed, you can ask the researcher or study team any questions you might have.
Related Terms: risk, absolute risk", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "repository (research)", "definition": "Glossary Definition: A collection of participant data and samples stored for future research.
Use in Context: A research repository is a safe way for data and samples to be stored for future research studies.
More Info: A research repository stores both data and samples that have been collected with the purpose of being used in the future. Like a database, a research repository has strict rules for protecting and accessing the data and samples that are stored in it.
Other Info to Think About When Joining a Study: A research study may collect data and/or samples to store in a repository for future use. \n\nIf data or samples will be stored for future use, the informed consent form will ask for your consent. You might want to ask the study team about your privacy is maintained, how the data and samples are protected, who can use them in the future, and for what purpose. It is unlikely you will be contacted in the future how they were used.
Related Terms: database, data bank, biobank, collection of information, sample repository", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "results (study)", "definition": "Glossary Definition: Findings from the study.
Use in Context: The final results of the study are only available after all data are analyzed.
More Info: Study results are based on the data that were collected, analyzed, and interpreted in the study.\n\nAn example of a study result is learning that yoga can decrease low back pain.\n\nStudy participants can ask for the research results. Results can be shared in several ways: In a journal article, in a study summary for participants, or in other types of communication.
Other Info to Think About When Joining a Study: The term \"results\" may appear in publications after the trial is completed and the study team has used the data collected to come out with their findings.\n\nYou can ask if and how the study team will share results with you when the study ends. If they will share, you can ask if you will get your individual information or if it will be a summary of the overall findings. \n\nYou can also ask if this information will be shared with your regular doctor. \n\nIn general, because research studies can take a long time to complete, it may take a while for study results to be finalized and shared. Feel free to ask about when the study team thinks the final study results will be ready.
Related Terms: outcome, conclusions, findings, data", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "retrospective study", "definition": "Glossary Definition: Research that uses already existing data.
Use in Context: Retrospective studies use data that were collected in the past.
More Info: A retrospective study looks at the historical data of participants. \n\nFor example, a study of people with cancer might use existing medical records to learn more about possible causes and exposures. \n\nA retrospective study may also use stored specimens or tissue samples that were collected in the past.
Other Info to Think About When Joining a Study: You may see the term \"retrospective study\" if you are asked to give informed consent for the study team will look through your past medical records. \n\nYou may want to ask them about what information they will be obtaining about you and why, and how your privacy will be protected.
Related Terms: backward-looking study", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "risk-benefit ratio", "definition": "Glossary Definition: A comparison of the possible bad and potential good things that could happen if a participant joins a research study.
Use in Context: It is important to discuss and understand the risk-benefit ratio of a research study before agreeing to participate.
More Info: People look at the risk-benefit ratio in different ways. Some may be less willing to accept a risk. Others may decide that the possible benefits are greater than the risks. \n\nFeelings about a study's risk-benefit ratio can differ from person-to-person based on their own experiences, life situation, pre-existing conditions, and concerns.\n\nIt can be helpful for someone who is thinking about joining a study to discuss the risk-benefit ratio with the study team, trusted friends, and family members.
Other Info to Think About When Joining a Study: The term \"risk-benefit ratio\" is sometimes part of consent forms and consent discussions. It is a way to try to describe how the risks and benefits of the study compare. Thinking about the risk-benefit ratio can help you decide whether the study has enough potential benefits to outweigh the risks of being in the study.\n\nYou can talk to the study team and other trusted people in your life to work through whether or not to join the study, based on the information that is known about the study treatment.
Related Terms: risk benefit assessment, risk benefit profile, therapeutic index", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "risks of a research study", "definition": "Glossary Definition: The possible harms of being in a research study.
Use in Context: Learning about the risks of a research study can help a person decide whether or not to join.
More Info: The risks of a research study depend on the study and study procedures. Both the serious and more common risks are listed in the consent form.\n\nA potential participant should talk about the study risks with the study team and others before deciding whether to participate.\n\nSome risks may not be known when a person signs the consent form. The study team will keep the participant updated if important new risks are identified.\n\nSometimes there are also risks to other people to consider so these should also be reviewed and understood. For example, if a study shares genetic information that could impact other family members.\n\nThe chance of a risk happening is sometimes called \"absolute risk.\" The absolute risk is a number that shows how many people might have a specific event happen. In general, \"\"absolute risk\"\" is a technical math term and will not usually be used in materials designed especially for patients and participants. A related word is \"relative risk.\"
Other Info to Think About When Joining a Study: You might see the term “risks of the research study” when the study team gives you a consent form to review and tells you about the study. They will explain the risks that you may experience if you join the study.\n\nAsk questions about any of the risks you don’t understand before agreeing to take part in a study. Risk may involve harm to the body but it may also include things like risk of stigma if sensitive information that is collected during the study is accidentally shared or accessed by others without permission. \n\nTo learn more about the risks of a research study, you can ask things like:\n– How much do the researchers know about the risks of the study treatment – especially if it is new or experimental?\n– Does the study treatment have FDA approval or oversight?\n–What are the short- or long-term risks, discomforts, or unpleasant side effects? How likely are they to occur, and are any of them severe?\n-What are the researchers doing to decrease risks, discomforts, or unpleasant side effects?\n– Is there anything a participant could do to minimize their risks during the study?
Related Terms: disadvantages, cons, harms, negative impacts, downsides, adverse effects, side effects", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "sample size", "definition": "Glossary Definition: The number of participants in a study or study group.
Use in Context: A study's sample size should ensure there will be enough participants enrolled to answer the study question.
More Info: The sample size must be able to be reached so that the study question can be answered. A statistician helps to calculate the sample size to ensure it is large enough to answer the study question. Sample size is often reported as n = . For example, a study with 100 participants would have its sample size described at n=100.
Other Info to Think About When Joining a Study: The study team may tell you about the sample size of the study. You may also read about the sample size of the study in the consent form. \n\nYou may want to ask how many people have already enrolled in the study before you agree to join.
Related Terms: population size, target population size, statistician, study population", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "schedule of assessments", "definition": "Glossary Definition: A chart that lists the study activities and when they will happen during a study.
Use in Context: A schedule of assessments can help participants plan for their study visits.
More Info: A schedule of assessments is like a calendar or timeline. The schedule of assessments shows a detailed overview of all the study activities that involve participants and when they will happen. \n\nStudy activities can include blood draws, exams, questionnaires, and other medical tests.
Other Info to Think About When Joining a Study: If you are thinking about joining a study,you may see a \"schedule of assessments\" in the consent form. Someone from the study team may also provide more details. The study team will want to know if you can make it to all the study visits and if you understand all the activities youhave to do while in the study.\n\nIf the schedule seems confusing, it can be helpful to ask the study team to help answer any questions. Additionally, putting all the activities that you need to do to participate in a study (like study visits or surveys) onto your personal calendar can help you plan.
Related Terms: schedule of activities, calendar, timetable, study schema", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "screening", "definition": "Glossary Definition: Tests and questions to find out if a person can join a study.
Use in Context: Screening is done before a person joins the study to see if they meet the study requirements.
More Info: Researchers review inclusion and exclusion criteria as part of the screening process to make sure the participants are eligible to join a study. \n\nScreening for research often includes an interview, reviewing a person's medical history, a physical examination, and laboratory tests to learn about the potential participant's health.\n\nThere is also screening that occurs outside of research like having a yearly breast cancer screening and colonoscopies.
Other Info to Think About When Joining a Study: \"Screening\" is a word that is commonly seen in consent forms. There may be some screening questions and medical tests you will complete to see if you meet the study eligibility criteria. \n\nYou can ask what kind of screening the study team will have to do before you can join the study.
Related Terms: eligibility criteria, inclusion criteria, exclusion criteria, assessment, study screening, screen failure, medical screening, health screening", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "secondary endpoint", "definition": "Glossary Definition: A measure used to answer other important questions in the study that are not the main research question.
Use in Context: A secondary endpoint can provide more information about the effect of the study treatment.
More Info: A secondary endpoint can help guide researchers to answer other questions related to the study treatment.\n\nA secondary endpoint can also be exploratory, for example, looking for information that might be useful for a future study.
Other Info to Think About When Joining a Study: You may see the concept of the \"secondary endpoint\" written about in the consent form or hear about it from the study team. Additionally, if you visit www.clinicaltrials.gov you may see references to primary and secondary endpoints. \n\nSecondary endpoints are other aspects that the study is designed to measure. If you are unsure of what it means, please ask the study team for clarification. After the study is done and publications are released, you may also read about different types of endpoints. \n\nSome studies might only have a primary endpoint and you will not see anything about a secondary endpoint. This is normal.
Related Terms: Secondary aim", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "sensitivity (medical test)", "definition": "Glossary Definition: How well a medical test can accurately identify people who have a disease or trait.
Use in Context: The sensitivity of a test refers to how well it detects a disease when the person actually has the disease.
More Info: A medical test that has high sensitivity means it is very good at detecting a disease. If it has low sensitivity it means it is not very good at detecting a disease.\n\nGreater sensitivity leads to a more precise diagnosis.\n\nFor example, a COVID test with high sensitivity means the test is able to detect the infection very well.
Other Info to Think About When Joining a Study: In clinical research and medicine, the term \"sensitivity\" is used to describe how well a medical test works to find cases of an illness or condition. A test that works well to identify people with an illness or condition is said to be very sensitive.\n\nYou can always ask the study team if you have any questions about the way the term \"sensitivity\" is being used in the study information.
Related Terms: specificity, true positive, false negative, false positive", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "sequential", "definition": "Glossary Definition: Happening in a specific order.
Use in Context: Events that occur one after the other are sequential.
More Info: Research studies have steps and procedures that must be followed in a specific, sequential order.
Other Info to Think About When Joining a Study: The word \"sequential\" can be used to describe the order that study activities are conducted.\n\nIf you have any questions about sequential study activities you can ask the study team to better understand.
Related Terms: one after the other, consecutive, successive", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "Serious Adverse Event (SAE)", "definition": "Glossary Definition: A health issue that happens during a study, and can lead to hospital care, lasting medical problems, life-threatening conditions, or death.
Use in Context: A Serious Adverse Event in a study is usually something very concerning that was not expected.
More Info: Serious Adverse Events (SAEs) are health problems that may result in death, an inpatient hospital stay or longer hospitalization, a life-threatening event, a disability happening, or a birth defect in a baby. An SAE may or may not be related to the study treatment.\n\nThere are sometimes new SAEs that could occur that even the study team does not know about yet. The study staff will collect this information from participants to keep track and figure out if it needs to be included in future study informed consent forms.\n\nIn contrast to a Serious Adverse Event, an \"adverse reaction\" is determined to be related to a study treatment.
Other Info to Think About When Joining a Study: You may learn about potential Serious Adverse Events during the consent process if they are known. You could also learn about them during or after the research study if they are discovered later. \n\nWhen you are in a research study, it is important to contact the study team if you have any new health problems whether or not they are serious. This is important for your safety and allows the study team to also monitor the other study participants. You can ask about adverse events, and Serious Adverse Events, before joining or during a study.
Related Terms: adverse event, adverse reaction, side effect, dangerous event, life-threatening event, unanticipated problem", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "side effect", "definition": "Glossary Definition: A health change that is not the intended effect of the treatment and usually considered a problem.
Use in Context: A side effect is not the main effect of the treatment.
More Info: Side effects are things that are known to be a possible effect of a treatment. \n\nFor example, a side effect of taking aspirin is excess bleeding. Often times, a side effect is unwanted, but in some cases a side effect could be considereda good thing.
Other Info to Think About When Joining a Study: Known side effects are listed in the consent form. A member of the study team may also tell you about possible side effects. \n\nAsk any questions about the side effects and how likely they are. You can also ask what you should do if you think you are getting a side effect and who you should tell.
Related Terms: health effect, adverse reaction", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "single-blind study", "definition": "Glossary Definition: A study that is set up so that the study treatment each participant receives is not known by the participants but is known by the researchers.
Use in Context: A participant in a single-blind study will not know which study group they are in, but the study doctor will.
More Info: Some studies are single-blind because participants knowing which treatment they are getting can affect the results of the study, through a concept called bias. \n\nBias can occur when participants know which study group they are in.\n \nParticipants can ask to find out which study treatment they received after the study ends.
Other Info to Think About When Joining a Study: The term \"single-blind study\" refers to how a study was designed. This means that the researcher will know what study treatment each participant received but the participant won't.\n\nYou can always ask the researchers why the study is done as a single-blind study. You could also ask if and when you will find out what study treatment you were given.
Related Terms: masked/masking, bias, double-blind study, randomization", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "specificity (medical test)", "definition": "Glossary Definition: How well a medical test can accurately identify people who do not have a disease or trait.
Use in Context: The specificity of a test refers to how well it identifies when an illness is not present.
More Info: A medical test that has high specificity means it is very good at detecting if a person does not have a disease. If it has low specificity it identifies people as having the disease when they actually do not.\n\nGreater specificity allows people who do not have a condition to be identified and screened out so resources can be put toward caring for people who do have the condition.\n\nFor example, a COVID test with high specificity means the test correctly identifies when people don't have the infection.
Other Info to Think About When Joining a Study: In clinical research and medicine, the term \"specificity\" is used to describe how well a medical test works to show people who do not have an illness or condition. A test that works well to rule out people who do not have an illness or condition is said to be very specific.\n\nYou can always ask the study team if you have any questions about the way the term \"specificity\" is being used in the study information.
Related Terms: sensitivity, false positive, true negative, false negative.", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "sponsor", "definition": "Glossary Definition: The group that is in charge of, or pays for, a research study.
Use in Context: The study sponsor is often the drug or device company that makes and studies the product.
More Info: A sponsor in clinical research is a person or group that is responsible for the design, conduct, and oversight of a research study. \n\nThere can be different types of sponsors. A sponsor can be a drug or device company, governmental agency, academic institution, person, group, or private organization.\n\nA \"Funding Sponsor\" is a person or group that pays for the research study but does not conduct the study. \n\nA \"Regulatory Sponsor\" is a person or group that has to report to regulators (like the FDA) about the specific drug or device being tested in a study and may be conducting the study. \n\nA \"Sponsor Investigator\" is a person who designs, conducts, reports the study. The person is generally an academic researcher who is responsible to the regulatory agencies.
Other Info to Think About When Joining a Study: The term \"sponsor\" may be in the consent form or mentioned when the study team discusses who is running the study. The sponsor is the person or group who is responsible for how the study is conducted.\n\nThe sponsor could be a drug company or a researcher. You can ask the study team who the sponsor is and if the sponsor of the study is the group that made the investigational product that will be used in the study. You may also ask if the investigators have any financial relationship with the sponsor outside of the study or have been paid by the sponsor for other services besides the study. In general, any financial relationships between researchers and companies should be described in the consent form and made clear to the study participants.
Related Terms: funder, sponsor investigator, pharmaceutical company", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "standard of care", "definition": "Glossary Definition: The usual treatment given to patients for an illness.
Use in Context: Some research studies compare the study treatment to the standard of care for a given condition.
More Info: Depending on the health issue, there could be many types of standard of care that are used by health care providers. \n \n For example, there are different medications used to treat high blood pressure.
Other Info to Think About When Joining a Study: The term \"standard of care\" may come up in the consent form where it may explain that the study is comparing a new study intervention with the existing standard of care. \n\nIf you have any questions about the study treatment you are being given or the standard of care, you should feel free to ask the study team.
Related Terms: standard therapy, standard treatment, usual care", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "statistically significant", "definition": "Glossary Definition: Results that are very unlikely to have occurred by chance.
Use in Context: Study results are analyzed to find out if they are statistically significant.
More Info: When a result is found to be statistically significant, it means that a study result probably did not happen by chance. It does not necessarily mean that the finding is clinically important.\n\nFor example - a study could show a new medicine lowers blood pressure (statistically significant) but the side effects are too great to make it a useful treatment (not clinically meaningful).\n\nSimilarly, a statistically significant result in a study may be different than a person's lived experience. For example, a study may show a statistically significant overall decrease in depression scores based on data collected from all participants in the study but an individual participant may still have feelings of depression.
Other Info to Think About When Joining a Study: You might see the term \"statistically significant\" used in a research article when the authors discuss the results and statistics.\n\nThe results section may also provide more information about what it means for the data to be statistically significant.\n\nYou may also see this term in Plain Language Summaries of a study's results.
Related Terms: clinically meaningful", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "study design", "definition": "Glossary Definition: The way a study is set up to answer the study question.
Use in Context: It is very important for researchers to use a study design that will answer the study questions.
More Info: The study design determines how participants will be recruited and enrolled, whether participants will be randomized, what kinds of data will be collected, and how the data will be analyzed. \n \n There are many different types of study design.
Other Info to Think About When Joining a Study: Researchers are careful about using the right study design to answer a specific question. You might learn about the study design from the study team or the consent form.\n\nYou may be able to find more information about the study you are thinking about joining at www.clinicaltrials.gov. There may be a section called \"How is the study designed?\"\n\n If there is anything you don't understand about the study design you should ask the study team.
Related Terms: design, single-blind study, double-blind study, randomization, observational study, clinical trial", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "study feasibility", "definition": "Glossary Definition: How likely it is that a study can be completed.
Use in Context: Research planning involves assessing study feasibility before starting to enroll participants.
More Info: In research, study feasibility means that: \n-the study procedures are all necessary and not overly burdensome, \n-the number of proposed participants should be possible to enroll in the locations chosen, and \n-the study will have enough participants to answer the study question.\n\nResearch teams should spend time looking at all the parts of the study to make sure they are \"doable.\"\n\nIf a study has low feasibility, it is less likely to generate enough data to answer the study questions.\n\nLow study feasibility is not a failure of participants. Low feasibility reflects a failure of the study planning process.
Other Info to Think About When Joining a Study: Study feasibility is about checking whether a study can be done as proposed. This is not the same as a \"feasibilty study\" which is a study that is done to find out if an intervention is possible\n\nFeel free to ask questions about how the research team planned the study and how they are ensuring the study will be able to answer the planned research questions.
Related Terms: feasible, achievable, possible", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "study intervention", "definition": "Glossary Definition: A treatment given to the participants in a study
Use in Context: If a research study includes an intervention, it means participants will test something to see its effects.
More Info: A study intervention can be a treatment of a disease or condition or include ways to change behavior, attitudes, and maintain or improve health. For example, a study might test whether giving participants a step counter leads to weight loss.
Other Info to Think About When Joining a Study: The study team or the consent form might mention the term \"study intervention.\" \n\nWhen you are learning about a clinical trial you should be given information about the study intervention which could be something you take (like a medicine) or do (like yoga) during the study to see if it has any effect.\n\nYou can always ask for more information about the study intervention the research is testing. You may also want to ask about the background of the study intervention and any prior research that was done that led to the researchers starting a new study.
Related Terms: study treatment, investigational product, investigational drug, investigational device", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "study life cycle", "definition": "Glossary Definition: The steps of a research study from beginning to end.
Use in Context: The study life cycle refers to all the steps that go into developing and running a research study.
More Info: The steps of the study life cycle include: developing the study protocol and procedures, participant recruitment, screening, and consent, ongoing study procedures, follow-up, end of study procedures, and study close-out.
Other Info to Think About When Joining a Study: You might hear about the \"study life cycle\" when discussing the research process with a study team member. The term \"study life cycle\" is often used to describe the overall plan for the study. \n\nAs a participant, you are part of the study life cycle. If you join a study, your data are important for the study to be completed. Please feel free to ask the study team any questions you might have about the study.
Related Terms: clinical trial life cycle", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "study participant", "definition": "Glossary Definition: A person who joins a research study.
Use in Context: A study participant volunteers to join research.
More Info: If you are a study participant in a research study, your data will help answer the research question.
Other Info to Think About When Joining a Study: A person who joins a study is called a \"study participant.\" The consent form will describe the study, and what study participants will be asked to do.\n\nBefore you join a study you should understand what participating in the research will involve. Please ask any questions you have about being in the study.
Related Terms: participant, subject, research participant, research subject, study subject, healthy volunteer, data, clinical trial, observational study", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "study population", "definition": "Glossary Definition: All the participants in a study.
Use in Context: A participant is a member of the study population.
More Info: The study population is described in the consent form and the protocol.
Other Info to Think About When Joining a Study: You may hear the term \"study population\" from the study team or read it in the consent form. \n\nThere may be information about the number of individuals who are in the study population or what similarities the study population shares. The study population is also discussed in results and journal publications that describe what the study looked at. \n\nIf you have any questions about the study population, you should feel free to ask.
Related Terms: participant population, participant", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "study statistician", "definition": "Glossary Definition: A person who uses math to help design a study and interpret the data.
Use in Context: The study statistician reviews and analyzes the data, and reports the results back to the study team.
More Info: Statisticians design studies to decrease bias and make the results as accurate as possible. \n\nBefore a study begins, a statistician can help the study team calculate how many participants should be enrolled so the research question can be answered.\n \nA study statistician can analyze study data and present the results using numbers, charts, and graphs. Study statisticians are trained to figure out whether a result happened by chance or not.
Other Info to Think About When Joining a Study: You may hear that a study statistician is part of the research study and will work with the data collected during the study. \n\nIt is always fine to ask about who is working on the study and how your data are protected.
Related Terms: data, results, analysis", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "substudy", "definition": "Glossary Definition: A study with a smaller group of participants already enrolled in the main study.
Use in Context: A substudy is a study to answer questions related to the main study.
More Info: A substudy asks a separate research question from the main study. It adds to the main study's objectives and uses all or a subset of participants or samples from the main study. \n\nA substudy is sometimes designed at the beginning of the main study. It is also possible that a substudy could happen later after some data have been analyzed.
Other Info to Think About When Joining a Study: When you enroll in a study, there may be additional substudies you can choose to enroll in. For some substudies, you may need to go in for extra study visits or do some extra tests. The study team may want to collect more information from you to answer additional questions that the first study you enrolled in is not looking at. You will be consented for additional substudies.\n\nYou may want to ask if there are substudies in the study you join. Additionally, you can ask if you have to join any substudies and ask for more information about how the substudy is different from the first study you are enrolling in. It could also be helpful to ask if you have to do additional tests or go in for more study visits if you join a substudy. This could help you decide if you have the time to join.
Related Terms: basket trial, umbrella trial, cohort trial, master protocol", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "superiority trial", "definition": "Glossary Definition: A study to test if a study treatment works better than another treatment for the same condition.
Use in Context: One example of a superiority trial would be to test whether a new medicine is better for treating asthma than an existing one.
More Info: Superiority trials are done to find treatment options that work better than those that are already approved for a specific disease or condition.
Other Info to Think About When Joining a Study: If you are thinking about joining a superiority trial, it will be a study that looks at whether one treatment works better than another. You should ask any questions you have about the treatments being tested before you consent.
Related Terms: superiority study, confirmatory trial, equivalence trial, control; non-inferiority trial", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "synergistic effect", "definition": "Glossary Definition: When two or more things combined have a greater effect than when their individual effects are added together.
Use in Context: A synergistic effect means that the combined effects of two products is stronger than what was expected.
More Info: A combination of medications is synergistic if they work better together than if the individual component effects were added together.\n\nFor example, Drug A decreases cancer growth by 10% and Drug B by 20% when each are used alone. One would predict that used together, cancer growth might be decreased by 30% (additive). If Drug (A + B) used together decreases cancer growth by 35% or more, the effect is considered \"synergistic.\"
Other Info to Think About When Joining a Study: The term \"synergistic\" is sometimes used to describe how two things that are used together actually have a greater effect than just one of them used alone. It's like the two things work in synergy with each other to have more of an effect.\n\nYou can ask the study team if you have any questions about the word \"synergistic\" is being used in study documents or conversations you are having.
Related Terms: additive effect", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "tolerability", "definition": "Glossary Definition: How much a participant or group of participants can accept a study treatment's unwanted effects so they can keep taking it.
Use in Context: Many drug studies look at the tolerability of the study treatment.
More Info: Tolerability is looked at to find out how easy it is for treatment is to be delivered. For example, researchers can learn whether a pill is too big to be swallowed. \n\nTolerability also tells us about the problems a treatment may cause, and how severe those problems are. If a person has to stop taking a study treatment because of related problems, then tolerability is low.
Other Info to Think About When Joining a Study: Someone from the study team may ask you about the tolerability of the study treatment during follow-up visits or on questionnaires. \n\nYou may want to clarify what they mean by \"tolerability.\" For some people, nausea might be tolerable while for others its not, so you should let the study staff know what you are feeling while taking the study treatment. You could also ask the study staff what you should do if you are having trouble taking the study treatment, or wish to stop taking it.
Related Terms: safety, side effects, adverse effects", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "treatment effect", "definition": "Glossary Definition: How much a study treatment changes a condition, symptom, or function.
Use in Context: Some drug studies collect information from participants to measure the treatment effect.
More Info: The treatment effect shows how much the study intervention changes what the study is measuring when compared to not getting the study treatment or getting something different.\n\nFor example, a study might measure whether, and by how much, blood pressure is lowered when participants take a new medicine.
Other Info to Think About When Joining a Study: For studies that are looking at what a study treatment does, researchers often measure the treatment effect which is connected to the main purpose of the study and the types of data being collected.\n\nYou should feel free to ask if you have any questions about how the treatment effect is being measured in the study.
Related Terms: study effect, intervention effect, efficacy", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "umbrella trial", "definition": "Glossary Definition: A research study that tests and compares two or more study treatments for one disease or condition.
Use in Context: In an umbrella trial, multiple study treatments are compared in a single disease or condition.
More Info: An umbrella trial is a type of randomized controlled trial (RCT). It is also a type of Master Protocol Study.\n\nAn umbrella trial uses a Master Protocol to compare different study treatments in the same way, using the same design, in a certain disease or condition.\n\nThe difference between a basket and an umbrella trial is that a basket trial uses one drug to test against multiple diseases while an umbrella trial studies different drugs in a single disease.
Other Info to Think About When Joining a Study: You may hear about umbrella trials when you are learning about different types of study designs.\n\nIf you are unsure about what it means for a research study to be an umbrella trial, you should ask a member of the study team to clarify any of your questions.
Related Terms: adaptive trial, master protocol, basket trial, platform trial", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "validate", "definition": "Glossary Definition: To confirm that a process or test works as planned, or results are true.
Use in Context: In research, to validate something is to make sure that it works as expected.
More Info: Anything used to measure research data should be valid and precise. \n\nMedical tests and surveys are validated through a process to make sure that they are measure what they intend to measure. \n\nResults can also be validated, which means they go through a process to see if the findings are true.
Other Info to Think About When Joining a Study: You may hear study teams talking about validating the data and information they collect. This could include checking measurements they take from you and comparing them to expected outcomes to see if the equipment is working correctly.
Related Terms: confirm, certify, substantiate, authenticate, replicate", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "voluntary participation", "definition": "Glossary Definition: Choosing to participate in research without feeling pressured.
Use in Context: Joining a research study is voluntary.
More Info: Research participation must be voluntary so no one feels pressured to join a study or that they must stay in a study. A participant can withdraw at any time.
Other Info to Think About When Joining a Study: Before you join a study, the study team will want to be sure you know that participation is voluntary. The study team may tell you this verbally and it may also be in the consent form they give you to review. You should not feel forced into joining the study.
Related Terms: free will, freely chosen, independent choice, autonomy", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "volunteer (to)", "definition": "Glossary Definition: To choose to join a study.
Use in Context: A participant is someone who chooses to volunteer to join a research study.
More Info: Participants might be healthy volunteers or patients, but everyone is free to make their own decision about participation. Even if they volunteer, they are free to leave the study at any time.
Other Info to Think About When Joining a Study: Your doctor may ask if you want to volunteer for a study during a visit. You may also see this when reviewing a consent form, letting you know you do not have to join this study if you do not want to.\n\nYou may want to ask your doctor about how to volunteer for a study. If you are considering volunteering for a study, you can ask about what the study procedures are and what will happen to participants in the study.\n\nYou should not feel pressured to join a research study.
Related Terms: agree, voluntary participation", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "wash-out", "definition": "Glossary Definition: A time before starting a study treatment when a person stops taking other medicines.
Use in Context: A wash-out period removes certain current medications in the body so that they don't interfere with the study treatment.
More Info: In research, a wash-out is a time when medication is stopped so it can be cleared from a person's body before the study intervention is given. \n\nA wash-out period makes sure that the medication and the study intervention don't interact and increases the study's safety. it also helps to show that effects observed in a study are from the study medication and not previous, unrelated medications. \n\nNot every study has a wash-out period. When a study has a wash-out period it is a pre-set time that depends on the protocol and what medicines the person is taking.
Other Info to Think About When Joining a Study: Whether a research study requires a wash-out will be described in the consent form and the study team will discuss this with you.\n\nIt will be important for you to ask if you will have to go through a wash-out period and how that might affect you if you have been taking medications regularly before starting the trial. It could be important to discuss this with your regular doctor to let them know you will be stopping your routine medications.
Related Terms: discontinue, remove", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "withdraw", "definition": "Glossary Definition: To stop being a participant in a study.
Use in Context: If a participant decides to withdraw from a study, they should discuss with the study team how to do so safely.
More Info: A participant can decide to withdraw from a study or an investigator can decide to withdraw the participant, usually for safety reasons. Reasons to withdraw from the study should be discussed.
Other Info to Think About When Joining a Study: There may be information about how to withdraw from the study that you hear about when going through the consent process. \n\nIf you are thinking about stopping your participation in the study, ask the study team how to withdraw safely. You may also ask what may cause an investigator to withdraw you from the study. \n\nIf there are incentives for being part of the study, you may not get all of them if you leave the study early.
Related Terms: discontinue", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "X-ray", "definition": "Glossary Definition: A way of taking pictures of the inside of a person's body using X-ray radiation.
Use in Context: An X-ray uses radiation to create images of bones, organs, and tissues.
More Info: An X-ray creates pictures of the inside of a person's body to view bone fractures and dislocations, certain tumors and other growths, pneumonias, fluid collections, and other problems.
Other Info to Think About When Joining a Study: You may hear the term \"X-ray\" when the study team is talking about what happens in the esearch study. The study team could also say they are looking at your X-rays to get data for the study.\n\n If you do need an X-ray for the study, you can get more information about what kind of X-ray the study team will need and what part of the body they will scan. You can also find out if the X-ray results from the research will be put into your medical records for your regular doctor to see.
Related Terms: imaging study, CT scan, MRI", "sources": [ "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx" ], "file": "2024-09-11-MRCT-Clinical-Research-Glossary-Excel-Download-alphabetized-1.xlsx", "sheet": "Clinical Research Glossary 0324", "type": "excel" }, { "term": "A&WC", "definition": "Long Name/Description: adequate and well-controlled", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AABB", "definition": "Long Name/Description: American Association of Blood Banks", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AADA", "definition": "Long Name/Description: abbreviated antibiotic drug application (FDA) (used primarily for generics)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AAMC", "definition": "Long Name/Description: Association of American Medical Colleges", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AAPS", "definition": "Long Name/Description: American Association of Pharmaceutical Scientists", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AAS", "definition": "Long Name/Description: American Association for the Advancement of Science", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ABPI", "definition": "Long Name/Description: Association of the British Pharmaceutical Industry", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ACCP", "definition": "Long Name/Description: American College of Clinical Pharmacology", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ACDM", "definition": "Long Name/Description: Association for Clinical Data Management (UK)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ACE", "definition": "Long Name/Description: angiotensin-converting enzyme", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ACIL", "definition": "Long Name/Description: American Council of Independent Laboratories. A national trade association representing independent, commercial scientific, and engineering firms.", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ACPU", "definition": "Long Name/Description: Association of Clinical Pharmacology units", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ACRA", "definition": "Long Name/Description: Associate Commissioner for Regulatory Affairs (FDA)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ACRP", "definition": "Long Name/Description: Association of Clinical Research Professionals (formerly Associates in Clinical Pharmacology, ACP)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ACT", "definition": "Long Name/Description: Applied Clinical Trials magazine", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ACTG", "definition": "Long Name/Description: AIDS Clinical Trials Group (NIAID)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ACTU", "definition": "Long Name/Description: AIDS Clinical Trials Unit (NIH)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ADaM", "definition": "Long Name/Description: Analysis Data Model (CDISC)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ADE", "definition": "Long Name/Description: adverse drug event; adverse drug effect", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ADME", "definition": "Long Name/Description: absorption, distribution, metabolism, and excretion (used to describe pharmacokinetic processes)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ADR", "definition": "Long Name/Description: adverse drug reaction", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AE", "definition": "Long Name/Description: adverse event", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AEGIS", "definition": "Long Name/Description: ADROIT Electronically Generated Information Service", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AERS", "definition": "Long Name/Description: adverse event reporting system (FDA)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AFMR", "definition": "Long Name/Description: American Federation for Medical Research (formerly the American Federation for Clinical Research, AFCR)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AHA", "definition": "Long Name/Description: American Heart Association", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AHCPR", "definition": "Long Name/Description: Agency for Healthcare Policy Research (NIH)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AHIC", "definition": "Long Name/Description: American Health Information Community", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AI", "definition": "Long Name/Description: Artificial Intelligence", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AICRC", "definition": "Long Name/Description: Association of Independent Clinical Research Contractors (UK)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AIDS", "definition": "Long Name/Description: acquired immune deficiency syndrome, acquired immunodeficiency syndrome", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ALCOA", "definition": "Long Name/Description: attributable, legible, contemporaneous, original, accurate", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "am", "definition": "Long Name/Description: ante meridian, morning (12:00 midnight thru 11:59:59)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AMA", "definition": "Long Name/Description: American Medical Association", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AMC", "definition": "Long Name/Description: antibody-mediated cytotoxicity", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "AmFAR", "definition": "Long Name/Description: American Foundation for AIDS Research", "sources": [ 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"Long Name/Description: Drug Enforcement Administration (US)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "DEN", "definition": "Long Name/Description: drug experience network", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "DES", "definition": "Long Name/Description: data encryption standard", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "DESI", "definition": "Long Name/Description: Drug Efficacy Study Implementation notice (FDA) (to evaluate drugs in use before 1962)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "DGPharMed", "definition": "Long Name/Description: Deutsche Gesellschaft für Pharmazeutische 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"Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "DICOM", "definition": "Long Name/Description: Digital Imaging and Communications in Medicine", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "DIMs", "definition": "Long Name/Description: domain information models", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "DITA", "definition": "Long Name/Description: Darwin Information Typing Architecture", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "DLT", "definition": "Long Name/Description: dose-limiting toxicity", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "DMB", "definition": "Long Name/Description: Data Management Biomedical (France)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "DNA", "definition": "Long Name/Description: deoxyribonucleic acid", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "DPC-PTR", "definition": "Long Name/Description: Drug Price Competition and Patent Term Restoration Act of 1984 (also Waxman-Hatch or Hatch-Waxman bill)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "DRE", "definition": "Long Name/Description: disease-related event", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "DSI", "definition": "Long Name/Description: Division of Scientific 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"E3C", "definition": "Long Name/Description: European CDISC coordinating committee", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "EAB", "definition": "Long Name/Description: ethical advisory board", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "EC", "definition": "Long Name/Description: European Commission; European Community (in documents older than the mid-1980s); ethics committee", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "EC50", "definition": "Long Name/Description: half maximal effective concentration", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ECG", "definition": "Long Name/Description: 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"Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "eCRF", "definition": "Long Name/Description: electronic case report form", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "ECRIN", "definition": "Long Name/Description: European Clinical Research Infrastructures Network", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "eCTD", "definition": "Long Name/Description: electronic common technical document", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "EDC", "definition": "Long Name/Description: electronic data capture/collection", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "EDI", "definition": "Long Name/Description: electronic data interchange", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "eDMS", "definition": "Long Name/Description: electronic data management system", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "EDR", "definition": "Long Name/Description: electronic document room", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "eDT", "definition": "Long Name/Description: electronic data transfer", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "EEC", "definition": "Long Name/Description: European Economic Community, now EU; some regulatory documents still have EEC document 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"definition": "Long Name/Description: European Medical Writers Association", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "EOP2A", "definition": "Long Name/Description: end-of-phase 2A", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "EORTC", "definition": "Long Name/Description: European Organization for Research and Treatment of Cancer", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "EP", "definition": "Long Name/Description: European Parliament", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "EPAR", "definition": "Long Name/Description: European Public Assessment Report", "sources": [ "Abbreviations.xlsx" ], 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See also IND.", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "TK", "definition": "Long Name/Description: toxicokinetics", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "Tmax", "definition": "Long Name/Description: time to maximum plasma concentration; time to maximum effect (drugs)", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "TMO", "definition": "Long Name/Description: trial management organization", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "UAT", "definition": "Long Name/Description: User Acceptance Testing", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "UCUM", "definition": "Long Name/Description: Unified Code for Units of Measure", "sources": [ "Abbreviations.xlsx" ], "file": "Abbreviations.xlsx", "sheet": "Acronyms,Abbreviations,Initials", "type": "excel" }, { "term": "UMT", "definition": "Long Name/Description: universal mean time (also known as Greenwich Mean Time and Universal Time). 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Premarket Notification (PMN) required for certain medical devices. See http://www.fda.gov/cdrh/510khome.html.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "abbreviation", "definition": "CDISC Definition: A set of letters that are drawn from a word or from a sequence of words and that are used for brevity in place of the full word or phrase. NOTE: An abbreviation is NOT pronounced as a word, but each letter is read in sequence (e.g., NIH). Compare to acronym.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "absorption", "definition": "CDISC Definition: The process by which medications reach the blood stream when administered other than intravenously, for example, through nasal membranes. See also ADME (pharmacokinetics).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "accelerated approval", "definition": "CDISC Definition: Regulatory mechanism by which new drugs meant to treat serious life-threatening diseases and that provide meaningful therapeutic benefit to patients over existing treatments can be approved rapidly. [after FDA, Guidance for Industry Expedited Programs for Serious Conditions - Drugs and Biologics; after NIH-FDA BEST (Biomarkers, Endpoints, and other Tools) Resource https://www.ncbi.nlm.nih.gov/books/NBK338448/]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "accrual", "definition": "CDISC Definition: The accumulation or increase in the number of study subject enrolled over time. NOTE: Accrual is often conflated with enrollment but there is a semantic distinction in that accrual represents the rate of subject enrollment. [After Schroen AT, Petroni GR, Wang H, Thielen MJ, Gray R, Benedetti J, Wang XF, Sargent DJ, Wickerham DL, Cronin W, Djulbegovic B, Slingluff CL Jr. Achieving sufficient accrual to address the primary endpoint in phase III clinical trials from U.S. Cooperative Oncology Groups. Clin Cancer Res. 2012 Jan 1;18(1):256-62.] See also target enrollment.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "acronym", "definition": "CDISC Definition: A word formed from the beginning letters (e.g., ANSI) or a combination of syllables and letters (e.g., MedDRA) of a name or phrase. NOTE: An acronym is usually pronounced as a word, not by speaking each letter individually. Compare to abbreviation", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "action letter", "definition": "CDISC Definition: An official communication from FDA to an NDA sponsor announcing an agency decision. See also approval letter, approvable letter, not-approvable letter.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "activation (EDC)", "definition": "CDISC Definition: Enabling an eClinical trial system to capture data; usually used for EDC systems.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "active ingredient dose", "definition": "CDISC Definition: The amount of a single active substance administered in a single dose.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "active ingredient", "definition": "CDISC Definition: Any component of a drug product intended to exert pharmacological activity or other direct effect in the diagnosis, cure, mitigation, treatment, or prevention of disease, or to affect the structure or any function of the body of humans or other animals. [After 21 CFR 210.3(b)(7)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "active substance", "definition": "CDISC Definition: Substance responsible for the activity of the medicine. NOTE: The protocol may define the active substance in terms of the Anatomical Therapeutic Chemical (ATC) code (level 3-5). [EMA Glossary of regulatory terms; EU Reg 536/2014] See also international nonproprietary name (INN), generic name.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "adaptive design", "definition": "CDISC Definition: A clinical trial design that allows for prospectively planned modifications to one or more aspects of the design based on accumulating data from subjects in the trial. [Adaptive Designs for Clinical Trials of Drugs and Biologics Guidance for Industry, FDA] See also master protocol.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "additive effect", "definition": "CDISC Definition: An interaction between bioactive compounds or drugs that is deemed to be equal to the sum of the individual components. NOTE: The terms additivity, synergism, and antagonism should be used with care, unless the specific pharmacological pathways or mechanisms of action of the investigated drugs are known. [After Calzetta L, Koziol-White C. Pharmacological interactions: Synergism, or not synergism, that is the question. Curr Res Pharmacol Drug Discov. 2021 Aug 11;2:100046.] See also synergistic effect, antagonistic effect, drug interaction.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "adequate and well-controlled studies", "definition": "CDISC Definition: Studies used to support drug marketing authorization and intended to provide substantial evidence of effectiveness required by law to support a conclusion that a drug is effective. NOTE: For additional information see COA glossary of terms. [After 1. FDA Clinical Outcome Assessment (COA) Glossary; 2. 21 CFR 314.126]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "adherence", "definition": "CDISC Definition: The act of abiding by a stated treatment plan or protocol. [NCI]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "adjuvant therapy", "definition": "CDISC Definition: Therapy administered to augment or stimulate other treatment modalities or to minimize or prevent disease recurrence subsequent to the main treatment. [After NCI Thesaurus] See also neoadjuvant therapy, treatment.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "administrable dosage form", "definition": "CDISC Definition: Pharmaceutical dose form for administration to the patient, after any necessary transformation of the manufactured items and their corresponding manufactured dose forms has been carried out. [After ISO 11615 Identification of medicinal products-Data elements and structures for the unique identification and exchange of regulated medicinal product information, Second edition 2017-10] See also route of administration, administration (substance).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "administration (substance)", "definition": "CDISC Definition: The act of introducing a substance into or onto the body. [After EDQM Standard Terms controlled vocabularies for pharmaceutical dose forms Version 1.2.0 2019. Internal controlled vocabularies for pharmaceutical dose forms. Version 1.2.0 - 28 January 2019.] See also route of administration, administrable dosage form.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "admission criteria", "definition": "CDISC Definition: Basis for selecting target population for a clinical trial. Subjects must be screened to ensure that their characteristics match a list of admission criteria and that none of their characteristics match any single one of the exclusion criteria set up for the study. See also inclusion criteria, exclusion criteria.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "adverse drug reaction (ADR)", "definition": "CDISC Definition: Any noxious and unintended response associated with the use of a drug in humans. NOTE: 1. Post-approval: an adverse event that occurs at doses normally used in man for prophylaxis, diagnosis, or therapy of diseases or for modification of physiological function. 2. Pre-approval: an adverse event that occurs at any dose and where a causal relationship is at least a reasonable possibility. 3. FDA 21 CFR 310.305 defines an adverse drug experience to include any adverse event, \"whether or not considered to be drug-related.\" CDISC recognizes that current usage incorporates the concept of causality. [WHO Technical Report 498(1972); ICH E2A]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "adverse event (AE)", "definition": "CDISC Definition: Any untoward medical occurrence in a patient or clinical investigation subject administered a pharmaceutical product and which does not necessarily have a causal relationship with this treatment. an adverse event (AE) can therefore be any unintended sign (including an abnormal laboratory finding), symptom, or disease temporally associated with the use of a medicinal (investigational) product, whether or not related to the medicinal (investigational) product. NOTE: For further information, see the ICH Guideline for Clinical safety Data Management: Definitions and standards for expedited Reporting. [After ICH E2A] See also serious adverse event, serious adverse experience.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "adverse event of special interest", "definition": "CDISC Definition: An event (serious or non-serious) of scientific and medical concern specific to the sponsor's product or program, for which ongoing monitoring and rapid communication by the investigator to the sponsor can be appropriate. [ICH E19; ICH E2F]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "adverse reaction", "definition": "CDISC Definition: A response to a medicinal product, devices, or procedures, which is noxious and unintended. Response in this context means that a causal relationship between a medicinal product and an adverse event is at least a reasonable possibility. In the context of drug development, the term is used as a synonym of adverse drug reaction. (After ICH E2A)", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "AEGIS (ADROIT Electronically Generated Information Service)", "definition": "CDISC Definition: A subscription service that provides subscribing organizations with access to adverse drug reaction data from the Medicines Control Agency ADROIT (Adverse Drug Reaction On-line Information Tracking) database.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "AHIC (American Health Information Community)", "definition": "CDISC Definition: A US government-charted commission providing input and recommendations to HHS on how to make health records digital and interoperable, and assure the privacy and security of those records (HITSP).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "AI prompt", "definition": "CDISC Definition: Natural language inputs; a request made by the user to the AI tool. NOTE: It can be a question, code syntax, or any combination of text and code. Depending upon the prompt, the tool returns the response. [After \"What is an AI Prompt?\", GeeksforGeeks, 15 April 2025, https://www.geeksforgeeks.org/what-is-an-ai-prompt/] See also Generative AI (GenAI).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "ALCOA", "definition": "CDISC Definition: Acronym for a number of attributes or dimensions that are considered of universal importance for data integrity of source data and the records that hold those data. These include that the data and records be: A-Attributable (to both subject and to any actor on a record); L-Legible (available for human review, possible to read electronically if an encoded eRecord); C-Contemporaneous (timing of data collection with respect to the time the observation is made: the more promptly an observation is recorded, the better the quality.); O-Original (the first suitably accurate and reliable recording of data for the intended purpose); A-Accurate (free from error especially as the result of care; an accurate diagnosis conforming exactly to truth or to a standard). NOTE: ALCOA stemmed from FDA's Dr. Stan Woollen's talks in the early 90's on earmarks for the quality of records and has become a widespread acronym reflecting best practices for clarity and usability of data. [From EMA Reflection Paper on eSOURCE in effect since 2010] See also: Data Quality and the Origin of ALCOA. See also: Six Primary Dimensions for Data Quality Assessment. See also ALCOA+, ALCOA++, data integrity, data quality.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "ALCOA+", "definition": "CDISC Definition: Acronym for a number of attributes or dimensions included in ALCOA, plus the following: Complete, Consistent, Enduring, and Available when needed. NOTE: ALCOA+ is a recent way to summarily refer to the attributes or dimensions of data integrity.) [After EMA Reflection Paper on eSOURCE in effect since 2010. After WHO Annex V, Guidance on Good Data and Record Management Practices] See also ALCOA, ALCOA++, data integrity, data quality.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "ALCOA++", "definition": "CDISC Definition: An extension to ALCOA+ (A-Attributable; L-Legible; C-Contemporaneous; O-Original; A-Accurate; + Complete, Consistent, Enduring, Available) to include traceability. [After Tetrascience ALCOA++ principles for data integrity Fact Sheet] See also ALCOA, ALCOA+, data integrity, data quality.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "alert", "definition": "CDISC Definition: To cause a high-priority signal (or warning) to be transmitted to the relevant stakeholder by way of the local system or another system (usually according to an established set of rules). For example, the system may transmit an alert to a patient's cardiologist that the patient has experienced another heart attack. another example is that the pharmacy system may transmit an alert to the prescribing physician that a potentially dangerous drug-drug interaction may occur based on the current list of medications. another example is that the system may notify a patient's physician that laboratory results (that are not within normal limits) are available. [HL7 EHR-SFM Glossary of Terms, 2010]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "algorithm", "definition": "CDISC Definition: Step-by-step procedures for making a series of choices among alternative decisions to reach a calculated result or decision. NOTE: An algorithm may be used clinically to guide treatment decisions for an individual patient on the basis of the patient's clinical outcome or result. [after AMA Style Guide, 9th Edition]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "alpha error", "definition": "CDISC Definition: The likelihood that a relationship observed between two variables is due to chance. The probability of a Type 1 error. [Modified from AMA Manual of Style]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "amendment", "definition": "CDISC Definition: A written description of a change(s) to, or formal clarification of, a document.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "American National Standards Institute (ANSI)", "definition": "CDISC Definition: Founded in 1918, ANSI itself does not develop standards. ANSI's roles include serving as the coordinator for US voluntary standards efforts, acting as the approval body to recognize documents developed by other national organizations as American National Standards, acting as the US representative in international and regional standards efforts, and serving as a clearinghouse for national and international standards development information. [HL7]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "analysis dataset", "definition": "CDISC Definition: An organized collection of data or information with a common theme arranged in rows and columns and represented as a single file; comparable to a database table. NOTE: standardizing analysis datasets is intended to make review and assessment of analysis more consistent [ADaM].", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "analysis set", "definition": "CDISC Definition: A set of subjects whose data are to be included in the main analyses. This should be defined in the statistical section of the protocol. NOTE: There are a number of potential analysis sets, including, for example, the set based upon the intent-to-treat principle. [ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "analysis variables", "definition": "CDISC Definition: Variables used to test the statistical hypotheses identified in the protocol and analysis plan; variables to be analyzed. See also variable.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "analysis", "definition": "CDISC Definition: The process of mathematically summarizing and comparing data to confirm or refute a hypothesis. [AMA Manual of Style]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "anchor", "definition": "CDISC Definition: Designation for a planned activity, often marking the transition between epochs or elements of a clinical study plan (e.g., \"FPFV-first patient first visit\").", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "anonymization", "definition": "CDISC Definition: The process of protecting privacy that removes the association between the identifying data and the data subject. In anonymized data, the patient cannot be identified by the recipient of the information. [ISO TS 25237:2008; TransCelerate Protection of Personal Data in Clinical Documents - A Model Approach]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "antagonistic effect", "definition": "CDISC Definition: An interaction between bioactive compounds or drugs that is deemed to be less than the sum of each individual component. NOTE: The terms additivity, synergism, and antagonism should be used with care, unless the specific pharmacological pathways or mechanisms of action of the investigated drugs are known. [After Calzetta L, Koziol-White C. Pharmacological interactions: Synergism, or not synergism, that is the question. Curr Res Pharmacol Drug Discov. 2021 Aug 11;2:100046.] See also synergistic effect, antagonistic effect, drug interaction.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "antibody", "definition": "CDISC Definition: A type of protein made by B lymphocytes in response to a foreign substance (antigen). Each antibody only binds to a specific antigen, helping to destroy the antigen directly or by assisting white blood cells to destroy the antigen. [NCI] See also antigen, immune system, autoimmunity.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "anticipated adverse event", "definition": "CDISC Definition: Other adverse events that are not study endpoints and are not \"expected\" (i.e., because they are not in the investigator's brochure) that can be anticipated to occur with some frequency during the course of the trial, regardless of drug exposure, depending on the patient population and disease under study. NOTE: Examples of such \"anticipated\" events include known consequences of the underlying disease or condition under investigation, events anticipated from any background regimen, or re-emergence or worsening of a condition relative to pretreatment baseline. [after FDA, Guidance for Industry and Investigators: Safety Reporting Requirements for INDs and BA/BE Studies]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "antigen", "definition": "CDISC Definition: Any substance, generally a protein, that stimulates the immune system and elicits an immune response. Recognition by the immune system elicits either a T-lymphocyte response, recognizing processed antigens, or a B-lymphocyte response, producing antibodies that bind to unprocessed antigens. [NCI] See also antibody, immune system, autoimmunity.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "applet", "definition": "CDISC Definition: A small application, typically downloaded from a server.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "applicable regulatory requirement(s)", "definition": "CDISC Definition: Any law(s) or regulation(s) addressing the conduct of clinical trials of investigational products. [ICH E6(R2) Glossary, 1.4]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "approvable letter", "definition": "CDISC Definition: An official communication from FDA to an NDA/ BLA sponsor that lists issues to be resolved before an approval can be issued. [Modified from 21 CFR 314.3; Guidance to industry and FDA staff (10/08/2003)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "approval (in relation to Institutional Review Boards)", "definition": "CDISC Definition: The affirmative decision of the IRB that the clinical trial has been reviewed and may be conducted at the institution site within the constraints set forth by the IRB, the institution, good clinical practice (GCP), and the applicable regulatory requirements. [ICH E6]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "approval letter", "definition": "CDISC Definition: An official communication from FDA to inform an applicant of a decision to allow commercial marketing consistent with conditions of approval. [Modified from 21 CFR 314.3; Guidance to industry and FDA staff (10/08/2003)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "artificial intelligence (AI)", "definition": "CDISC Definition: A system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation. NOTE: Types of artificial intelligence include generative AI and large language models. [Kaplan, A; Haenlein, M (1 January 2019) Business Horizons; IEEE-USA POSITION STATEMENT. Artificial Intelligence Research, Development & Regulation Adopted by the IEEE-USA, Board of Directors (February 2017); After FDA Medical Products Paper: Artificial Intelligence & Medical Products: How CBER, CDER, CDRH, and OCP are Working Together, March 2024] See also machine learning, deep learning, natural language processing, synthetic data.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "assent form", "definition": "CDISC Definition: A document explaining all the relevant information to assist an individual, who is unable to give informed consent on their own behalf, in understanding the expectations and risks in making a decision about a procedure.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "assessment", "definition": "CDISC Definition: The interpretation or evaluation of an obtained value by using a test, tool, instrument, or expert judgement of the status of a study subject. [After BEST Resource] See also variable, outcome, endpoint.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "attributable", "definition": "CDISC Definition: A quality by which records and data can be traced back to the subject to whom they pertain, as well as to those persons who have acted on the records.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "attribute (n)", "definition": "CDISC Definition: In data modeling, refers to specific items of data that can be collected for a class.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "audit certificate", "definition": "CDISC Definition: Document that certifies that an audit has taken place (at an investigative site, CRO, or clinical research department of a pharmaceutical company). [ICH E6 Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "audit report", "definition": "CDISC Definition: A written evaluation by the auditor of the results of the audit. [Modified from ICH E6 Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "audit trail", "definition": "CDISC Definition: A process that captures details such as additions, deletions, or alterations of information in an electronic record without obliterating the original record. An audit trail facilitates the reconstruction of the history of such actions relating to the electronic record. [after ICH E6, CSUICI]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "audit", "definition": "CDISC Definition: A systematic and independent examination of trial-related activities and documents to determine whether the evaluated trial-related activities were conducted and the data were recorded, analyzed, and accurately reported according to the protocol, sponsor's standard operating procedures (SOPs), good clinical practice (GCP), and the applicable regulatory requirement(s). [ICH E6 Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "authorised auxiliary medicinal product", "definition": "CDISC Definition: A medicinal product that is currently authorised for marketing in a country or region, that is related to the specific needs of the clinical trial as described in the protocol, but not as an investigational medicinal product, regardless of labelling of the auxiliary medicinal product. [after EU CTR]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "authorised investigational medicinal product", "definition": "CDISC Definition: A medicinal product that is currently authorised for marketing in a country or region and used as an investigational medicinal product, irrespective of changes to the labelling of the medicinal product. [After EU CTR (EU) No 536/2014] See also investigational medicinal product.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "authorization", "definition": "CDISC Definition: The process of giving someone permission to do or have something. In multi-user computer systems, a system administrator defines for the system which users are allowed access to the system and what privileges of use are permitted. [HL7 EHR-S FM Glossary of Terms, 2010].", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "autoimmunity", "definition": "CDISC Definition: An attack response of the immune system against the body's own components. [Janeway CA Jr, Travers P, Walport M, et al. Immunobiology: The Immune System in Health and Disease. 5th edition. New York: Garland Science; 2001. Autoimmune responses are directed against self antigens. Available from: https://www.ncbi.nlm.nih.gov/books/NBK27155/] See also immune system, antibody, antigen.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "auxiliary medicinal product", "definition": "CDISC Definition: A medicinal product that is related to the specific needs of the clinical trial as described in the protocol, but not as an investigational medicinal product. NOTE: Auxiliary medicinal products may be authorised for marketing in a country or region or non-authorised. [after EU-CTR]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "back translation (natural language)", "definition": "CDISC Definition: The process of translating a document that was translated from one language to another back to the original language. Used to ensure that consent forms, surveys, and other clinical trial documents will be clear and accurate in the translated form.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "background material", "definition": "CDISC Definition: Information pertinent to the understanding of a protocol. NOTE: Examples include investigator brochure, literature review, history, rationale, or other documentation that places a study in context or presents critical features.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "background treatment", "definition": "CDISC Definition: Medicinal products that are administered to each clinical trial subject, regardless of randomization group, a) to treat the indication which is the object of the study, or b) required in the protocol as part of standard care for a condition that is not the indication under investigation, and is relevant for the clinical trial design. [After Recommendations from the expert group on clinical trials for the implementation of Regulation (EU) No 536/2014' dd 28 June 2017]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "balanced study", "definition": "CDISC Definition: Trial in which a particular type of subject is equally represented in each study group.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "bandwidth", "definition": "CDISC Definition: An indicator of the throughput (speed) of data flow on a transmission path; the width of the range of frequencies on which a transmission medium carries electronic signals. All digital and analog signal channels have a bandwidth.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "baseline assessment", "definition": "CDISC Definition: Assessment of subjects as they enter a trial and before they receive any treatment.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "baseline characteristics", "definition": "CDISC Definition: Demographic, clinical, and other data collected for each participant at the beginning of the trial before the intervention is administered. NOTE: Randomized, controlled trials aim to compare groups of participants that differ only with respect to the intervention (treatment). although proper random assignment prevents selection bias, it does not guarantee that the groups are equivalent at baseline. any differences in baseline characteristics are, however, the result of chance rather than bias. The study groups should be compared at baseline for important demographic and clinical characteristics. Baseline data may be especially valuable when the outcome measure can also be measured at the start of the trial. [CONSORT statement]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "baseline imbalance", "definition": "CDISC Definition: A systematic error in creating intervention groups, such that they differ with respect to prognosis. That is, the groups differ in measured or unmeasured baseline characteristics because of the way participants were selected or assigned. NOTE: also used to mean that the participants are not representative of the population of all possible participants. [ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "basket trial design", "definition": "CDISC Definition: A type of trial design under a master protocol designed to test a single investigational drug or drug combination in different populations defined by disease stage, histology, number of prior therapies, genetic or other biomarkers, or demographic characteristics. [After US FDA, Master Protocols: Efficient Clinical Trial Design Strategies to Expedite Development of Oncology Drugs and Biologics Guidance for Industry, 2022] See also master protocol.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "basket trial", "definition": "CDISC Definition: A type of trial conducted under a master protocol and designed to test a single investigational drug or drug combination in different populations defined by disease stage, histology, number of prior therapies, genetic or other biomarkers, or demographic characteristics. [After US FDA, Master Protocols: Efficient Clinical Trial Design Strategies to Expedite Development of Oncology Drugs and Biologics Guidance for Industry, 2022; Woodcock J, LaVange LM. Master Protocols to Study Multiple Therapies, Multiple Diseases, or Both. N Engl J Med. 2017 Jul 6;377(1):62-70.]. See also basket trial design, adaptive design, master protocol.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Bayesian approaches", "definition": "CDISC Definition: Approaches to data analysis that provide a posterior probability distribution for some parameter (e.g., treatment effect), derived from the observed data and a prior probability distribution for the parameter. The posterior distribution is then used as the basis for statistical inference. [ICH E9 Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Bayesian statistics", "definition": "CDISC Definition: Statistical approach named for Thomas Bayes (1701-1761) that has among its features giving a subjective interpretation to probability, accepting the idea that it is possible to talk about the probability of hypotheses being true and of parameters having particular values.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "benefit summary", "definition": "CDISC Definition: A description of any physical, psychological, social, or other potential positive effects to individual participants as a result of participating in the study, addressing immediate potential benefits and/or long-range potential benefits. NOTE: In a benefit summary, no or minimal benefits must also be described. [After ICH M11] See also clinical benefit, treatment benefit.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "beta error", "definition": "CDISC Definition: Probability of showing no significant difference when a true difference exists; a false acceptance of the null hypothesis. See also Type 2 error. [AMA Manual of style]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "bias", "definition": "CDISC Definition: Bias refers to defects in study design, measurement, analysis or interpretation such that they cause a result to depart from the true value in a consistent direction. [after AMA Manual of style, ICH E9, CONSORT Statement]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "bioanalytical assays", "definition": "CDISC Definition: Methods for quantitative measurement of a drug, drug metabolites, or chemicals in biological fluids.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "bioavailability", "definition": "CDISC Definition: Rate and extent to which a drug is absorbed or is otherwise available to the treatment site in the body.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "biobanking", "definition": "CDISC Definition: The storage of collected biospecimens for future use. (After NHMRC (2010) National Health and Medical Research Council \"Biobanks information paper\". Canberra. https://www.nhmrc.gov.au/sites/default/files/documents/attachments/Biobanks-information-paper-2010.pdf) See also biospecimen, biorepository.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "bioequivalence", "definition": "CDISC Definition: Scientific basis on which drugs with the same active ingredient(s) are compared. NOTE: To be considered bioequivalent, the bioavailability of two products must not differ significantly when the two products are given in studies at the same dosage under similar conditions.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "biological product", "definition": "CDISC Definition: A product of biological origin applicable to the prevention, treatment, or cure of a disease or condition. Such products may include virus, therapeutic serum, toxin, antitoxin, vaccine, blood, blood component or derivative, allergenic product, protein, or analogous product. NOTE: Biological products may be produced through biotechnology in a living system, such as a microorganism, plant cell, or animal cell. Biological products are generally large, complex molecules and are often more difficult to characterize than small molecule drugs. [After 21 CFR 600.3; After FDA Biological Product Definitions] See also vaccine, cell therapy, gene therapy, pharmaceutical product, drug product, medicinal product.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Biologics licensing application (BLA)", "definition": "CDISC Definition: Biologics licensing application (BLA). an application to FDA for a license to market a new biologic product in the United states.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "biomarker", "definition": "CDISC Definition: A defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions. Molecular, histologic, radiographic, or physiologic characteristics are types of biomarkers. A biomarker is not an assessment of how an individual feels, functions, or survives. Categories of biomarkers include: susceptibility/risk biomarker; diagnostic biomarker; monitoring biomarker; prognostic biomarker; predictive biomarker; safety biomarker; pharmacodynamic/response biomarker. [NIH-FDA BEST (Biomarkers, Endpoints, and other Tools) Resource, https://www.ncbi.nlm.nih.gov/books/NBK338448/]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "biometric signature", "definition": "CDISC Definition: A signature based on the verification of an individual's identity, based on measurement of the individual's physical feature(s) or repeatable action(s), where those features and/or actions are both unique to that individual, and measureable [21 CFR 11]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "biorepository", "definition": "CDISC Definition: A storage facility for biospecimens. NOTE: The biorepository has stringent guidelines regarding the standardized collection, handling, storage, and documentation of biological specimens. See also biobanking, biospecimen.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "biosimilar", "definition": "CDISC Definition: A biological product that is highly similar to the reference product notwithstanding minor differences in clinically inactive components. This requires that there are no clinically meaningful differences between the biological product and the reference product in terms of the safety, purity, and potency of the product (see section 351(i)(2) of the PHS Act). [after FDA, Guidance for Industry: Quality Considerations in Demonstrating Biosimilarity of a Therapeutic Protein Product to a Reference Product]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "biospecimen", "definition": "CDISC Definition: Any material from a biological entity used for testing, diagnostic, propagation, treatment, or research purposes. [See SDTM codelists: https://evsexplore.semantics.cancer.gov/evsexplore/subset/ncit/C78734; https://evsexplore.semantics.cancer.gov/evsexplore/subset/ncit/C111114] See also biobanking, biorepository.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "biostatistics", "definition": "CDISC Definition: Branch of statistics applied to the analysis of biological phenomena.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "blind review", "definition": "CDISC Definition: Checking and assessing data prior to breaking the blind, for the purpose of finalizing the planned analysis. [Modified ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "blinded (masked) medications", "definition": "CDISC Definition: Products that appear identical in size, shape, color, flavor, and other attributes to make it very difficult for subjects and investigators (or anyone assessing the outcome) to determine which medication is being administered.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "blinded study", "definition": "CDISC Definition: A study in which the subject, the investigator, or anyone assessing the outcome is unaware of the treatment assignment(s). NOTE: Blinding is used to reduce the potential for bias. [Modified ICH E6 Glossary] See also blinding/masking, double-blind study, single-blind study, triple-blind study; contrast with open-label or unblinded study.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "blinding", "definition": "CDISC Definition: A procedure to limit bias by preventing subjects and/ or study personnel from identifying which treatments or procedures are administered, or from learning the results of tests and measures undertaken as part of a clinical investigation. [After Abhaya Indrayan, Martin P. Holt. Concise Encyclopedia of Biostatistics for Medical Professionals. Chapman & Hall; November 17, 2016] See also double-blind study, single-blind study, triple-blind study. Contrast with open-label and/or unblinded study, masking.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "blood draw", "definition": "CDISC Definition: The collection of blood from a vein, most commonly via needle venipuncture. (NCI)", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "branch", "definition": "CDISC Definition: Point within a study design where there is an allocation of subject subsets to particular procedures or treatment groups.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "browser", "definition": "CDISC Definition: Computer program that runs on the user's desktop computer and is used to navigate the World Wide Web. See also web browser.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "cache", "definition": "CDISC Definition: Storage area on a computer's hard drive where the browser stores (for a limited time) web pages and/or graphic elements.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "carry-over effect", "definition": "CDISC Definition: Effects of treatment that persist after treatment has been stopped, sometimes beyond the time of a medication's known biological activity.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "case history", "definition": "CDISC Definition: An adequate and accurate record prepared and maintained by an investigator that records all observations and other data pertinent to the investigation of each individual administered the investigational drug (device or other therapy) or employed as a control in the investigation. NOTE: Case histories include the case report forms and supporting data including, for example, signed and dated consent forms and medical records including, for example, progress notes of the physician, the individual's hospital chart(s), and the nurses' notes. The case history for each individual shall document that informed consent was obtained prior to participation in the study. [21 CFR 312.62(b)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "case report form (CRF)", "definition": "CDISC Definition: A printed, optical, or electronic document designed to record all of the protocol-required information to be reported to the sponsor for each trial subject. NOTE: In common usage, CRF can refer to either a CRF page, which denotes a group of one or more data items, linked together for collection and display, or a casebook, which includes the entire group of CRF pages on which a set of clinical study observations can be or have been collected by completion of such CRF pages for a subject in a clinical study. See also CRF (paper), eCRF. [ICH E6 Glossary, FDA Final Guidance on eSource].", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "case report tabulations (CRT)", "definition": "CDISC Definition: In a paper submission, listings of data that may be organized by domain (type of data) or by subject. See also eCRT.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "case-control study", "definition": "CDISC Definition: A study in which individuals to an outcome (cases) are compared with those who do not have the outcome (controls). NOTE: The outcome variable (disease, event, experience, biomarker) is chosen first, and the exposure is evaluated in cases vs controls to see whether there is an association between exposure and outcome. [After AMA Manual of Style] See also outcome, observational study.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "categorical data", "definition": "CDISC Definition: Data evaluated by sorting values (for example, severe, moderate, and mild) into various categories.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "causality assessment", "definition": "CDISC Definition: An evaluation performed by a medical professional concerning the likelihood that a therapy or product under study caused or contributed to an adverse event.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "CDISC Library", "definition": "CDISC Definition: A global, accessible, electronic library, which, through advanced technology, enables precise and standardized data element definitions that can be used within applications and across studies to improve biomedical research and its link with healthcare. NOTE: Formerly known as CDISC SHARE. [CDISC]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "CDISC standards", "definition": "CDISC Definition: A set of models, implementation guides, controlled vocabularies, and exchange formats developed by the Clinical Data Interchange Standards Consortium (CDISC), which are intended to provide for consistent use of common representations of data, terms and specifications. NOTE: These standards apply to translational research, electronic submission of clinical data, and the life-cycle of clinical product development, which includes protocol representation, data collection, aggregation, tabulation, and analysis and unambiguous information exchange across disparate systems. [After https://www.ncbi.nlm.nih.gov]. See also standard, data standards, Study Data Standardization Plan, and Standards Development Organization.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "cell therapy", "definition": "CDISC Definition: The prevention or treatment of human disease by the administration of cells that have been selected, multiplied, and pharmacologically treated or altered outside the body (ex vivo), or methods (pharmacological as well as nonpharmacological) to modify the function of intrinsic cells of the body for therapeutic purposes (in vivo). NOTE: Cell therapies can be classified based on therapeutic indication, cell type, source of cells, and underlying technology, among others, in medical and regulatory contexts. [After https://www.sciencedirect.com/topics/neuroscience/cell-therapy; After Regulation (EC) No 1394/2007 of the European Parliament and of the Council of 13 November 2007.] See also regenerative medicine therapy, regenerative medicine advanced therapy, gene therapy, biological product.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "certified copy", "definition": "CDISC Definition: A copy (irrespective of the type of media used) of the original record that has been verified (i.e., by a dated signature or by generation through a validated process) to have the same information, including data that describe the context, content, and structure, as the original. [ICH E6 (R2)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "certified IRB professional (CIP)", "definition": "CDISC Definition: Persons certified to participate on an institutional review board, who satisfy the educational and employment requirements and pass an examination conducted by the applied Research ethics national association (aRena), the membership division of Public Responsibility in Medicine and Research (PRiM&R).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "challenge agent", "definition": "CDISC Definition: A non-investigational medicinal product (NIMP) given to trial subjects to produce a physiological response that is necessary before the pharmacological action of the investigational medicinal product can be assessed. [After Recommendations from the expert group on clinical trials for the implementation of Regulation (EU) No 536/2014' dd 28 June 2017]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "CHI (consolidated health informatics)", "definition": "CDISC Definition: CHI began as an eGov initiative to establish a portfolio of existing health information interoperability standards (health vocabulary and messaging) enabling all agencies in the federal health enterprise to \"speak the same language\" based on common enterprise-wide business and information technology architectures. CHI is currently managed under the Office of the National Coordinator for Health Informational Technology's (ONC) Federal Health Architecture (FHA) Program Management Office. Ref: The United States Health Information Knowledgebase [USHIK]. [HITSP]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "class", "definition": "CDISC Definition: A definition of objects with properties (attributes, methods, relationships) that all objects in the class have in common. [HL7, 2001] in data modeling, a class defines a set of objects that share the same attributes, relationships, and semantics. A class is usually an entity that represents a person, place, or thing.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clean database", "definition": "CDISC Definition: A set of reviewed data in which errors have been resolved to meet QA requirements for error rate and in which measurements and other values are provided in acceptable units; database that is ready to be locked. See also database lock, clean file.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clean file", "definition": "CDISC Definition: When all data cleaning is completed and database is ready for quality review and unblinding.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "client", "definition": "CDISC Definition: A program that makes a service request of another program, usually running on a server, that fulfills the request. Web browsers (such as Firefox and Microsoft explorer) are clients that request HTML files from web servers.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical benefit", "definition": "CDISC Definition: A therapeutic intervention may be said to confer clinical benefit if it prolongs life, improves function, and/or improves the way a subject feels. See also benefit summary, treatment benefit.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical clarification", "definition": "CDISC Definition: A query resolution received from the sponsor staff (medical monitors, DSMB monitoring board, etc.). See also self-evident change.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical data", "definition": "CDISC Definition: Data pertaining to the medical well-being or status of a patient. Category also includes clinical reports and individual patient data (IPD) as defined in the EMA Policy 0070 Implementation Guide. [http://www.ema.eoropa.eu/docs/en_GB/document_library/REPORT/2014/10/WC500174378.PDF]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical development plan", "definition": "CDISC Definition: A document that describes the collection of clinical studies that are to be performed in sequence, or in parallel, with a particular active substance, device, procedure, or treatment strategy, typically with the intention of submitting them as part of an application for a marketing authorization. NOTE: The plan should have appropriate decision points and allow modification as knowledge accumulates. [from ICH E9] See also development plan.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical document architecture", "definition": "CDISC Definition: Specification for the structure and semantics of \"clinical documents\" for the purpose of exchange. [HL7; SPL]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical document", "definition": "CDISC Definition: A documentation of clinical observations and services. NOTE: an electronic document should incorporate the following characteristics: persistence, stewardship, potential for authentication, wholeness, and human readability. [SPL]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical efficacy", "definition": "CDISC Definition: Power or capacity to produce a desired effect (i.e., appropriate pharmacological activity in a specified indication) in humans. [SQA]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical encounter", "definition": "CDISC Definition: Contact between subject/patient and healthcare practitioner/researcher, during which an assessment or activity is performed. Contact may be physical or virtual. [CDISC]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical hold (of a clinical trial)", "definition": "CDISC Definition: An order issued by FDA to the sponsor to delay a proposed clinical investigation or to suspend an ongoing investigation. NOTE: The clinical hold order may apply to one or more of the investigations covered by an IND. [21 CFR 312.42] See also suspension (of a clinical trial), termination (of a clinical trial), temporary halt (of a clinical trial).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical investigation", "definition": "CDISC Definition: Any experiment that involves a test article and one or more human subjects, and that either must meet the requirements for prior submission to the FDA or the results of which are intended to be later submitted to, or held for inspection by, the FDA as part of an application for a research or marketing permit. Considered synonymous with clinical research by FDA. See clinical study, clinical trial. [FDA Science & Research]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical outcome assessment (COA) qualification", "definition": "CDISC Definition: A formal conclusion by FDA that, within the stated context of use, the results of the COA measurement can be relied upon to have a specific interpretation and application. NOTE: For qualified COAs, FDA permits drug developers to use the COA in the qualified context in IND and NDA/BLA submissions without requesting that the relevant CDER review group reconsider and reconfirm the suitability of the COA. [FDA Clinical Outcome Assessment (COA) Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical outcome assessment (COA)", "definition": "CDISC Definition: Any assessment that may be influenced by human choices, judgment, or motivation and may support or refute treatment benefit. NOTE: Unlike biomarkers that rely completely on an automated process or algorithm, COAs reflect interpretation of reporting from a patient, a clinician, or an observer. There are four types of COAs. See also patient-reported outcome (PRO), clinician-reported outcome (ClinRO), observer-reported outcome (ObsRO), and performance outcome (PerfO). [FDA Clinical Outcome Assessment (COA) Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical pharmacology", "definition": "CDISC Definition: Science that deals with the characteristics, effects, properties, reactions, and uses of drugs, particularly their therapeutic value in humans, including their toxicology, safety, pharmacodynamics, and pharmacokinetics (ADME).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical research and development", "definition": "CDISC Definition: The testing of a drug compound in humans primarily done to determine its safety and pharmacological effectiveness. Clinical development is done in phases, which progress from very tightly controlled dosing of a small number of subjects to less tightly controlled studies involving large numbers of patients. [SQA]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical research associate (CRA)", "definition": "CDISC Definition: Person employed by a study sponsor or by a contract research organization (CRO) acting on a sponsor's behalf, who monitors the status, data integrity, and protocol adherence of investigator sites participating in a clinical study. See also sponsor.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical research coordinator (CRC)", "definition": "CDISC Definition: A qualified study staff member who manages the participation of subjects according to the study protocol. NOTE: CRCs coordinate communication among the subject, investigator, and sponsor. Responsibilities may also include screening, enrollment, monitoring of potential participants, and informed consent. [After Clinical Research Manual: Practical Tools and Templates for Managing Clinical Research Cavalieri Jennifer and Rupp Mark Clinical Research Manual: Practical Tools and Templates for Managing Clinical Research 336pp US$44.95 Sigma Theta Tau 9781937554637 1937554635 [Formula: see text]. Nurs Manag (Harrow). 2014 Aug 28;21(5):13.; After SOCRA]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical research subject", "definition": "CDISC Definition: A person who is enrolled into a clinical study or trial. See also study, trial, and study population.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical significance", "definition": "CDISC Definition: Change in a subject's clinical condition regarded as important whether or not due to the test intervention. NOTE: some statistically significant changes (in blood tests, for example) have no clinical significance. The criterion or criteria for clinical significance should be stated in the protocol. The term \"clinical significance\" is not advisable unless operationally defined.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical study data element", "definition": "CDISC Definition: A single observation associated with a subject in a clinical study. A data element in an eCRF represents the smallest unit of observation captured for a subject in a clinical investigation. NOTE: Examples include birth date, white blood cell count, pain severity measure, and other clinical observations made and documented during a study. Data element identifiers should be attached to each data element as it is entered or transmitted by the originator into the eCRF. See also eCRF, data element identifier, data originator, item. [After FDA Guidance for Industry Electronic Source Data in Clinical Investigations , Body text and Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical study report", "definition": "CDISC Definition: A written description of a study of any therapeutic, prophylactic, or diagnostic agent conducted in human subjects, in which the clinical and statistical description, presentations, and analysis are fully integrated into a single report. NOTE: For further information, see the ICH Guideline for Structure and Content of Clinical Study Reports. [ICH E6 Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical study", "definition": "CDISC Definition: A clinical study involves research using human volunteers (also called participants) that is intended to add to medical knowledge. There are two main types of clinical studies: clinical trials (also called interventional studies) and observational studies. [ClinicalTrials.gov] See also clinical trial.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical trial authorization", "definition": "CDISC Definition: Authorization granted by a Medicines Regulatory Agency to conduct a clinical trial in a jurisdiction. NOTE: If an ethical committee allows a trial to proceed it is called an approval to proceed. [After ISO 11615:2017, 3.1.12]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical trial data", "definition": "CDISC Definition: Data collected in the course of a clinical trial. See also clinical trial information.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical trial exemption (CTX)", "definition": "CDISC Definition: A scheme that allows sponsors to apply for approval for each clinical study in turn, submitting supporting data to the Medicines Control Agency (MCA), which approves or rejects the application (generally within 35 working days). NOTE: Approval means that the company is exempt from the requirement to hold a clinical trial certificate (CTC). [UK]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical trial information", "definition": "CDISC Definition: Data collected in the course of a clinical trial or documentation related to the integrity or administration of that data. A superset of clinical trial data.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical trial materials", "definition": "CDISC Definition: Complete set of supplies provided to an investigator by the trial sponsor.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical trial registry", "definition": "CDISC Definition: A web-based publicly accessible platform for providing structured information about clinical trials. NOTE: Such registries help patients, family members, health care professionals, researchers, and the public identify studies in which they might participate. Some registries include clinical trial results. Examples include: EU Clinical Trials Register (EU CTR), for studies in the EU or the EEA after 1 May 2001; ClinicalTrials.gov, a web-based resource from the National Library of Medicine (NLM) in the US. [After International Committee of Medical Journal Editors]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical trial results registry", "definition": "CDISC Definition: A web-based publicly accessible platform for providing structured summary results information about clinical trials. See also clinical trial registry.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinical trial", "definition": "CDISC Definition: A research investigation involving human subjects that is designed to answer specific questions about the safety and efficacy of a biomedical intervention (drug, treatment, device) or new ways of using a known drug, treatment, or device). NOTE: NIH Office of Science Policy further specifies that a clinical trial is a type of research study that prospectively assigns subjects to interventions, and the EU clinical trial regulations set forth 3 specific conditions, any one of which qualifies a study as a clinical trial. These conditions include applying diagnostic or monitoring procedures not used in normal clinical practice to subjects. [After ICH E6 [R2], EU CTR 2014] See also clinical study, clinical investigation, randomized controlled trial (RCT).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "clinician-reported outcome (ClinRO)", "definition": "CDISC Definition: A type of clinical outcome assessment. A measurement based on a report that comes from a trained health-care professional after observation of a patient's health condition. [After BEST Resource]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "co-packaged product", "definition": "CDISC Definition: Two or more separate products packaged together in a single package or as a unit and composed of drug and device products, device and biological products, or biological and drug products. [After 21 CFR 3.2 (e) FAQ] See also combination product, single-entity product, cross-labeled product.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "codelist", "definition": "CDISC Definition: Finite list of codes and their meanings that represent the only allowed values for a data item. A codelist is one type of controlled vocabulary. See also controlled vocabulary.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "coding", "definition": "CDISC Definition: In clinical trials, the process of assigning data to categories for analysis. NOTE: Adverse events, for example, may be coded using MedDRA.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "cognitive debriefing", "definition": "CDISC Definition: A qualitative research tool used to determine whether concepts and items are understood by patients in the same way that PRO instrument developers intend. NOTE: Cognitive debriefing interviews involve incorporating follow-up questions in a field test interview to gain better understanding of how patients interpret questions asked of them and to collect and consider all concepts elicited by an item. [from PRO Draft Guidance Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "cohort study", "definition": "CDISC Definition: Study of a group of individuals, some of whom are exposed to a variable of interest, in which subjects are followed over time. NOTE: Cohort studies can be prospective or retrospective. [After AMA Manual of Style] See also prospective study, observational study, retrospective study, case-control study, cohort.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "cohort", "definition": "CDISC Definition: A group of individuals who share a common exposure, experience or characteristic or a group of individuals followed-up or traced over time in a cohort study. [AMA Manual of Style] See also cohort study.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "combination product", "definition": "CDISC Definition: A product composed of two or more different types of medical products (i.e., a combination of a drug, device, and/or biological product with one another and are referred to as \"constituent parts\" of the combination product). NOTE: A combination product might be a single-entity product, a co-packaged product or a cross-labeled product. [After 21 CFR 3.2 (e)] See also single-entity product, co-packaged product, cross-labeled product.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "commercially confidential information (CCI)", "definition": "CDISC Definition: Any information contained in clinical reports or other documents that is not in the public domain or publicly available and where disclosure may undermine the legitimate economic interest of the company (the Marketing Application Holder) and cause harm (if disclosed). [After EMA Policy 0070 implementation Guide]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "common data element (CDE)", "definition": "CDISC Definition: A structured item characterized by a stem and response options together with a history of usage that can be standardized for research purposes across studies conducted by and for NIH. NOTE: The mark up or tagging facilitates document indexing, search and retrieval, and provides standard conventions for insertion of codes. [NCI, CaBIG]. See also item, item (PRO), stem, data element, data element identifier.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Common Technical Document", "definition": "CDISC Definition: A format agreed upon by ICH to organize applications to regulatory authorities for registration of pharmaceuticals for human use. [ICH] See also eCTD.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Comparative Effectiveness Research (CER)", "definition": "CDISC Definition: A type of study in which the intervention of interest is compared against another intervention(s) of interest to see if there is evidence about the effectiveness, benefits and harms of different treatment options. [NCI]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "comparative study", "definition": "CDISC Definition: One in which the investigative drug is compared against another product, either active drug or placebo.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "comparator (product)", "definition": "CDISC Definition: An investigational or marketed product (i.e., active control), or placebo, used as a reference in a clinical trial. [ICH E6 Glossary] See also control.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "compendial name", "definition": "CDISC Definition: A name within a pharmaceutical compendium that designates a small or large molecule substance that complies with compendial standards for strength, quality, and purity. NOTE: Used for all drugs within the US. [After USP Nomenclature Guidelines (last revision on March 30, 2020)] See also proprietary name, generic name, international nonproprietary name (INN), established name, medicinal product name.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Competent Authority (CA)", "definition": "CDISC Definition: The regulatory body charged with monitoring compliance with the national statutes and regulations of European Member States.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "compliance (in relation to trials)", "definition": "CDISC Definition: Adherence to specifications in the study protocol and regulations by patients, investigators, and other study staff. NOTE: The investigator and sponsor have obligations to follow the protocol and GCP. [After Spilker, B. Guide to Clinical Trials. Lippincott Williams & Wilkins. 2000.]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "computer application", "definition": "CDISC Definition: Software designed to fill specific needs of a user; for example, software for navigation, project management, or process control.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "concept of interest", "definition": "CDISC Definition: In the context of clinical outcomes, the thing measured by a COA assessment (e.g., pain intensity). [After Clinical Outcome Assessment (COA) Glossary of Terms FDA FDA eCOA Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "concept", "definition": "CDISC Definition: Discrete notion having a single meaning. In a controlled vocabulary a concept is mapped to one or more of the words that convey its meaning.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "concerned member state (CMS)", "definition": "CDISC Definition: A classification of a Member States in the Mutual Recognition Procedure (MRP) in the European authorization route resulting in a mutually recognized product. In the Mutual Recognition Procedure, one or more Member States that is a CMS is asked to mutually recognize the Market Authorization of the Reference Member State (RMS). [After Heads of Medicines Agencies (HMA) website http://www.hma.eu/medicinesapprovalsystem.html] See also Mutual Recognition Procedure (MRP) and Reference Member State (RMS).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "concomitant medication", "definition": "CDISC Definition: Any product that is taken by clinical trial participants that is not the investigational medicinal product or auxiliary medicinal product, and is not relevant for the design of the clinical trial. [After Regulation (EU) no 535/2014 Clinical Trials on medicinal products for human use] See also Investigational medicinal product, auxiliary medicinal product.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "conduct", "definition": "CDISC Definition: An ongoing and/or past performance of a formal investigation as specified in a study protocol. [NCI]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "confidence interval (CI)", "definition": "CDISC Definition: A measure of the precision of an estimated value. The interval represents the range of values, consistent with the data, that is believed to encompass the \"true\" value with high probability (usually 95%). The confidence interval is expressed in the same units as the estimate. Wider intervals indicate lower precision; narrow intervals, greater precision. [CONSORT Statement]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "confidentiality", "definition": "CDISC Definition: Prevention of disclosure to other than authorized individuals of a sponsor's proprietary information or of a subject's identity. [ICH E6 Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "confirmatory trial", "definition": "CDISC Definition: Phase 3 trial with results that confirm the preliminary evidence accumulated in earlier phases that a drug is safe and effective for use for the intended indication and recipient population. [After ICH E8] See also non-confirmatory trial result, pragmatic trial. Compare to exploratory trial.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "conformity assessment", "definition": "CDISC Definition: The process by which compliance with the EMA's essential requirements is assessed. See also Notified Body (NB).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "confounding variable", "definition": "CDISC Definition: A factor that may interfere with the interpretation of the effect of an exposure on an outcome. [After AMA Manual of Style]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "consent form", "definition": "CDISC Definition: Document used during the informed consent process that is the basis for explaining to potential subjects the risks and potential benefits of a study and the rights and responsibilities of the parties involved. NOTE: The informed consent document provides a summary of a clinical trial (including its purpose, the treatment procedures and schedule, potential risks and benefits, alternatives to participation, etc.) and explains an individual's rights as a subject. it is designed to begin the informed consent process, which consists of conversations between the subject and the research team. if the individual then decides to enter the trial, s/he gives her/his official consent by signing the document. Informed consent is sometimes administered electronically, i.e., eICF. See also informed consent.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "construct validation (COA)", "definition": "CDISC Definition: Establishing, using quantitative methods, the extent to which the relationships among items, domains, and concepts of a clinical outcome assessment (COA) conform to a priori hypotheses concerning logical relationships that should exist with other measures or characteristics of patients and patient groups. [NIH-FDA BEST (Biomarkers, Endpoints, and other Tools) Resource, https://www.ncbi.nlm.nih.gov/books/NBK338448/] See also validation.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "consumer safety officer (CSO)", "definition": "CDISC Definition: FDA official who coordinates the review process of various applications.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "content validation (COA)", "definition": "CDISC Definition: Establishing from qualitative research the extent to which the clinical outcome assessment (COA) instrument measures the concept of interest including evidence that the items and domains of an instrument are appropriate and comprehensive relative to its intended measurement concept, population, and use. [NIH-FDA BEST (Biomarkers, Endpoints, and other Tools) Resource, https://www.ncbi.nlm.nih.gov/books/NBK338448/] See also validation.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "content validity", "definition": "CDISC Definition: The extent to which a variable (for example, a rating scale) measures what it is supposed to measure. [ICH E9 Glossary] evidence from qualitative research demonstrating that the instrument measures the concept of interest, including evidence that the items and domains of an instrument are appropriate and comprehensive, relative to its intended measurement concept, population, and use. NOTE: Testing other measurement properties will not replace or rectify problems with content validity. [FDA Final PRO Guidance]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "context of use", "definition": "CDISC Definition: In the context of clinical outcomes, a comprehensive statement that fully and clearly describes and justifies the way a COA is to be used and the drug development-related purpose of the use. NOTE: The context of use defines the boundaries within which the available data adequately justify use of the COA and describes important criteria regarding the circumstances under which the COA is qualified. [FDA Clinical Outcome Assessment (COA) Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "contingent subject trial contact", "definition": "CDISC Definition: Planned response to an anticipated but conditional event in a clinical trial. [CDISC Trial Design Project]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "contract research organization (CRO)", "definition": "CDISC Definition: A person or an organization (commercial, academic, or other) contracted by the sponsor to perform one or more of a sponsor's trial-related duties and functions. [ICH E6 Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "contract", "definition": "CDISC Definition: A written, dated, and signed agreement between two or more involved parties that sets out any arrangements on delegation and distribution of tasks and obligations and, if appropriate, on financial matters. The protocol may serve as the basis of a contract. [ICH E6 Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "control group", "definition": "CDISC Definition: A cohort of study participants that is defined for the purpose of comparison to the treatment group in a controlled trial. NOTE: In an epidemiological study, this cohort may or may not have the outcome of interest. [After 21 CFR 314.126] See also control, controlled study, arm (protocol).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "control of electronic records", "definition": "CDISC Definition: To prepare and maintain case histories and other records for regulated clinical investigations or other regulated research. NOTE: Control is often used as a casual synonym for the terms in 21 CFR 312.62 requiring investigative sites to prepare, maintain, and retain adequate and accurate case histories. [After 1. 21 CFR 11; 2. CSUCT] See also record.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "control", "definition": "CDISC Definition: A comparator against which the study treatment is evaluated [e.g., concurrent (placebo, no treatment, dose-response, active), and external (historical, published literature, synthetic data)]. [After ICH E10]. See also comparator (product), control group, controlled study, arm (protocol), synthetic data.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "controlled study", "definition": "CDISC Definition: A study in which a test article is compared with a treatment that has known effects (active control), no treatment, placebo, or dose comparison concurrent control, or external (historic) control. [21 CFR 314.126 and ICH E10]. See also control, comparator (product), control group.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "controlled vocabulary", "definition": "CDISC Definition: A finite set of values that represent the only allowed values for a data item. These values may be codes, text, or numeric. See also codelist.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "coordinating committee", "definition": "CDISC Definition: A committee that a sponsor may organize to coordinate the conduct of a multicenter trial. [ICH E6]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "coordinating investigator", "definition": "CDISC Definition: An investigator assigned the responsibility for the coordination of investigators at different centers participating in a multicenter trial. NOTE: Depending on the scope of the trial, coordination could be across centers/sites in a region, across regions, or within a nation. [ICH E6] See also investigator, investigator/institution, principal investigator, site investigator, sponsor-investigator, sub-investigator.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "correlation", "definition": "CDISC Definition: The degree to which two or more variables are related. Typically the linear relationship is measured with either Pearson's correlation or spearman's Rho. NOTE: Correlation does not necessarily mean causation. [After Hyperstat Online Glossary; CDISC ADaM]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "covariate (prognostic)", "definition": "CDISC Definition: Factor or condition that influences outcome of a trial. [ADaM]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "CRF (paper)", "definition": "CDISC Definition: Case report form in which the data items are linked by the physical properties of paper to particular pages. NOTE: Data are captured manually and any comments, notes, and signatures are also linked to those data items by writing or typescript on the paper pages. See also eCRF, case report form.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "CRF data", "definition": "CDISC Definition: Subset of clinical trial data that are entered into fields on a case report form.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "criterion validation (COA)", "definition": "CDISC Definition: Establishing the extent to which the scores of a clinical outcome assessment instrument are related to a known gold standard measure of the same concept. For most COAs clinical outcome assessments (COAs), criterion validity cannot be measured because there is no gold standard. [NIH-FDA BEST (Biomarkers, Endpoints, and other Tools) Resource, https://www.ncbi.nlm.nih.gov/books/NBK338448/] See also validation.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "cross-labeled product", "definition": "CDISC Definition: An investigational drug, device, or biological product packaged separately that, according to its proposed labeling, is intended for use only with another investigational or approved individually specified drug, device, or biological product where both are required to achieve the intended use, indication, or effect. NOTE: In the case where an approved product is combined with an investigational product, upon approval of the cross-labeled product the label of the previously approved product should be modified to reflect the combination status. [After 21 CFR 3.2 (e) FAQ] See also combination product, single-entity product, co-packaged product.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "cross-sectional study", "definition": "CDISC Definition: A study that measures the prevalence of health outcomes or determinants of health, or both, in a population at a point in time or over a short period. [After British Medical Journal, Epidemiology for the uninitiated, Chapter 8, Fifth Edition, BMJ Book 2004] See also observational study.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "crossover trial", "definition": "CDISC Definition: A trial design in which subjects function as their own control and are assigned to receive an investigational product(s) and control(s) in an order determined by randomization, with or without a washout period between the interventions. [After ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "CTCAE (Common Terminology Criterion for Adverse Events)", "definition": "CDISC Definition: Standard terminology developed and maintained by the National Cancer Institute to report adverse events occurring in cancer clinical trials. The CTCAE contains a grading scale for each adverse event term representing the severity of the event. NOTE: CTCAE is often used in study adverse event summaries and Investigational New Drug (IND) reports to the Food and Drug Administration. [After NCI]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "CUI (common unique identifier)", "definition": "CDISC Definition: A code used in the Enterprise Vocabulary System (EVS) to link a particular concept across one or more terms.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "curriculum vitae (CV)", "definition": "CDISC Definition: Document that outlines a person's educational and professional history.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "cybersecurity", "definition": "CDISC Definition: Protection of computers, servers, mobile devices, electronic systems, networks, programs, and data from malicious attacks, damage, or unauthorized access, to ensure confidentiality, integrity, and availability of information. [After FDA CDRH; After 17 CFR Part 229.106; After Cybersecurity and Infrastructure Security Agency]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data acquisition", "definition": "CDISC Definition: Capture of data into a structured, computerized format without a human-to-computer interface (i.e., from another measuring instrument or computerized source). Contrast with data entry, electronic data capture.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data capture", "definition": "CDISC Definition: The process of collecting and recording measures and assessments for a specific purpose. NOTE: Data are said to be captured when they are extracted as permanent records for use in a new context or created as a source document in that context. An example would be data that are manually copied or otherwise extracted from an EHR that are then transferred into a clinical trial database to be used for a clinical trial. [After Working with Data, Australian National Data Service, Accessed 4 Sept 2020; AFter FDA Guidance on Use of Electronic Health Record Data in Clinical Investigations Guidance for Industry, July 2018] See also data entry, EDC (electronic data capture).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data clarification form", "definition": "CDISC Definition: A form used to query an investigator and collect feedback to resolve questions regarding data.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data clarification", "definition": "CDISC Definition: Answer supplied by the investigator in response to a query. NOTE: The investigator may supply a new data point value to replace the initial value or a confirmation of the queried data point.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data coding", "definition": "CDISC Definition: The process of transforming raw data into a structured and manageable format, enabling researchers to identify patterns, themes, and relationships within their data. NOTE: It involves assigning labels or alphanumerical codes to different pieces of information based on predefined criteria or categories, or through the use of coding dictionaries such as MedDRA. These codes act as a bridge between the raw data and the analytical phase of research, facilitating the organization, integration, and interpretation of data. [Dr. Sowndarya Somasundaram, Data Coding in Research Methodology - iLovePhD, October 7, 2023, https://www.ilovephd.com/data-coding-in-research-methodology/, Accessed 2024-08-22] See also data, data management, data entry.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data collection instruments", "definition": "CDISC Definition: Documents or tools which are used to collect, record or transcribe information on substantially identical items from a group of respondents. NOTE: Instruments can be either electronic or paper based tests, questionnaires, inventories, interview schedules or guides, rating scales, and survey plans or any other forms. [After 45 CFR 63.32]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data collection", "definition": "CDISC Definition: In the context of clinical research, accessing and recording information that provides source data for analysis and interpretation See data entry and data capture. [CDISC]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data controller", "definition": "CDISC Definition: The entity that determines which, and how, personal data are processed. [Article 4 GDPR Definitions] See also personal data, processing (personal data).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data element identifier", "definition": "CDISC Definition: An identifier that may include information such as the origin of the data element, the date and time of entry, or the identification number of the study subject to whom the data element applies. NOTE: Data element identifiers should be attached to each data element as it is entered or transmitted by the originator into the eCRF. [After body and glossary of FDA Final Guidance eSource]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data element", "definition": "CDISC Definition: Smallest unit of information in a transaction. [From body and glossary of FDA Final Guidance on eSource] See also eXtensible markup language (XML) data element, common data element, clinical study data element.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data encryption standard (DES)", "definition": "CDISC Definition: A FIPS approved cryptographic algorithm for encrypting (enciphering) and decrypting (deciphering) binary coded information. Encrypting data converts it to an unintelligible form called cipher. Decrypting cipher converts the data back to its original form called plaintext. NOTE: Data that are considered sensitive by the responsible authority or data that represent a high value should be cryptographically protected if vulnerable to unauthorized disclosure or undetected modification during transmission or while in storage. [After Federal Information Processing Standards (FIPS) Publication 46-2]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data entry", "definition": "CDISC Definition: Human input of data into a structured, computerized format using an interface such as a keyboard, pen-based tablet, or voice recognition. Contrast with data acquisition, electronic data capture, direct entry. See also data collection, data capture.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data integrity verification", "definition": "CDISC Definition: Process of manually supervised verification of data for internal consistency.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data integrity", "definition": "CDISC Definition: A condition of data reflecting the degree to which the data are complete, consistent, accurate, trustworthy, and reliable at any given time as well as consistently so maintained throughout the data life cycle. NOTE: The data should be collected and maintained in a secure manner, so that they are Attributable, Legible, Contemporaneously recorded, Original (or a true copy) and Accurate (ALCOA). Assuring data integrity requires appropriate quality and risk management systems, including adherence to sound scientific principles and good documentation practices. [After ICH E6; After MHRA GXP Data Integrity Guidance and Defintions, Revision 1, March 2018; After 21 CFR Part 11] See also ALCOA, ALCOA+, ALCOA++, traceability (data), data quality, electronic data transfer.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data interchange", "definition": "CDISC Definition: Transfer of information between two or more parties, which maintains the integrity of the contents of the data for the purpose intended. See also interoperability.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data item", "definition": "CDISC Definition: A named component of a data element. Usually the smallest component [ANSI]. See also data model, data element.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data listing", "definition": "CDISC Definition: Set of observations organized by domain.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data management conventions", "definition": "CDISC Definition: Procedures and policies for data management (e.g., documented procedure(s) for resolving self-evident changes). [ICH E6] See self-evident change.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data management", "definition": "CDISC Definition: Tasks associated with the entry, transfer, and/or preparation of source data and derived items for entry into a clinical trial database. NOTE: Data management could include database creation, data entry, review, coding, data editing, data QC, locking, or archiving; it typically does not include source data capture.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data minimization", "definition": "CDISC Definition: The requirement that personal data must be adequate, relevant, and limited to what is necessary in relation to the purposes for which they are collected and processed. [After GDPR Article 5(1)(c); After Article 4(1)(c) of EU Regulation 2018/1725] See also personal data, processing (personal data).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data model", "definition": "CDISC Definition: Unambiguous, formally stated, expression of items, the relationship among items, and the structure of the data in a certain problem area or context of use. A data model uses symbolic conventions agreed to represent content so that content does not lose its intended meaning when communicated.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data monitoring committee (DMC)", "definition": "CDISC Definition: A group of independent experts who are appointed to monitor the safety and scientific integrity of a research intervention, protect the confidentiality of participant data, and to make recommendations to the sponsor regarding the stopping of the trial for safety, efficacy, or for futility. [After clinicaltrials.gov; Committee for Medicinal Products for Human Use (CHMP), 2005, EMA; FDA Establishment and Operation of Clinical Trial Data Monitoring Committees. March 2006]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data monitoring", "definition": "CDISC Definition: Process by which clinical data are examined for completeness, consistency, and accuracy for the duration of the study lifecycle. NOTE: Monitoring is undertaken by qualified study personnel following a specific process and auditable methods. See also ALCOA+", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data origin", "definition": "CDISC Definition: Source of information collected in the course of a clinical trial, specifically used to differentiate between data as collected versus data that are derived or calculated. NOTE: In CDISC, a metadata attribute defined for each dataset variable in the Define.xml document of an SDTM submission that refers to the source of a variable (e.g., CRF, derived, sponsor defined, PRO, etc.). See also data element originator.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data originator", "definition": "CDISC Definition: Metadata characterizing the entity creating a data element in an eCRF for a clinical investigation. NOTE: Per FDA Final Guidance on eSource, \"Each data element is associated with an origination type that identifies the source of its capture in the eCRF. This could be a person, a computer system, a device, or an instrument that is authorized to enter, change, or transmit data elements into the eCRF (also sometimes known as an author).\" See also data element, data element originator, origin. [CDISC, Note is from FDA Final Guidance on eSource]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data processor", "definition": "CDISC Definition: Processes personal data as instructed by a data controller to support the relevant processing activities on a data controller's behalf. [Article 4 GDPR Definitions] See also personal data, processing (personal data).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data quality", "definition": "CDISC Definition: A dimension of data contributing its trustworthiness and pertaining to accuracy, sensitivity, validity, and suitability to purpose. NOTE: Key elements of data quality include attribution, legibility (decipherable, unambiguous), contemporaneousness, originality (i.e., not duplicated), accuracy (ALCOA), precision, completeness, consistency (logical, not out of range), and those who have modified the data. Scientists may reasonably trust data that are accurate (high quality) that have also been reviewed by investigators and protected from unauthorized alteration (high integrity). [After ICH E6; After MHRA GXP Data Integrity Guidance and Defintions, Revision 1, March 2018; After 21 CFR Part 11] See also ALCOA, ALCOA+, ALCOA++, traceability (data), data integrity, electronic data transfer.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data security", "definition": "CDISC Definition: Degree to which data are protected from the risk of accidental or malicious alteration or destruction and from unauthorized access or disclosure. [After US FDA 21 CFR Part 11] See also personal data, processing (personal data).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data selection criteria", "definition": "CDISC Definition: The rules by which particular data are selected and/ or transferred between the point of care and the patient record; subsequently, from the patient record to the database; and from database to inclusion in sub-population analyses.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data sharing", "definition": "CDISC Definition: Providing clinical trial data or access to data and final results to key stakeholders with the goal of increasing scientific knowledge and ultimately better therapies for patients. NOTE: guiding principles for data sharing: (1) maximize the benefits of clinical trials while minimizing the risks or harm of sharing clinical trial data, (2) respect individual participants whose data are shared, (3) increase public trust in clinical trials and the sharing of trial data, and (4) conduct the sharing of clinical trial data in a fair manner. [After National Academies of Sciences, Institute of Medicine. Sharing Clinical Trial Data: Maximizing Benefits, Minimizing Risk. Washington, DC: National Academies Press, 2015, accessed 2022-09-07]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data standards", "definition": "CDISC Definition: Defined rules, conventions, guidelines, characteristics, methods, formats, and terminologies that provide structure and consistency for exchange and utilization of data. NOTE: Data standards may describe the elements and relationships necessary to achieve the unambiguous exchange of data between disparate information systems. [After https://www.fda.gov/media/124694/download Standards Development and the Use of Standards in Regulatory Submissions Reviewed in the Center for Biologics Evaluation and Research Guidance for Industry MARCH2019, NCI Thesaurus]. See also interoperability, standard, CDISC standards, Study Data Standardization Plan, and Standards Development Organization.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data storage", "definition": "CDISC Definition: To maintain data by placing the data, or a copy of the data, onto an electronically accessible device for preservation (either in plain-text or encrypted format). [HL7 eHR-s FM Glossary of Terms, 2010].", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data subject", "definition": "CDISC Definition: In the context of privacy guidelines, An individual who is the subject of personal data, persons to whom data refers, and from whom data are collected, processed, and stored. [after ISO/TS 2537:2008; and EU GDPR] See also study participant, participant.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data transformations", "definition": "CDISC Definition: Algorithmic operations on data or data sets to achieve a meaningful set of derived data for analysis. [ADaM] See also derived variable.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data type", "definition": "CDISC Definition: Data types define the structural format of the data carried in the attribute and influence the set of allowable values an attribute may assume. [HL7]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data validation", "definition": "CDISC Definition: Process used to determine whether data are accurate, authentic, complete, and/or compliant with applicable standards, rules, and conventions. NOTE: The process may include format checks, completeness checks, check key tests, reasonableness checks, and limit checks. [After FDA.; ISO] See also data integrity, validation.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "data", "definition": "CDISC Definition: Representations of facts, concepts, or instructions in a manner suitable for communication, interpretation, or processing by humans or by automated means. [FDA]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "database lock", "definition": "CDISC Definition: Action taken to prevent further changes to a clinical trial database or any equivalent clinical data storage system. NOTE: Locking of a database is done after review, query resolution, and a determination has been made that the database is ready for analysis.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "database", "definition": "CDISC Definition: A collection of data or information, typically organized for ease and speed of search and retrieval.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "dataset", "definition": "CDISC Definition: A collection of structured data in a single file. [CDISC] Compare to analysis dataset, tabulation dataset.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "date of first enrollment", "definition": "CDISC Definition: Date or date and time of first subject enrollment into a study, as verifiable by a convention that is consistent with authoritative regulatory criteria. [Modified from ICH E3] Compare to study start date.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "de-identification", "definition": "CDISC Definition: The process of removing potentially identifying data or data elements to render data into a form that does not identify individuals and where identification is not likely to take place. NOTE: A general term for a process of removing the association between a set of identifying data and the data subject. Examples of potentially identifying data include name, birth date, social security number, home address, telephone number, e-mail address, medical record numbers, health plan beneficiary numbers, full-face photographic images). [After ISO/TS 25237: 2008 - Health Informatics - Pseudonymization; HIPAA: 45 CFR, 164.514] See also anonymization.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "de-identified information", "definition": "CDISC Definition: Records that have had enough personally identifiable information removed or obscured such that the remaining information does not identify an individual, and there is no reasonable basis to believe that the information can be used to identify an individual. [Guide to Protecting Personally Identifiable Information (PII): Special Publication NIST pubs/800-122]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "decentralized clinical trial (DCT)", "definition": "CDISC Definition: A trial in which data capture, administration of medication, and possibly other procedures are done at the subject's location, e.g., at home or by telemedicine, mobile technology, and local HCPs (like family physicians, general practitioners). NOTE: The procedures (entry of data, medical tests, clinical evaluations, objective measures, observations) for capturing safety and efficacy measurements and observations may be done in-person by a traveling clinician or nurse so DCTs are not necessarily virtual. The responsibility for preparation, maintenance and retention of source records may be allocated to a centralized investigator or sponsor investigator. [After CTTI Recommendations: Decentralized Clinical Trials, September 2018] See also remote clinical trial, virtual, visit.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "decision rule", "definition": "CDISC Definition: Succinct statement of how a decision will be reached based upon the expected foreseen clinical benefits in terms of outcomes of the primary endpoint. [FDA documentation]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Declaration of Helsinki", "definition": "CDISC Definition: A set of recommendations or basic principles that guide medical doctors in the conduct of biomedical research involving human subjects. it was originally adopted by the 18th World Medical assembly (Helsinki, Finland, 1964) and recently revised (64th WMA General Assembly, Fortaleza, Brazil, October 2013).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "deep learning", "definition": "CDISC Definition: A subset of machine learning that is part of the broader family of machine learning methodologies based on artificial neural networks. A deep neural network has multiple layers between input and output layers to progressively extract higher level features from the raw input. [After DeepAI Machine Learning Glossary and Terms] See also machine learning, artificial intelligence (AI).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Define-XML", "definition": "CDISC Definition: A table in XML that transmits metadata that describes any tabular dataset structure. NOTE: When used with the CDISC content standards, it provides the metadata for human and animal model tabular datasets such as SDTM, SEND, and ADaM. [After CDISC.org] See also eXtensible markup language (XML) data element, XML (eXtensible Markup Language).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "demographic data", "definition": "CDISC Definition: Characteristics of subjects or study populations, which include such information as age, sex, family history of the disease or condition for which they are being treated, and other characteristics relevant to the study in which they are participating.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "dependent variable", "definition": "CDISC Definition: A variable that is expected to change as a result of an experiment. Dependent variables are influenced by independent variables. [After AMA Manual of Style] See also independent variable.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "deployment", "definition": "CDISC Definition: Readying an electronic clinical trial system for field use by providing or disseminating capture devices, tokens, or passwords for users of an activated system. See activation.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "derived variable", "definition": "CDISC Definition: New variable created as a function of existing variables and/or application of mathematical functions. See also variable, raw data.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "design configuration", "definition": "CDISC Definition: Clinical trial design developed to compare treatment groups in a clinical trial. NOTE: The configuration usually requires randomization to one or more treatment arms, each arm being allocated a different (or no) treatment. examples include: Parallel Group Design, Crossover Design, Factorial Designs. [After ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "development plan", "definition": "CDISC Definition: An ordered program of clinical trials, each with specific objectives. [adapted from ICH E9, see ICH E8]. See also clinical development plan.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "diagnose", "definition": "CDISC Definition: A process to identify the disease, condition, or injury that explains the symptoms and signs occurring in a patient. NOTE: The information required for diagnosis is collected from a history and physical examination of the patient and preferably confirmed by one or more diagnostic procedures such as laboratory tests, radiologic studies and other technical investigations. [After \\\"Making a diagnosis\\\", John P. Langlois, Chapter 10 in Fundamentals of clinical practice (2002). Mark B. Mengel, Warren Lee Holleman, Scott A. Fields. 2nd edition.] See also treatment, intervention, disease, sign, symptom.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "diagnostic device", "definition": "CDISC Definition: A class of medical devices intended to provide evidence for diagnosis. [After Regulation (EU) 2017/745; After US FDA, Referencing the Definition of \"Device\" in the Federal Food, Drug, and Cosmetic Act in Guidance, Regulatory Documents, Communications, and Other Public Documents, Nov 14, 2022] See also medical device, investigational device, in vitro diagnostic device.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "DIBD (development international birth date)", "definition": "CDISC Definition: The sponsor's first authorization to conduct a clinical trial in any country worldwide. NOTE: Used to start the annual period for the Development Safety Update Report (DSUR). [After CIOMS VII; ICH E2F]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "digital signature", "definition": "CDISC Definition: An electronic signature, based on cryptographic methods of originator authentication, computed by using a set of rules and a set of parameters, such that the identity of the signer and the integrity of the data can be verified. [21 CFR 11]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "direct access", "definition": "CDISC Definition: Permission to examine, analyze, verify, and reproduce any records and reports that are important to evaluation of a clinical trial. NOTE: The party (e.g., domestic and foreign regulatory authorities, sponsor's monitors and auditors) with direct access should take all reasonable precautions within the constraints of the applicable regulatory requirement(s) to maintain the confidentiality of subjects' identities and sponsor's proprietary information. [ICH E6 Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "direct entry", "definition": "CDISC Definition: Recording of data by human or automated action where an electronic record is the original means of capturing the data into an electronic records system without a paper source document. Examples are an individual keying original observations into a system or the automatic recording into the system of the output from measuring devices such as a balance that measures subject's body weight or an ECG machine. Compare to data entry, data acquisition.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "direct identifier", "definition": "CDISC Definition: A piece of data that can be used to uniquely identify an individual (e.g., name, patient ID, social security number, exact address, telephone number, e-mail address, government issued identifiers, passport/VISA numbers) either without additional information or with cross-linking through other information that is in the public domain. [After PhUSE De-identification Standard for SDTM 3.2, version 1.0.1.]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "discontinuation", "definition": "CDISC Definition: The act of concluding participation by an enrolled subject prior to completion of all protocol-required elements in a study. NOTE: Four categories of discontinuation are distinguished: a) dropout: Active discontinuation by a subject (also a noun referring to such a discontinued subject); b) investigator initiated discontinuation (e.g., for cause); c) loss to follow-up: cessation of participation without notice or action by the subject; d) sponsor initiated discontinuation. Note that subject discontinuation does not necessarily imply exclusion of subject data from analysis. \"Termination of subject\" has a history of synonymous use, but is now considered nonstandard. [After ICH E3, section 10.1 and FDA Guidance for Industry: Submission of Abbreviated Reports & Synopses in Support of Marketing Applications, IV A] See also withdrawal.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "discrepancy", "definition": "CDISC Definition: The failure of a data point to pass a validation check. NOTE: Discrepancies may be detected by computerized edit checks or observed/ identified by the data reviewer as a result of manual data review. See also query.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "disease", "definition": "CDISC Definition: Any abnormal condition of the body or mind that causes discomfort, dysfunction, or distress to the affected person. NOTE: The term is often used broadly to include injuries, disabilities, syndromes, symptoms, deviant behaviors, and atypical variations of structure and function. [After NCI Thesaurus] See also diagnosis.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "disease-free survival", "definition": "CDISC Definition: The length of time after treatment for a specific disease during which a patient survives with no sign of recurrence of the disease. [NCI]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "document (HL7)", "definition": "CDISC Definition: An ordered presentation of XML elements, possibly including text and tabular analyses, description, and figures. Descriptors for HL7 documents include type, class, and element. NOTE: In HL7, a document can be either physical (referring to the paper) or logical (referring to the content) with the following characteristics: 1) Stewardship; 2) Potential for authentication; 3) Wholeness; 4) Human readability; 5) Persistence; 6) Global vs. local context.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "document root", "definition": "CDISC Definition: The element in an XML document that contains all other elements; the first element in the document. [SPL Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "document type definition (DTD)", "definition": "CDISC Definition: XML specification for content and presentation of data and text in a document including definitions for the elements considered to be legal in the document. NOTE: Agreeing on a common DTD facilitates interoperability among systems incorporating the agreed standards. [From Electronic Submission File Formats and Specifications. Orientation and Best Practices For Data Formats and Submission to The Center For Tobacco Products. January 2018; Providing Regulatory Submissions in Electronic Format - Certain Human Pharmaceutical Product Applications and Related Submissions Using the eCTD Specifications Guidance for Industry. January 2019]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "documentation", "definition": "CDISC Definition: All records, in any form (including but not limited to written, electronic, magnetic, and optical records, and scans, x-rays, and electrocardiograms) that describe or record the methods, conduct, and/or results of a trial, the factors affecting a trial, and the actions taken. [ICH E6 Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "domain name", "definition": "CDISC Definition: The way a particular web server is identified on the internet. For example, www.fda.gov names the World Wide Web (www) server for the Food and Drug administration, which is a government (.gov) entity. [Center for advancement of Clinical Research]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "domain", "definition": "CDISC Definition: A collection of logically related observations with a common, specific topic that are normally collected for all subjects in a clinical investigation. NOTE: The logic of the relationship may pertain to the scientific subject matter of the data or to its role in the trial. Example domains include laboratory test results (LB), adverse events (AE), concomitant medications (CM). [After SDTM Implementation Guide version 3.2, CDISC.org] See also general observation class.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "dosage form", "definition": "CDISC Definition: Physical characteristics of a drug product, (e.g., tablet, capsule, or solution) that contains a drug substance, generally, but not necessarily, in association with one or more other ingredients. [After 21 CFR 314.3; After IDMP] See also drug product.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "dosage regimen", "definition": "CDISC Definition: The schedule of doses of an agent per unit of time, including the number of doses per given time period and the elapsed time between doses. NOTE: For example, every six hours or the time that the doses are to be given (for example, at 8 a.m. and 4 p.m. daily); and/or the amount of a medicine (the number of capsules, for example) to be given at each specific dosing time. [After AMA Manual of Style]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "dosage", "definition": "CDISC Definition: The amount of drug administered to a patient or test subject over a period of time; a regulated time bound administration of individual doses. NOTE: For example, a daily dosage specified in a prescription or a clinical trial, such as one 100mg tablet taken 4 times per day. [After AMA Manual of style]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "dose strength", "definition": "CDISC Definition: The strength of a drug product, which indicates the amount of each active ingredient in a single dose. For liquids, it is the proportion of each active substance to the volume of a liquid dosage form. [After FDA Glossary of Terms]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "dose", "definition": "CDISC Definition: Specified quantity of a medicine, to be taken at one time or at stated intervals. [ISO 11615:2012 Health Informatics]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "dose-escalation trial", "definition": "CDISC Definition: A study in which the dosage of the test article is increased until the desired physiological effect or toxicity is seen. (CDISC; After ICH E4)", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "double-blind study", "definition": "CDISC Definition: A study in which neither the subject nor the investigator nor the research team interacting with the subject or data during the trial knows the treatment a subject is receiving. [After FDA Glossary of Terms]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "double-dummy", "definition": "CDISC Definition: A technique for retaining the blind when administering supplies in a clinical trial, when the two treatments cannot be made identical. supplies are prepared for Treatment a (active and indistinguishable placebo) and for Treatment B (active and indistinguishable placebo). subjects then take two sets of treatment; either a (active) and B (placebo), or a (placebo) and B (active). [ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "dropout", "definition": "CDISC Definition: A subject in a clinical trial who for any reason fails to continue in the trial until the last visit or observation required of him/her by the study protocol. [from ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "drug development process", "definition": "CDISC Definition: The program for advancing an investigational product from preclinical studies through approval for marketing following review by regulatory agencies.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "drug distribution", "definition": "CDISC Definition: In pharmacokinetics, the processes that control transfer of a drug from the site of measurement to its target and other tissues. See also ADME.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "drug interaction", "definition": "CDISC Definition: Changes in a drug's effects due to recent or concurrent use of another drug or drugs (drug-drug interactions), ingestion of food (drug-nutrient interactions), or ingestion of dietary supplements (dietary supplement-drug interactions). [MSD Manual Professional Version. Drug Interactions. By Shalini S. Lynch , PharmD, University of California San Francisco School of Pharmacy. Reviewed/Revised Jul 2022; Modified Sep 2022. Merck & Co, Inc., Rahway, NJ, USA] See also additive effect, synergistic effect, antagonistic effect.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "drug product", "definition": "CDISC Definition: A finished dosage form, for example, tablet, capsule, solution, etc., that contains an active drug ingredient generally, but not necessarily, in association with inactive ingredients. The term also includes a finished dosage form that does not contain an active ingredient but is intended to be used as a placebo. [21 CFR 210.3] See also medicinal product, dosage form.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "drug treatment", "definition": "CDISC Definition: A drug given to a patient to mitigate or cure an illness, injury, or reduced health status. See also drug, treatment, intervention.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "drug", "definition": "CDISC Definition: An active natural, synthetic or semi-synthetic ingredient including endogenous body substance that is intended to furnish pharmacological activity or other direct effect in the diagnosis, cure, mitigation, treatment, or prevention of disease or to affect the structure or any function of the human body, but does not include intermediates used in the synthesis of such ingredient [21 CFR 314.3(b)]. See also medicinal product, active substance.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "dynamic HTML", "definition": "CDISC Definition: Collective term for a combination of tags and options, style sheets, and programming that allows users to create web pages in hypertext Mark-up language (HTML) that are more responsive to user interaction than previous versions of HTML.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Early Phase I", "definition": "CDISC Definition: Originally described as an exploratory study with no safety or efficacy targets. It is not cited in current FDA guidance and no longer in common usage. See also phase.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "early termination of trial", "definition": "CDISC Definition: The premature end of a clinical trial due to any reason before the conditions specified in the protocol are complied with. [REGULATION (EU) No 536/2014 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 16 April 2014 on clinical trials on medicinal products for human use, and repealing Directive 2001/20/EC; ICH E6] See also termination (of a clinical trial).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "eCertified copy", "definition": "CDISC Definition: A copy of an electronic record that is created through the application of a process validated to preserve the data and metadata of the original and where the validation of the process is certified by the dated signature of an authorized person. [CDISC, after EMA/INS/GCP/454280/2010 GCP Inspectors Working Group (GCP IWG) June 2010]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "eClinical trial", "definition": "CDISC Definition: Clinical trial in which primarily electronic processes are used to plan, collect (acquire), access, exchange, and archive data required for conduct, management, analysis, and reporting of the trial. NOTE: FDA has recently drawn a distinction between studies and trials. Both words refer to systematic efforts to obtain evidence relevant to regulatory authorities, but, depending on regulatory context and particularly in the case of postmarketing commitments, a study might not be the appropriate word for a clinical trial (prospective, controlled, randomized), but should be reserved instead for surveillance, structured gathering of information, epidemiological studies, or even animal studies [Guidance for industry Postmarketing studies and Clinical Trials-implementation of section 505(o) of the Federal Food, Drug, and Cosmetic act]. See also clinical study, clinical trial.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "eConsent form", "definition": "CDISC Definition: An electronic document explaining all the relevant information to assist an individual in understanding the expectations and risks in making a decision about a procedure. This document is presented to and signed by the individual or guardian. [NCI] See also informed consent, consent form, electronic signature.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "eCRF (electronic case report form)", "definition": "CDISC Definition: An auditable electronic record of information that is reported to the sponsor (or sponsor's agent such as an EDC provider) on each trial subject to enable data pertaining to a clinical investigation protocol to be systematically captured, reviewed, managed, stored, analyzed, and reported. The eCRF is a CRF in which related data items and their associated comments, notes, and signatures are linked programmatically. See also case report form, CRF, eSRF.[CSUICI; Revised from FDA Final Guidance on eSource]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "eCRT (electronic case report tabulation)", "definition": "CDISC Definition: Case report tabulation (CRT) provided in electronic format for eSubmissions (electronic regulatory submissions). NOTE: according to FDA guidance, eCRTs are datasets provided as SAS Transport files with accompanying documentation in electronic submissions. They enable reviewers to analyze each dataset for each study. Each CRF domain should be provided as a single dataset; however, additional datasets suitable for reproducing and confirming analyses may also be needed. SDTM is the preferred format.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "EDC (electronic data capture)", "definition": "CDISC Definition: The process of collecting clinical trial data into a permanent electronic form. NOTE: Permanent in the context of these definitions implies that any changes made to the electronic data are recorded with an audit trail. EDC usually denotes manual entry of CRF data by transcription from source documents. The transcription is typically done by personnel at investigative sites. [After Guidance for Industry, Use of Electronic Health Record Data in Clinical Investigations, July 2018] See also data entry, direct data entry, data acquisition, data capture.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "edit check", "definition": "CDISC Definition: An auditable process, usually automated, of assessing the content of a data field against its expected logical, format, range, or other properties that is intended to reduce error. NOTE: Time-of-entry edit checks are a type of edit check that is run (executed) at the time data are first captured or transcribed to an electronic device at the time entry is completed of each field or group of fields on a form. Back-end edit checks are a type that is run against data that has been entered or captured electronically and has also been received by a centralized data store.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "EDR (electronic document room)", "definition": "CDISC Definition: The electronic document room is an extension of the e-Submissions central document room. A check is performed on each submission sent to the EDR for file formats used and the integrity of bookmarks and hypertext links.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "effect size", "definition": "CDISC Definition: A measure or estimate of the observed or expected change in an outcome as a result of an intervention. [After AMA Manual of Style]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "effectiveness", "definition": "CDISC Definition: A measure of intended effect on the disease or condition based on regulatory determination made on the basis of clinical efficacy and other substantial evidence, including real-world observations. [After Providing Clinical Evidence of Effectiveness for Human Drug and Biological Products. FDA GUIDANCE DOCUMENT. MAY 1998. After Demonstrating Substantial Evidence of Effectiveness for Human Drug and Biological Products. FDA Guidance for Industry (DRAFT GUIDANCE). December 2019] See also efficacy.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "efficacy", "definition": "CDISC Definition: A measure of intended effect on the disease or condition based on adequate and well-controlled clinical trials. [After Providing Clinical Evidence of Effectiveness for Human Drug and Biological Products. FDA GUIDANCE DOCUMENT. MAY 1998. After Demonstrating Substantial Evidence of Effectiveness for Human Drug and Biological Products. FDA Guidance for Industry (DRAFT GUIDANCE). December 2019] See also effectiveness.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "EHR (electronic health record)", "definition": "CDISC Definition: An electronic record for healthcare providers to create, import, store, and use clinical information for patient care, according to nationally recognized interoperability standards. NOTE: The EHR has the following distinguishing features: able to be obtained from multiple sources; shareable; interoperable; accessible to authorized parties. [After National Office of Health Information Technology-HIT, USHHS]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "electronic data transfer", "definition": "CDISC Definition: The exchange of data across computer systems and networks, taking into account all required quality aspects such as security, data privacy, data quality, data integrity, and audit trail. [After FDA 21 CFR Part 11; After Guideline on computerized systems and electronic data in clinical trials (europa.eu) EMA/INS/GCP/112288/2023] See also data security, data quality, data integrity, data entry, ALCOA++.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "electronic personal health record (ePHR)", "definition": "CDISC Definition: An electronic record for individuals to create, import, store, and use clinical information to support their own health.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "electronic record", "definition": "CDISC Definition: Any combination of text, graphics, data, audio, pictorial, or other information representation in digital form that is created, modified, maintained, archived, retrieved, or distributed by a computer system. [21 CFR 11.3(b) (6)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "electronic signature", "definition": "CDISC Definition: A computer data compilation of any symbol or series of symbols, executed, adopted, or authorized by an individual to be the legally binding equivalent of the individual's handwritten signature. [CSUICI; 21 CFR 11.3(7); After US FDA Guidance for Industry, Part 11, Electronic Records; Electronic Signatures - Scope and Application, Aug 2003] See also eConsent form.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "eligibility criteria", "definition": "CDISC Definition: Requirements that must be met for a person to be included in a study, which help make sure that the results of the study are caused by the intervention being tested and not by other factors. NOTE: Eligibility Criteria, including inclusion and exclusion criteria, should enable constitution of the targeted cohorts in the clinical study. [After NCI's Dictionary of Cancer Terms]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "emergency use authorization", "definition": "CDISC Definition: Approval by FDA for the emergency use of certain unapproved medical products or an unapproved use of an approved medical product for certain emergency circumstances, when applied for under a declared health emergency. These medical products may be referred to as medical countermeasures (MCMs) and may include drugs, biologics, and devices. [After Emergency Use Authorization of Medical Products and Related Authorities. FDA Guidance for Industry and Other Stakeholders. January 2017.] See also pre-approval access.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "EMR (electronic medical record)", "definition": "CDISC Definition: An electronic record for healthcare providers within one healthcare organization to create, store, and use clinical information for patient care. An electronic record derived from a computerized system used primarily for delivering patient care in a clinical setting. NOTE: EMRs (or EHRs) may serve as source documents, and such data could serve also as source data for clinical trials provided that the controls on the EMR system and the transfer of such data to the eClinical trial system were to fulfill regulatory requirements (e.g., 21 CFR 11). [After Guidance for Industry, Use of Electronic Health Record Data in Clinical Investigations, July 2018]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "end-point assessment medicinal product", "definition": "CDISC Definition: Medicinal products given to the subject as an aid to assess a relevant clinical trial end-point; it is not being tested or used as a reference in the clinical trial. [After Recommendations from the expert group on clinical trials for the implementation of Regulation (EU) No 536/2014' dd 28 June 2017]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "endemic disease", "definition": "CDISC Definition: The constant presence of a disease or infectious agent within a given geographic area or population group; may also refer to the usual prevalence of a given disease within such area or group. [A dictionary of epidemiology, edited for the International Epidemiological Association by John M. Last, Oxford University Press 2001]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "endpoint", "definition": "CDISC Definition: A defined variable intended to reflect an outcome of interest to address a particular research question. NOTE: A precise definition of an endpoint typically specifies the type of assessments made, the timing of those assessments, the assessment tools used, and possibly other details, as applicable, such as how multiple assessments within an individual are to be combined. Primary endpoints are usually statistically analyzed. [After BEST Resource] See also outcome, variable, surrogate endpoint.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "enrolled", "definition": "CDISC Definition: Status assigned to a subject who agrees to participate in a study, following completion of the informed consent process and meeting eligibility criteria as specified in the protocol. NOTE: Enrollment routinely requires verification of eligibility and inclusion in the analysis database. A less common definition confers enrolled status at the signing of an informed consent form, e.g., Clinicaltrials.gov. See also informed consent, enrollment.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "enrollment (cumulative)", "definition": "CDISC Definition: Current enrollment including any subjects who were once enrolled and have ended participation.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "enrollment (current)", "definition": "CDISC Definition: Subjects actively continuing to participate in a clinical trial as of the current date.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "enrollment", "definition": "CDISC Definition: The action of enrolling one or more subjects. NOTE: The subject will have met the inclusion/exclusion criteria to participate in the trial and will have signed an informed consent form. [After Glossary Of Terms On Clinical Trials For Patient Engagement Advisory Committee Meeting] See also enrolled.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "epidemic", "definition": "CDISC Definition: The occurrence in a community or region of cases of an illness, specific health-related behavior, or other health-related events clearly in excess of normal expectancy. NOTE: The community or region and the period in which the cases occur are specified precisely. The number of cases indicating the presence of an epidemic varies according to the agent, size, and type of population exposed; previous experience or lack of exposure to the disease; and time and place of occurrence. [After A dictionary of epidemiology, edited for the International Epidemiological Association by John M. Last, OXFORD UNIVERSITY PRESS 2001]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "epoch", "definition": "CDISC Definition: Planned interval of time in the conduct of a study wherein an activity is specified and consistent, e.g., specific treatment dose or study activity such as Screening. NOTE: A CDISC variable used in the SDTM model to indicate a time period defined in the protocol with a study-specific purpose. See also arm, visit, phase (within a study).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "ePRO", "definition": "CDISC Definition: Patient reported outcome data initially captured electronically. NOTE: Usually ePRO data is captured as eSource. [DIA ePRO Working Group]. See also patient reported outcome, PRO, eSource.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "equipoise", "definition": "CDISC Definition: A state in which an investigator is uncertain about which arm of a clinical trial would be therapeutically superior for a patient. NOTE: An investigator who has a treatment preference or finds out that one arm of a comparative trial offers a clinically therapeutic advantage should disclose this information to subjects participating in the trial.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "equivalence trial", "definition": "CDISC Definition: A trial with the primary objective of showing that the response to two or more treatments differs by an amount that is clinically unimportant. NOTE: This is usually demonstrated by showing that the true treatment difference is likely to lie between a lower and an upper equivalence margin of clinically acceptable differences.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "equivalent effect", "definition": "CDISC Definition: An effect of two or more bioactive compounds or drugs that is deemed to be equal, and can be expected to have the same clinical effect and safety profile. [After US FDA, Evaluation of Therapeutic Equivalence, Draft Guidance for Industry, July 2022]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "eSource data", "definition": "CDISC Definition: Source data captured initially into a permanent electronic record (eSource document) used for the reconstruction and evaluation of a clinical study or a source data item included in an eCRF when direct entry is made. NOTE: permanent in the context of these definitions implies that any changes made to the electronic data are recorded via an audit trail. See also eSource document, source data, permanent data, data originator. [From body of FDA Final Guidance on eSource]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "eSource document", "definition": "CDISC Definition: Electronic record containing source data for a clinical trial, used to aggregate a particular instance of eSource data items for capture, transmission, storage, and/ or display, and serving as a source document for a clinical investigation. NOTE: Electronic Source documents are recorded in electronic systems according to conventions (such as those for PDF documents) that ensure that all the fields of eSource data and associated contextual information (e.g. time of capture, time zone, authorship, origin, signatures, revisions, etc.) are linked to each other in a particular structure for presentation. The encoded specifications in the electronic record thus serve the same role as have the physical properties of paper (binding data items together). eSource documents are subject to regulations and guidance that apply to source documents. See also source documents. [relevant to FDA Final Guidance on eSource]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "eSource", "definition": "CDISC Definition: Source record that is electronic. See also source, electronic record.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "eSRF (electronic source report form)", "definition": "CDISC Definition: The human-readable rendering of an electronic record serving as an eSource document that is part of a case history. The eSRF supports capture, transmission, storage, editing and/ or display of eSource documents (original records and certified copies of original records of clinical findings, observations, or other activities in a clinical investigation) used for reconstructing and evaluating the investigation. NOTE: Intended use distinguishes eCRF and eSRF. The eCRF is for capture, review and editing of protocol data belonging to the sponsor; the eSRF is for the human-readable representation of the eSource document for review or to maintain the eSource document that is part of the case history under 21CFR312.62. See also eCRF, eSource document. [CDISC, relevant to FDA Final Guidance on eSource]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "essential documents", "definition": "CDISC Definition: Documents that individually and collectively permit evaluation of the conduct of a study and the quality of the data produced. [ICH E6 Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "established name", "definition": "CDISC Definition: The official name of a drug or pharmaceutical product, relevant in US regulations. [US FDA, 21 CFR 299.4] See also proprietary name, generic name, international nonproprietary name (INN), medicinal product name, compendial name.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "estimand", "definition": "CDISC Definition: A precise description of the treatment effect reflecting the clinical question posed by a given clinical trial objective. It summarizes at a population level what the outcomes would be in the same patients under different treatment conditions being compared. NOTE: The four characteristics of an estimand include the definition of the target study population, statement of the endpoint of interest, intercurrent event details, and the population level summary of the variable of interest. (ICH E9 R1 Addendum; After Estimand Framework: What it is and Why You Need it. Applied Clinical Trials. February 27, 2020]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "ethics committee", "definition": "CDISC Definition: Group convened to protect research subjects. NOTE: Such bodies, depending on the country or region, are abbreviated as: CCI, CCPPRB, CHR, CPPHS, CRB, EAB, HEX, HSRC, LREC, MREC, NIRB, NRB, and REB. See also institutional review board, independent ethics committee.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "ethnicity", "definition": "CDISC Definition: Denotes social groups with a shared history, sense of identity, geography, and cultural roots.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "European Medicines Agency (EMA)", "definition": "CDISC Definition: The regulatory agency for the EU.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "evaluable (for efficacy and safety)", "definition": "CDISC Definition: Pertains to data or subjects that meet Statistical Analysis Plan criteria for inclusion in efficacy/safety datasets.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "event", "definition": "CDISC Definition: Planned protocol activities such as randomization and study completion, and occurrences, conditions, or incidents independent of planned study evaluations occurring during the trial (e.g., adverse events) or prior to the trial (e.g., medical history). [After SDTM, www.cdisc.org] See also general observation class, intervention, finding.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "exclusion criteria", "definition": "CDISC Definition: List of characteristics in a protocol, any one of which may exclude a potential subject from participation in a study.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "excretion", "definition": "CDISC Definition: The act or process of eliminating waste products from the body. See also ADME.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "expansion cohort trial", "definition": "CDISC Definition: A predominantly First-in-Human (FIH) trial with a single protocol with an initial dose-escalation phase followed by three or more additional subject cohorts with cohort-specific objectives. NOTE: The objectives of these expansion cohorts can include assessment of antitumor activity in a disease-specific setting, assessment of a dose with acceptable safety in specific populations (e.g., pediatric or elderly subjects, subjects with organ impairment, subjects with specific tumor types), evaluation of alternative doses or schedules, establishment of dose and schedule for the investigational drug administered with another oncology drug, or evaluation of the predictive value of a potential biomarker. In general, comparison of activity between cohorts is not planned except when a prespecified randomization and analysis plan are part of the protocol design. [FDA Guidance: Expansion Cohorts: Use in First-in-Human Clinical Trials to Expedite Development of Oncology Drugs and Biologics Guidance for Industry. March 2022]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "experimental intervention", "definition": "CDISC Definition: The drug, device, therapy, procedure, or process under investigation in a clinical study that is believed to have an effect on outcomes of interest in a study (e.g., health-related quality of life, efficacy, safety, pharmacoeconomics). NOTE: This does not include comparators or placebos. [After https://grants.nih.gov/grants/policy/faq_clinical_trial_definition.htm#5224; https://grants.nih.gov/policy/clinical-trials/protocol-template.htm] See also test articles, devices, drug product, combination product, treatment, diagnosis, investigational medicinal product.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "experimental unit", "definition": "CDISC Definition: A physical entity which is the primary interest in a specific research objective. NOTE: Depending on the research objectives, a single study may have multiple levels of experimental units. Commonly the individual study subject (animal, person or product) is the experimental unit. (BRIDG v5.3)", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "exploratory IND study", "definition": "CDISC Definition: A clinical study that is conducted early in Phase 1; involves very limited human exposure and has no therapeutic or diagnostic intent (e.g., screening studies, microdose studies) [FDA Guidance for industry, investigators, and Reviewers: exploratory IND studies, January 2006] See also Phase 0.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "exploratory objective", "definition": "CDISC Definition: Additional scientific question(s) within the study that enable further discovery research, beyond the primary and secondary objectives. See also objective, primary objective, secondary objective.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "exploratory study", "definition": "CDISC Definition: Phase 1 or 2 study during which the actions of a therapeutic intervention are assessed and measured. NOTE: Procedures in exploratory studies may appropriately be altered beyond the standard adequate and well controlled processes to expand the scope or method of investigation. [NOTE: After FDA eCOA Glossary] Compare to confirmatory study.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "exposure (individual)", "definition": "CDISC Definition: The result of an intentional contact (e.g., intervention, dosage, drug/product use, misuse, or abuse) or an unintentional contact (circumstantial events leading to unknown, inadvertent, or accidental contact) resulting in inputs to the body of an individual which can occur directly through primary bodily contact routes or indirectly through secondary contact routes (such as via fluids as in fetal exposure during pregnancy or lactation/breast feeding or other biological transfers). [After FDA, Reviewer Guidance Evaluating the Risks of Drug Exposure in Human Pregnancies] See also exposure, intervention, extent of exposure.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "exposure", "definition": "CDISC Definition: Contact between an agent and a target. A state of contact or close proximity to a medicinal product, chemical, pathogen, radioisotope or other substance. NOTE: Types of exposure may be described by many qualifiers (e.g., local, systemic, acute, chronic, cumulative, environmental, population, individual, gestational, or occupational.) See also exposure (individual), intervention, extent of exposure. [After International Programme on Chemical Safety (IPCS) 2004 WHO]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "eXtensible markup language (XML) data element", "definition": "CDISC Definition: For XML, an item of data provided in a mark-up mode to allow machine processing. NOTE: The mark-up or tagging facilitates document indexing, search and retrieval, and provides standard conventions for insertion of codes. [After Study Data Technical Conformance Guide, Technical Specifications Document, March 2019] See also XML (eXtensible Markup Language), Define-XML.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "extent of exposure", "definition": "CDISC Definition: A variable of exposure taking into consideration the strength, dose, duration, frequency, route, and/or timing or gestational stage in utero and other factors. NOTE: Measures of concentrations in biological fluids and tissues may be used to attempt to quantify the extent of exposures (e.g., Cmax, Cmin, Css, AUC in pharmacokinetics or other exposure measurement and assessment models). [After, FDA Guidance for Industry Exposure-Response Relationships] See also exposure, exposure (individual), intervention.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "extraction transformation load (ETL)", "definition": "CDISC Definition: A class of software applications for data extraction, transformation, and loading that are used to implement data interfaces between disparate database systems, often to populate data warehouses.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "feels", "definition": "CDISC Definition: A patient's physical sensation (e.g., symptoms) or perceived mental state. A patient may feel pain, feel feverish, or perceive a severely low mood (as with depression). [FDA Clinical Outcome Assessment (COA) Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "field", "definition": "CDISC Definition: Locus on a data collection instrument (usually a CRF) for recording or displaying a data element. See data item.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "File Transfer Protocol (FTP)", "definition": "CDISC Definition: A standard protocol for exchanging files between computers on the internet. See also TCP/IP.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "final report", "definition": "CDISC Definition: A written description of a trial/study of any therapeutic, prophylactic, or diagnostic agent conducted in human subjects, in which the clinical and statistical description, presentations, and analyses are fully integrated into a single report. [ICH E3]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "finding", "definition": "CDISC Definition: A meaningful interpretation of data or observations resulting from planned evaluations. Compare to conclusion, hypothesis. See also general observation class, intervention, event.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "first subject in - date, time (FSI - date, time)", "definition": "CDISC Definition: The date and/or date and time the first subject is enrolled into a study. See also enrollment.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "first subject in - identity (FSI - identity)", "definition": "CDISC Definition: The first subject enrolled. See also enrollment.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "first subject screened - date, time", "definition": "CDISC Definition: The date and/or date and time the first subject signs the informed consent form and is screened for potential enrollment or randomization into a study, but has not yet been determined to meet the inclusion/exclusion criteria for the trial.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "first subject screened - identity", "definition": "CDISC Definition: The first subject who is so screened.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "first subject treated - date, time", "definition": "CDISC Definition: The date and/or date and time when the first subject receives the test article or placebo in a clinical investigation.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "first subject treated - identity", "definition": "CDISC Definition: The first subject who is so treated.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "first-in-humans study", "definition": "CDISC Definition: The first Phase 1 study in which the test product is administered to human beings.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "follow-up (clinical study)", "definition": "CDISC Definition: A period in a clinical study during which selected observations are made, starting after the end of the active part of the study or as specified in the protocol.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Food and Drug Administration (FDA)", "definition": "CDISC Definition: The United States regulatory authority charged with, among other responsibilities, granting IND and NDA approvals.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Form", "definition": "CDISC Definition: A collection of items and item groups for capturing and displaying clinical trial data.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "frequentist methods", "definition": "CDISC Definition: Statistical methods, such as significance tests and confidence intervals, which can be interpreted in terms of the frequency of certain outcomes occurring in hypothetical repeated realizations of the same experimental situation. [ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "frozen", "definition": "CDISC Definition: Status of a database, file, or element that has been presumed to be in its final state pending \"lock\" and where further editing is prevented without \"unfreezing.\" NOTE: Freezing and unfreezing are usually formalized in audit trails and differ from \"locking\" and \"unlocking\" only in the degree of approval required. See database lock.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "functional roles (in a study)", "definition": "CDISC Definition: The function or responsibility assumed by a person in the context of a clinical study. Examples include data manager, investigator. [HL7]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "functions", "definition": "CDISC Definition: The manner in which a patient can perform successfully tasks and roles required for everyday living. A patient's ability to perform specified activities that are a meaningful (to the patient), part of typical (e.g., daily) life. [FDA Clinical Outcome Assessment (COA) Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "gender", "definition": "CDISC Definition: Subject self-identification re: masculine/feminine. [IOM] See also sex. [The NCI Thesaurus contains biomedical terminologies that NCI does not own or control. This concept contains gender-related content that does not comply with Executive Order 14168.]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "gene therapy", "definition": "CDISC Definition: Ex vivo or in vivo gene modification of cells in order to correct or treat an inherited or acquired disease or condition. NOTE: Gene therapy mechanisms can include: Replacing a disease-causing gene with a healthy copy of the gene; Inactivating a disease-causing gene that is not functioning properly; and Introducing a new or modified gene into the body to help treat a disease. [After Natalie Mount, et al. Cell-based therapy technology classifications and translational challenge. Philos Trans R Soc Lond B Biol Sci. 2015 Oct 19; 370(1680): 20150017; After What is Gene Therapy?, US FDA, 07/25/2018] See also cell therapy, regenerative medicine therapy, regenerative medicine advanced therapy, biological product.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "general observation class", "definition": "CDISC Definition: In the context of the Study Data Tabulation Model (SDTM), a higher level categorization of the subject-level observation domains. NOTE: Most CDISC domains are assigned to one of three general observation classes: 1) The Interventions general observation class is a domain that captures investigational treatments, therapeutic treatments, and surgical procedures that are intentionally administered to the subject (usually for therapeutic purposes) either as specified by the study protocol (e.g., exposure), coincident with the study assessment period (e.g., concomitant medications), or other substances self-administered by the subject (such as alcohol, tobacco, or caffeine). 2) The Events general observation class captures occurrences or incidents independent of planned study evaluations occurring during the trial (e.g., \"adverse events\" or \"disposition\") or prior to the trial (e.g., \"medical history\"). 3) The Findings general observation class captures the observations resulting from planned evaluations such as observations made during a physical examination, laboratory tests, ECG testing, and sets of individual questions listed on questionnaires. [Based on SDTM and SDTM Implementation Guide, www.CDISC.org] See also domain, event, intervention, finding. Compare with special purpose domain.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "generalizability", "definition": "CDISC Definition: The extent to which the findings of a clinical trial can be reliably extrapolated from the subjects who participated in the trial to a broader patient population and a broader range of clinical settings. [ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Generative AI (GenAI)", "definition": "CDISC Definition: Algorithms to organize large, complex data sets into meaningful clusters of information in order to create new content, including text, images and audio, in response to a query or prompt. [George Lawton, \"What is GenAI? Generative AI explained\", Informa, Mar 13, 2025, https://www.techtarget.com/searchenterpriseai/definition/generative-AI] See also AI prompt, Large Language Model (LLM).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "generic name", "definition": "CDISC Definition: The name of a drug based on its chemical and molecular structure. NOTE: In the United States of America, this is assigned by the United States Adopted Names (USAN) council. [After Merck Manual, Consumer Version, 2023] See also proprietary name, international nonproprietary name (INN), established name, medicinal product name, compendial name.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "global assessment variable", "definition": "CDISC Definition: A single variable, usually a scale of ordered categorical ratings, which integrates objective variables and the investigator's overall impression about the state or change in state of a subject. [ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "glossary", "definition": "CDISC Definition: A collection of specialized words or terms with their meanings.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Good Clinical Practice (GCP)", "definition": "CDISC Definition: A standard for the design, conduct, performance, monitoring, auditing, recording, analyses, and reporting of clinical trials that provides assurance that the data and reported results are credible and accurate and that the rights, integrity, and confidentiality of trial subjects are protected. NOTE: For Guidance on Good Clinical Practice see COMP/ICH/135/95; Declaration of Helsinki; 21 CFR 50, 21 CFR 54, 21 CFR 56, and 21 CFR 312. [ICH]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "granularity", "definition": "CDISC Definition: Refers to the size of an information unit in relation to a whole. NOTE: Structuring \"privileges\" in electronic systems is said to be highly granular when each of many roles can differ in their capacity to act on electronic records.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "group sequential design", "definition": "CDISC Definition: A type of adaptive trial design that allows successive, unscheduled interim analyses of the data at particular time points or after a pre-defined number of patients have been enrolled. NOTE: This kind of trial design is chosen to allow for spontaneous interim analyses, in order to, for example, determine whether to stop the trial, adjust the sample size, adjust the dose, or otherwise amend the protocol. [After https://toolbox.eupati.eu/glossary/group-sequential-design/; https://www.statisticshowto.com/group-sequential-design/; https://toolkit.ncats.nih.gov/glossary/group-sequential-trial/] See also interim analysis(es), adaptive design, Bayesian statistics, Bayesian approaches.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "handwritten signature", "definition": "CDISC Definition: The scripted name or legal mark of an individual handwritten by that individual and executed or adopted with the present intention to authenticate a writing in a permanent form. NOTE: The act of signing with a writing or marking instrument such as a pen or stylus is preserved. [21CFR 11]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "harmonized standard", "definition": "CDISC Definition: A European Norm (EN) that has been accepted by all Member States and has been published in the Official Journal of the European Communities (OJEC).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "hazard ratio", "definition": "CDISC Definition: A complex statistical analysis that compares the risk of harm in one group to another. NOTE: Calculated from the Cox Proportional Hazard model. [After AMA Manual of Style; After NCI Thesaurus] See also relative risk.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Health Level 7 (HL7)", "definition": "CDISC Definition: An ANSI-accredited Standards Developing Organization (SDO) operating in the healthcare arena. NOTE: Level 7 refers to the highest level of the International Standards Organization's (ISO) communications model for Open Systems Interconnection (OSI), the application level. The application level addresses definition of the data to be exchanged, the timing of the interchange, and the communication of certain errors to the application. Level 7 supports such functions as security checks, participant identification, availability checks, exchange mechanism negotiations, and, most importantly, data exchange structuring.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "health literacy", "definition": "CDISC Definition: The degree to which an individual has the capacity to obtain, communicate, process, and understand basic health information and services to make health decisions. [After The Patient Protection and Affordable Care Act of 2010, Title V; After What is Health Literacy? Oct 23, 2019]. See also plain language writing.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "health-related quality of life (HRQoL)", "definition": "CDISC Definition: A multi-domain concept that represents the patient's general perception of the effect of illness and treatment on physical, psychological, and social aspects of life. NOTE: Claiming a statistical and meaningful improvement in HRQoL implies: (1) that all HRQoL domains that are important to interpreting change in how the clinical trial's population feels or functions as a result of the targeted disease and its treatment were measured; (2) that a general improvement was demonstrated; and (3) that no decrement was demonstrated in any domain. [FDA Clinical Outcome Assessment (COA) Glossary] Compare to quality of life (QoL).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "healthcare facility", "definition": "CDISC Definition: Any public or private entity or agency or medical or dental facility where healthcare services are provided or clinical trials are conducted. [After ICH E6; CIOMS Glossary of ICH terms and definitions]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "healthcare provider", "definition": "CDISC Definition: A person licensed, certified, or otherwise authorized or permitted by law to administer healthcare in the ordinary course of business or practice of a profession, including a healthcare facility. [HL7]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "healthy volunteer", "definition": "CDISC Definition: A person with no significant health-related issues who agrees to participate as a subject in a clinical study. NOTE: This is often a healthy person in a Phase 1 trial. See also Phase 1. [After Patient Recruitment Healthy Volunteer, NIH Clinical Center, 05/18/2022, Webpage accessed 2023-03-30]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "hereditary", "definition": "CDISC Definition: Transmitted from parent to child by genetic transmission. [After NCI]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "HIE (Health Information Exchange)", "definition": "CDISC Definition: The mobilization of healthcare information electronically across organizations within a region or community. HIE provides the capability to electronically move clinical information between disparate healthcare information systems, while maintaining the meaning of the information being exchanged. The goal of HIE is to facilitate access to, and retrieval of, clinical data to provide safer, more timely, efficient, effective, equitable, and patient-centered care. [HITSP]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "human subject", "definition": "CDISC Definition: Individual who is or becomes a participant in research, either as a recipient of the test article or as a control. A subject may be either a healthy human or a patient. [21 CFR 50.3]. See also clinical research subject.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Huriet Law", "definition": "CDISC Definition: France's regulations covering the initiation and conduct of clinical trials.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "HyperText Markup Language (HTML)", "definition": "CDISC Definition: A specification of the W3C that provides markup of documents for display in a web browser. [HL7] Contrast to XML.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "hypertext", "definition": "CDISC Definition: Links in a document that permit browsers to jump immediately to another document. NOTE: In most browsers links are displayed as colored, underlined text.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "hypothesis to test", "definition": "CDISC Definition: In a trial, a statement relating to the possible different effect of the interventions on an outcome. The null hypothesis of no such effect is amenable to explicit statistical evaluation by a hypothesis test, which generates a P value. [CONSORT Statement]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "immediately life-threatening disease or condition", "definition": "CDISC Definition: A stage of disease in which there is reasonable likelihood that death will occur within a matter of months, or in which premature death is likely without early treatment. [21 CFR 312.300]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "immune system", "definition": "CDISC Definition: A complex network of cells, chemicals, tissues, and organs that defends the body from infection and disease. NOTE: Bone marrow, thymus, lymphatic system, lymph nodes, spleen, and mucous membranes can be involved. [After NCI Thesaurus] See also antigen, antibody, autoimmunity.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "impartial witness", "definition": "CDISC Definition: A person who is independent of the trial, who cannot be unfairly influenced by people involved with the trial, who attends the informed consent process if the subject or the subject's legally acceptable representative cannot read, and who reads the informed consent form and any other written information supplied to the subject. [ICH]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "in vitro diagnostic device", "definition": "CDISC Definition: A reagent, reagent product, calibrator, control material, kit, instrument, apparatus, piece of equipment, software or system, whether used alone or in combination, intended by the manufacturer to be used in vitro for the examination of specimens, including blood and tissue donations, derived from the human body, solely or principally for the purpose of providing information to aid towards a diagnosis. (After Regulation (EU) 2017/746; After US FDA 21 CFR 809.3)", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "incidence rate", "definition": "CDISC Definition: A proportion calculated as the number of individuals who develop the disease during a period of time divided by the number of persons at risk. [After AMA Style Guide, 10th Edition; After Principles of Epidemiology in Public Health Practice, Third Edition. An Introduction to Applied Epidemiology and Biostatistics, Lesson 3: Measures of Risk, CDC 2012] See also morbidity rate, morbidity, mortality, incidence, prevalence.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "incidence", "definition": "CDISC Definition: The occurrence of new cases of disease, injury, or disability in a defined population over a specified period of time. NOTE: Incidence is most often expressed relative to the total population at risk (i.e., per unit of population). [After Basic Epidemiology, R. Bonita and others, WHO 2006; After Principles of Epidemiology in Public Health Practice, Third Edition. An Introduction to Applied Epidemiology and Biostatistics, Lesson 3: Measures of Risk, CDC 2012] Compare to prevalence. See also morbidity rate, morbidity, mortality, incidence rate.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "inclusion criteria", "definition": "CDISC Definition: The criteria in a protocol that prospective subjects must meet to be eligible for participation in a study. NOTE: Exclusion and inclusion criteria define the study population. See also exclusion criteria.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "independent data monitoring committee (IDMC)", "definition": "CDISC Definition: A committee established by the sponsor to assess at intervals the progress of a clinical trial, safety data, and critical efficacy variables and recommend to the sponsor whether to continue, modify, or terminate the trial. [ICH E9] See also data monitoring committee.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "independent ethics committee (IEC)", "definition": "CDISC Definition: An independent body (a review board or a committee, institutional, regional, national, or supranational) constituted of medical/scientific professionals and non-scientific members, whose responsibility it is to ensure the protection of the rights, safety, and well-being of human subjects involved in a trial and to provide public assurance of that protection by, among other things, reviewing and approving/providing favorable opinion on the trial protocol, the suitability of the investigator(s), facilities, and the methods and material to be used in obtaining and documenting informed consent of the trial subjects. NOTE: The legal status, composition, function, operations, and regulatory requirements pertaining to independent ethics committees may differ among countries but should allow the independent ethics committee to act in agreement with GCP as described in the ICH guideline. [After ICH E6 R2 Glossary] See also institutional review board, ethics committee.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "independent variable", "definition": "CDISC Definition: A variable that is not affected by other variables that the study is trying to understand. Independent variables influence dependent variables. [After AMA Manual of Style] See also dependent variable.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "indication", "definition": "CDISC Definition: The target disease or condition, or its manifestations or symptoms, for which the treatment, prevention, mitigation, cure, or diagnosis is studied or approved. NOTE: In the context of product labeling, the disease indication is usually associated with a population of interest. [After 21 CFR 201.57(c)(2); Wording of therapeutic indication. A Guide for Assessors of Centralised Applications. 21 October 2019, EMA/CHMP/483022/2019. Committee for Medicinal Products for Human Use (CHMP)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "indirect identifier", "definition": "CDISC Definition: Data which in connection with other information can be used to identify an individual with high probability, e.g., age at baseline, race, sex, events, specific findings, etc. NOTE: Two levels of indirect identifier are distinguished. Level 1 - not likely to change over time, is visible, and is available in other sources. Typically it is demographic data such as sex, age at a particular date, country, body mass index (BMI). Level 2 - longitudinal information that is likely to change such as measurements, events, age. See also quasi identifier. [PhUSE De-identification Standard for SDTM 3.2, version 1.0.1.]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "informed consent", "definition": "CDISC Definition: A process that provides the subject with explanations that will help in making decisions about whether to begin or continue participating in a study, after having achieved an understanding of the potential risks and benefits. NOTE: Informed consent is an ongoing, interactive process rather than a one-time information session and can use digital methodologies. Under 21 CFR 50.20, no informed consent form may include any \"language through which the subject or the representative is made to waive or appear to waive any of the subject's legal rights, or releases or appears to release the investigator, the sponsor, the institution, or its agents from liability for negligence.\" In some cases, when the prospective subject is unable to provide legal consent, permission to participate may be obtained from a legally-authorized representative. [US FDA 21 CFR 50.20; After US FDA Use of Electronic Informed Consent Questions and Answers Guidance for Institutional Review Boards, Investigators, and Sponsors, Dec 2016] See also consent form, eConsent form.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "infusion", "definition": "CDISC Definition: Any form of treatment that is introduced into the body slowly by constant administration or drip via a blood vessel, a muscle, or the spinal cord. [After EDQM Standard Terms controlled vocabularies for pharmaceutical dose forms Version 1.2.0 2019. Internal controlled vocabularies for pharmaceutical dose forms. Version 1.2.0 - 28 January 2019.] See also administration (substance).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "ingredient", "definition": "CDISC Definition: Active and/or inactive material used in pharmaceutical product. [After ISO 11615:2017, 3.1.28]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "inspection", "definition": "CDISC Definition: The act by a regulatory authority(ies) of conducting an official review of documents, facilities, records, and any other resources that are deemed by the authority(ies) to be related to the clinical trial and that may be located at the site of the trial, at the sponsor's and/or contract research organization's (CRO's) facilities, or at other establishments deemed appropriate by the regulatory authority(ies). [ICH] See also audit.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "institutional review board (IRB)", "definition": "CDISC Definition: An independent body constituted of medical, scientific, and non-scientific members, whose responsibility it is to ensure the protection of the rights, safety, and well-being of human subjects involved in a study by, among other things, reviewing, approving, and providing continuing review of study protocol and of the methods and material to be used in obtaining and documenting informed consent of the study subjects. [ICH E6 1.31]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "instrument", "definition": "CDISC Definition: A means to capture data (e.g., questionnaire, diary) plus all the information and documentation that supports its use. NOTE: Generally, instruments include clearly defined methods and instructions for administration or responding, a standard format for data collection, and well-documented methods for scoring, analysis, and interpretation of results. [from PRO Draft Guidance] Compare to questionnaire, survey (see Comments on Draft PRO Guidance, April 4, 2006, by ISOQOL, p. 8).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "intended use", "definition": "CDISC Definition: The specific clinical circumstance or purpose for which a medical product or test is being developed. NOTE: In the regulatory context, this term refers to the \"Statement of Intended Use\" prepared by the persons legally responsible for the labeling of medical products. [after NIH-FDA BEST (Biomarkers, Endpoints, and other Tools) Resource, https://www.ncbi.nlm.nih.gov/books/NBK338448/]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "intention-to-treat", "definition": "CDISC Definition: The principle that asserts that the effect of a treatment policy can be best assessed by evaluating the basis of the intention to treat a subject (i.e., the planned treatment regimen) rather than the actual treatment given. NOTE: This has the consequence that subjects allocated to a treatment group should be followed up, assessed, and analyzed as members of that group irrespective of their compliance with the planned course of treatment. The principle is intended to prevent bias caused by loss of participants that may reflect non-adherence to the protocol and disrupt baseline equivalence established by random assignment. [ICH E9; after CONSORT statement]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "inter-rater reliability", "definition": "CDISC Definition: The property of scales yielding equivalent results when used by different raters on different occasions. [ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "intercurrent event", "definition": "CDISC Definition: An event(s) occurring after treatment initiation that affects either the interpretation or the existence of the measurements associated with the clinical question of interest. [ICH E9 Addendum on Estimands]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "interim analysis schedule", "definition": "CDISC Definition: The time/information points at which interim analyses are planned.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "interim analysis(es)", "definition": "CDISC Definition: Analysis comparing intervention groups at any time before the formal completion of the trial, usually before recruitment is complete. [CONSORT statement]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "interim clinical trial/study report", "definition": "CDISC Definition: A report of intermediate results and their evaluation based on planned analyses performed during the course of a trial. [ICH]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "internal consistency", "definition": "CDISC Definition: Pertaining to data that do not include contradictions.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "international birth date (IBD)", "definition": "CDISC Definition: The date of the first marketing authorization for a new product granted to any company in any country in the world. NOTE: Used for Periodic Safety Update Report (PSUR). [After ICH E2C(R2), Appendix A]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "international nonproprietary name (INN)", "definition": "CDISC Definition: Unique name for a drug substance (pharmaceutical ingredient) that is globally recognized and public property. NOTE: The INN name is established by the World Health Organization (WHO). [After WHO, Health products policy and standards, INN and medicines classification] See also proprietary name, generic name, established name, medicinal product name, compendial name, active substance.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "internet service provider (ISP)", "definition": "CDISC Definition: A company that provides access to the internet for individuals and organizations.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "internet", "definition": "CDISC Definition: A global system of computer networks that provides the common TCP IP infrastructure for e-mail, the World Wide Web, and other online activities.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "interoperability", "definition": "CDISC Definition: Ability of two or more systems or components to exchange information and to use the information that has been exchanged. [IEEE Standard Computer Dictionary]. See also syntactic, semantic, semantic interoperability.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "intervention", "definition": "CDISC Definition: An activity that produces an effect, or that is intended to alter the course of a disease in a patient or population. This is a general term that encompasses the medical, social, behavioral, and environmental acts that can have preventive, therapeutic, or palliative effects. (NCI) See also investigational product, experimental intervention, vaccine, medical device, diagnostic device.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Investigational Device Exemption (IDE)", "definition": "CDISC Definition: An application to FDA for a waiver to test a device in a clinical trial. [After US FDA, 21 CFR Part 812]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "investigational device", "definition": "CDISC Definition: A device that is assessed in a clinical investigation. [REGULATION (EU) 2017/745 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 5 April 2017 on medical devices] See also investigational product, medical device.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "investigational medicinal product", "definition": "CDISC Definition: A pharmaceutical form of an active ingredient being tested or used as a reference in a clinical trial, including a product with a marketing authorization when used or assembled (formulated or packaged) in a way different from the approved form, or when used for an unapproved indication, or when used to gain further information about an approved use. Reference products and placebos are also considered investigational medicinal products in a clinical trial. [After E6(R2) Good Clinical Practice (GCP) -- Step 4 (final), 9 November 2016 -- Glossary] See also authorised investigational medicinal product, experimental intervention.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Investigational New Drug (IND) application", "definition": "CDISC Definition: An application to FDA for a waiver to allow the administration of investigational products in a clinical trial. [After US FDA, 21 CFR Part 312]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "investigational product code", "definition": "CDISC Definition: A symbol or combination of symbols (usually alphanumeric characters) that are assigned by the sponsor to uniquely identify an experimental intervention. [After ICH M11]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "investigator", "definition": "CDISC Definition: A person responsible for the conduct of the study, ensuring adherence to the protocol and good clinical practices. NOTE: For example, under whose immediate direction the test article is administered or dispensed to, or used involving a subject. [21 CFR 50.3, ICH E6] See also sponsor-investigator, site investigator, principal investigator, coordinating investigator, sub-investigator.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "investigator's brochure", "definition": "CDISC Definition: A compilation of the clinical and non-clinical data on the investigational product(s) that is relevant to the study of the investigational product(s) in human subjects.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "investigator/institution", "definition": "CDISC Definition: An expression meaning \"the investigator and/or institution, where required by the applicable regulatory requirements\" with respect to the transfer or assignment of responsibilities. [After ICH E6 1.35] See also coordinating investigator, investigator, principal investigator, site investigator, sponsor-investigator, sub-investigator.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "item (PRO)", "definition": "CDISC Definition: An individual question, statement, or task (and its standardized response options) that is evaluated by the patient to address a particular concept. [FDA Clinical Outcome Assessment (COA) Glossary] See also item generation, response option.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "item definition", "definition": "CDISC Definition: Formal specification of the properties of an item or field of data in an eClinical trial. [CDISC ODM, CDISC CDASH]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "item generation", "definition": "CDISC Definition: Establishing the content to be covered by the items in a PRO instrument, including generating item wording, evaluating the completeness of item coverage of the concepts of interest, and performing initial assessment of clarity and readability. NOTE: PRO instrument item generation is potentially incomplete without patient involvement. [from ISOQOL comments on PRO Draft Guidance]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "item group definition", "definition": "CDISC Definition: The specification in an eClinical trial of a collection of items often clinically related to each other and useful to consider as an ensemble. NOTE: Item groups are likely to have greater granularity in analysis datasets using SDTM which can, for example, distinguish between different therapy types: study therapy, prior therapy, concomitant therapy, protocol forbidden therapies, rescue therapies. [ODM]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "item", "definition": "CDISC Definition: A representation of a clinical variable, fact, concept, or instruction in a manner suitable for communication, interpretation, or processing by humans or by automated means. NOTE: Items are collected together to form item groups. [CDISC] Compare to data item, item (PRO).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Janus conceptual model", "definition": "CDISC Definition: A logical design for a data warehouse intended to integrate submission data, protocol descriptions, and analysis plans from clinical and animal studies into an FDA review environment that uses a set of validated, standards-based tools to allow reproducible cross-study, data mining, and retrospective comparative analysis. [FDA Study Data Standards]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Janus study data repository", "definition": "CDISC Definition: The Janus is a data repository for subject-level clinical and nonclinical study data submitted to FDA as part of a regulatory submission. NOTE: Sometimes written as JANUS, the term is not an acronym. [FDA Study Data Standards]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "label", "definition": "CDISC Definition: Description of a drug product/ device that includes: the indication, who should use it, adverse events, instructions for use, and safety information. NOTE: Labels must be approved by regulatory authorities. [FDA; SPL]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "labeling (content of)", "definition": "CDISC Definition: All text, tables, and figures in labeling as described in regulations for a specific product (e.g., 21 CFR 201.56 and 201.57 for human prescription drugs; 201.66 for human over-the-counter drugs; 21 CFR 801 for medical devices; and 21 CFR 606.122 for blood products). See also structured product label.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "laboratory (clinical)", "definition": "CDISC Definition: A laboratory providing analyses of samples collected in clinical care or research.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Large Language Model (LLM)", "definition": "CDISC Definition: A type of AI model trained on large text datasets to learn the relationships between words in natural language. NOTE: These models can apply these learned patterns to predict and generate natural language responses to a wide range of inputs or prompts they receive, to conduct tasks like translation, summarization, and question answering. [FDA Digital Health and Artificial Intelligence Glossary - Educational Resource, 09/26/2024] See also AI prompt, generative AI.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "last subject in - date, time (LSI - date, time)", "definition": "CDISC Definition: The date and/or date and time when a last subject to participate in a clinical trial is enrolled.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "last subject in - identity (LSI - identity)", "definition": "CDISC Definition: The last subject enrolled in a clinical trial.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "last subject last visit - date, time (LSLV - date, time)", "definition": "CDISC Definition: The date and/or date and time when a last subject has reached a planned or achieved milestone representing the completion of the trial.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "last subject last visit - identity (LSLV - identity)", "definition": "CDISC Definition: The last subject to reach a planned or achieved milestone representing the completion of the trial.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "legal authentication", "definition": "CDISC Definition: A completion status in which a document has been signed manually or electronically by the individual who is legally responsible for that document. [HL7]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "legally acceptable representative", "definition": "CDISC Definition: An individual or juridical or other body authorized under applicable law to consent, on behalf of a prospective subject, to the subject's participation in the clinical trial. [ICH, E6 Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "life-threatening adverse event/ experience", "definition": "CDISC Definition: Any adverse drug experience that places the patient or subject, in the view of the investigator, at immediate risk of death from the reaction as it occurred (i.e., it does not include a reaction that, had it occurred in a more severe form, might have caused death). [FDA 21 CFR 312.32; ICH-E2A]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "long term follow-up (clinical study)", "definition": "CDISC Definition: A period in a clinical study during which selected observations are made over an extended timeframe, starting after the end of the active part of the study. NOTE: LTFU may be a post-study commitment. [After Long Term Follow-up After Administration of Human Gene Therapy Products. FDA Guidance for Industry. JAN 2020] See also follow-up (clinical study).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "longitudinal study", "definition": "CDISC Definition: A prospective observational study designed to monitor health measures of individuals over a defined period of time. NOTE: A well-known example is the Framingham study, which began in 1948. [After clinicaltrials.gov] See also observational study.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "low-interventional clinical trial", "definition": "CDISC Definition: A clinical trial which fulfills all of the following conditions: (a) the investigational medicinal products, excluding placebos, are authorized; (b) according to the protocol of the clinical trial, (i) the investigational medicinal products are used in accordance with the terms of the marketing authorization; or (ii) the use of the investigational medicinal products is evidence-based and supported by published scientific evidence on the safety and efficacy of those investigational medicinal products in any of the Member States concerned; and (c) the additional diagnostic or monitoring procedures do not pose more than minimal additional risk or burden to the safety of the subjects compared to normal clinical practice in any Member State concerned. [REGULATION (EU) No 536/2014 Article 2.2.(3)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "machine learning", "definition": "CDISC Definition: A computing system (inspired by biological neural networks) that learns (progressively improves its ability) to do tasks by considering examples without task-specific programming. NOTE: Machine learning algorithms build a mathematical model based on sample data, known as \"training data\", in order to make predictions or decisions without being explicitly programmed to do so. [After DeepAI Machine Learning Glossary and Terms] See also deep learning, artificial intelligence (AI).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "manufacturer (device)", "definition": "CDISC Definition: Any person or entity who manufactures, prepares, propagates, compounds, assembles, or processes a device by chemical, physical, biological, or other procedure. The term includes any person who either (1) Repackages or otherwise changes the container, wrapper, or labeling of a device in furtherance of the distribution of the device from the original place of manufacture; (2) Initiates specifications for devices that are manufactured by a second party for subsequent distribution by the person initiating the specifications; (3) Manufactures components or accessories that are devices that are ready to be used and are intended to be commercially distributed and intended to be used as is, or are processed by a licensed practitioner or other qualified person to meet the needs of a particular patient; or (4) Is the U.S. agent of a foreign manufacturer. [after 21 CFR 803.3, FDA] See also manufacturer (drug).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "manufacturer (drug)", "definition": "CDISC Definition: Any person or entity involved in the processing, packing, or holding of a medicinal product, including packaging and labeling, testing, and quality control. [after 21 CFR 210.3] See also manufacturer (device).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "mapping", "definition": "CDISC Definition: In the context of representing or exchanging data, connecting an item or symbol to a code or concept. Compare to translation.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "marketing authorization holder", "definition": "CDISC Definition: Organization or person that is permitted to market a medicinal product in a jurisdiction. [After ISO 11615:2017, 3.1.41]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "marketing authorization procedure", "definition": "CDISC Definition: Formal EU procedure applied by a medicines regulatory agency to grant a marketing authorization, to amend an existing one, to extend its duration or to revoke it. [After ISO 11615:2017, 3.1.43]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "marketing authorization", "definition": "CDISC Definition: Authorisation issued from a medicines regulatory agency that allows a Medicinal Product to be placed on the market. [after ISO 11615 2017-10 on Regulated Medicinal Product information]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "marketing support trials", "definition": "CDISC Definition: Clinical studies that are designed to clarify therapeutic benefits of a marketed product or to show potential decision-makers the rationale for preferring one therapy over another.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "markup", "definition": "CDISC Definition: Computer-processable annotations within a multimedia document. NOTE: in the context of the HL7 specification, markup syntax is according to the XML specification. [HL7]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "masking", "definition": "CDISC Definition: The mechanism used to obscure the distinctive characteristics of the study intervention or procedure to make it indistinguishable from the comparator. NOTE: Blinding refers to study participants while masking refers to the study intervention. [After Crisp A. Blinding in pharmaceutical clinical trials: An overview of points to consider. Contemp Clin Trials. 2015;43:155-163.] See also blinding.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "master protocol", "definition": "CDISC Definition: A protocol designed to enable multiple substudies, which may have different objectives and involve coordinated efforts to evaluate one or more investigational drugs in one or more disease subtypes within the overall trial structure. NOTE: The term \"master protocol\" is often used to describe the design of such trials, with terms such as \"umbrella\", \"basket\", or \"platform\" describing specific designs. [After US FDA, Master Protocols: Efficient Clinical Trial Design Strategies to Expedite Development of Oncology Drugs and Biologics Guidance for Industry, 2022; Woodcock J, LaVange LM. Master Protocols to Study Multiple Therapies, Multiple Diseases, or Both. N Engl J Med. 2017 Jul 6;377(1):62-70.] See also umbrella trial design, basket trial design, platform trial design, adaptive design.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "matched-pair design", "definition": "CDISC Definition: A type of parallel trial design in which investigators identify pairs of subjects who are 'identical' with respect to relevant factors, then randomize them so that one receives Treatment a and the other Treatment B. See also pairing.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "mean", "definition": "CDISC Definition: The sum of the values of all observations or data points divided by the number of observations; an arithmetical average.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "MedDRA (Medical Dictionary for Regulatory Activities)", "definition": "CDISC Definition: A global standard medical terminology designed to supersede other terminologies used in the medical product development process, including COSTART, ICD9, and others.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "median", "definition": "CDISC Definition: The middle value in a data set; that is, just as many values are greater than the median and lower than the median value. (With an even number of values, the conventional median is halfway between the two middle values.)", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "medical countermeasure", "definition": "CDISC Definition: Pharmaceutical products, such as vaccines, antimicrobials, and antitoxins, and nonpharmaceutical products, such as ventilators, diagnostic tests, personal protective equipment (PPE), and patient (also general) decontamination materials, that may be used to prevent, mitigate, or treat the adverse health effects from a public health emergency. [After National Health Security Strategy 2019-2022]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "medical device", "definition": "CDISC Definition: Any instrument, apparatus, implement, machine, appliance, implant, reagent for in vitro use, software, material or other similar or related article, intended by the manufacturer to be used, alone or in combination, for human beings, for one or more specific medical purpose(s). NOTE: Specific medical purposes include diagnosis; prevention; monitoring; treatment or alleviation of disease; diagnosis; monitoring; treatment; alleviation of or compensation for an injury; investigation; replacement; modification; or support of the anatomy or of a physiological process; supporting or sustaining life, control of conception; disinfection of medical devices providing information by means of in vitro examination of specimens derived from the human body; and does not achieve its primary intended action by pharmacological, immunological or metabolic means, in or on the human body, but which may be assisted in its intended function by such means. [After REGULATION (EU) 2017/745 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 5 April 2017 on medical devices; After MHRA Guidance: Medical device stand-alone software including apps]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "medical monitor", "definition": "CDISC Definition: A sponsor representative who has medical authority for the evaluation of the safety aspects of a clinical trial.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "medical monitoring", "definition": "CDISC Definition: Act of tracking the progress or severity of a disease, injury or handicap in patients in order to support a medical purpose. See also monitoring.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "medication error", "definition": "CDISC Definition: Any unintentional error in the prescribing, dispensing or administration of a medicinal product while in the control of the healthcare professional, patient or consumer. [HMA, Guideline on good pharmacovigilance practices (GVP)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "medicinal product classification", "definition": "CDISC Definition: Categorisation or grouping of Medicinal Products based on specific properties and according to various classification systems (e.g., UNII-SRS), which may be regional or international. NOTE: The classification system is specified using an appropriate identification system; the applicable controlled term and the controlled term identifier is specified. [after ISO 11615 2017-10 on Regulated Medicinal Product information]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "medicinal product identifier", "definition": "CDISC Definition: Unique identifier allocated to a medicinal product supplementary to any existing authorization number as ascribed by a medicines regulatory agency in a jurisdiction. NOTE: proposed by IDMP as a new universal identifier. [After ISO 11615:2017, 3.1.53]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "medicinal product name", "definition": "CDISC Definition: Name as authorized by a Medicines Regulatory Agency. NOTE: As a general principle, a marketing authorization is granted to a single Marketing Authorization Holder or sponsor who is responsible for placing a single Medicinal Product on the market. The marketing authorization contains the name of the Medicinal Product, which can refer to, for example, a single invented name or a scientific name [when available, the INN of the active substance(s)] accompanied by a trademark or other characteristics. Other characteristics of the name can refer to strength, pharmaceutical form, intended usage or an administration device, etc. [After ISO 11615:2017, 3.1.54] See also proprietary name, generic name, international nonproprietary name (INN), established name, medicinal product name, compendial name.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "medicinal product", "definition": "CDISC Definition: Any substance or combination of substances that may be administered to human beings (or animals) for treating or preventing disease, or with the intent to make a medical diagnosis or to restore, correct or modify physiological functions. NOTE: 1. A Medicinal Product may contain one or more manufactured items and one or more pharmaceutical products. 2. In certain jurisdictions a Medicinal Product may also be defined as any substance or combination of substances which may be used to make a medical diagnosis. [After IDMP]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Medicines and Healthcare products Regulatory agency (MHRA)", "definition": "CDISC Definition: The UK government agency responsible for ensuring that medicines and medical devices work, and are acceptably safe. [MHRA]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "mega-trials", "definition": "CDISC Definition: Massive trials that test the advantages of therapeutic interventions by enrolling 10,000 or more subjects.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "memorandum of understanding (MOU)", "definition": "CDISC Definition: A formal agreement between the Food and Drug administration (FDA) and federal, state, or local government agencies; academic institutions; and other entities. NOTE: The MOU constitutes an understanding between the parties but is a non-binding agreement. it is FDA's policy to enter into MOUs with other entities whenever there is a need to define lines of authority or responsibility, or to clarify cooperative procedures.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "message (HL7)", "definition": "CDISC Definition: The atomic unit of data transferred between systems. It comprises a group of segments in a defined sequence, each message has a message type that defines its purpose. NOTE: For example, the Admission, Discharge and Transfer (ADT) Message type is used to transmit portions of a patient's ADT data from one system to another. in HL7, a three-character code contained within each message identifies its type. [HL7]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "meta-analysis protocol", "definition": "CDISC Definition: The document describing the plan for combining of evidence from relevant studies using appropriate statistical methods to allow inference to be made to the population of interest. NOTE: The most common reason for performing a meta-analysis is to provide an estimate of a treatment effect or measure of relative risk associated with an intervention and to quantify the uncertainty about the estimated effect or risk, when data from a single existing study are insufficient for this purpose. [FDA Draft Guidance, Meta-Analyses of Randomized Controlled Clinical Trials to Evaluate the Safety of Human Drugs or Biological Products Guidance for Industry, November 2018] See also meta-analysis.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "meta-analysis", "definition": "CDISC Definition: The formal evaluation of the quantitative evidence from two or more trials bearing on the same question. NOTE: This most commonly involves the statistical combination of summary statistics from the various trials, but the term is sometimes also used to refer to the combination of the raw data. The methodology for performing the meta-analysis can be found in a meta-analysis protocol, or plan. [After ICH E9 Glossary] See also meta-analysis protocol.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "metabolism", "definition": "CDISC Definition: The biochemical alteration of substances introduced into the body.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "metadata", "definition": "CDISC Definition: Data that describe other data, particularly XML tags characterizing attributes of values in clinical data fields.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "migration", "definition": "CDISC Definition: The act of moving a system or software product (including data) from an old to new operational environment in accordance with a software quality system. ISO/IEC/IEEE 12207:1995 5.5.5]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "minor", "definition": "CDISC Definition: A subject who, according to the law of the applicable jurisdiction concerned, is under the age of legal competence to give informed consent. [after EU CTR]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "missing data", "definition": "CDISC Definition: Data not completed or corrupted in reports and case report forms, e.g., the data not captured when a subject withdraws from a trial. NOTE: Reviewers are concerned about missing data since patients who are not improved or who believe they have experienced side effects may be particularly prone to leave a trial, thus skewing the analysis of results if such analysis were to be done only on the subjects who had continued with the trial. Trial designs therefore specify plans for how such missing data will be treated in analysis. See also intention to treat. [FDA Guidance on Subject Withdrawal, 2008]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "mode", "definition": "CDISC Definition: The most frequently occurring value in a data set.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "model", "definition": "CDISC Definition: A formal structure for representing and analyzing a process such as a clinical trial or the information pertaining to a restricted context (e.g., clinical trial data). [CDISC]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "modem", "definition": "CDISC Definition: From modulator/ demodulator; a device that converts digital data into analog data that can be transmitted via telephone or cable lines used for communications.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "moiety", "definition": "CDISC Definition: An entity that has a complete and continuous molecular structure and is part of a substance. The active moiety of the molecule is the basis for the physiological or pharmacological action of the drug substance. NOTE: The strength of a pharmaceutical product is often based on what is referred to as the active moiety. [after ISO 11238 2012-11 on Regulated information on Substances]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "monitor", "definition": "CDISC Definition: Person employed by the sponsor or CRO who is responsible for determining that a trial is being conducted in accordance with the protocol and GCP guidance. NOTE: A monitor's duties may include, but are not limited to, helping to plan and initiate a trial, assessing the conduct of trials, and assisting in data analysis, interpretation, and extrapolation. Monitors work with the clinical research coordinator to check all data and documentation from the trial. [from ICH E6, 5.18] See also clinical research associate.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "monitoring plan", "definition": "CDISC Definition: A document that describes the strategy, methods, responsibilities, and requirements for monitoring the trial. [ICH E6(R2) Glossary Addendum] See also monitoring.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "monitoring report", "definition": "CDISC Definition: A written report from the monitor to the sponsor after each site visit and/or other trial-related communication according to the sponsor's SOPs. [ICH]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "monitoring visit", "definition": "CDISC Definition: A visit to a study site to review the progress of a clinical study and to ensure protocol adherence, accuracy of data, safety of subjects, and compliance with regulatory requirements and good clinical practice guidelines. [from ICH E6, 5.18]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "monitoring", "definition": "CDISC Definition: Act of overseeing, tracking, observing, evaluating or supervising over time by a person, device or system. See also subject monitoring, medical monitoring, study monitoring, trial monitoring, data monitoring, risk based monitoring.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "morbidity rate", "definition": "CDISC Definition: A measure of the frequency of occurrence of a specific disease, injury, or disability in a defined population during a specified interval. [After Principles of Epidemiology in Public Health Practice, Third Edition. An Introduction to Applied Epidemiology and Biostatistics] See also morbidity, incidence, prevalence, mortality rate, incidence rate.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "morbidity", "definition": "CDISC Definition: Departure from physiological or psychological health, i.e., disease, injury, or disability. NOTE: Most often measures of morbidity frequency characterize the number of persons in a population who become ill (incidence) or are ill at a given time (prevalence). See also morbidity rate, incidence, prevalence, mortality rate, incidence rate, virulence.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "mortality rate", "definition": "CDISC Definition: A measure of the frequency of occurrence of death in a defined population during a specified interval. [After Principles of Epidemiology in Public Health Practice, Third Edition. An Introduction to Applied Epidemiology and Biostatistics] See also morbidity, morbidity rate, incidence, prevalence, incidence rate.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "multicenter trial", "definition": "CDISC Definition: Clinical trial conducted according to a single protocol but at more than one site and, therefore, carried out by more than one investigator. [ICH E9 Glossary] See investigator/institution, study.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "mutual recognition procedure (MRP)", "definition": "CDISC Definition: The EU procedure to be used when a product is already authorized in at least one Member State and the Marketing Authorization Holder wishes to obtain a Marketing Authorization (MA) for the same product in at least one other Member State. The Member State that has already authorized the product is known as the Reference Member State (RMS). The RMS submits their evaluation of the product to other Member State/s, these are known as Concerned Member State/s (CMS). If the applicant is successful, the CMS will then issue a MA for that product permitting the marketing of that product in their country. [After Heads of Medicines Agencies (HMA) website http://www.hma.eu/medicinesapprovalsystem.html] See also Reference Member State (RMS) and Concerned Member State (CMS).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "n-of-1 study", "definition": "CDISC Definition: A trial in which an individual subject is administered a treatment repeatedly over a number of episodes to establish the treatment's effect in that person, often with the order of experimental and control treatments randomized.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "natural language processing", "definition": "CDISC Definition: The use of algorithms to determine properties of natural, human language so that computers can understand what humans have written or said. NLP includes teaching computer systems how to extract data from bodies of written text, translate from one language to another, and recognize printed or handwritten words. NOTE: NLP is the field that allows for our everyday use of virtual assistants such as Siri, Alexa, or Google. [After DeepAI Definitions] See also artificial intelligence (AI).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "natural language", "definition": "CDISC Definition: Language as used in ordinary communications among humans and distinguished from controlled terminologies and structured languages used exclusively for communication and interoperability among machines.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "NCI Enterprise Vocabulary Services (EVS)", "definition": "CDISC Definition: A US national resource to house and maintain a number of health-related glossaries and controlled vocabularies under strict versioning. Provides resources and services to meet the National Cancer Institute's needs for controlled terminology, and to facilitate the standardization of terminology and information systems across the NCI and the larger biomedical community.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "negative test result", "definition": "CDISC Definition: The finding of the test indicates the criteria for the condition tested were not met. NOTE: The test condition and the applied criteria are dependent on the specific case, as defined in the test design. The test results must be validated by comparison to a recognized reference standard.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "neoadjuvant therapy", "definition": "CDISC Definition: Therapy administered prior to the primary treatment for the purpose of making the primary treatment more effective. [After NCI Thesaurus] See also adjuvant therapy, treatment.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "neural network", "definition": "CDISC Definition: A computational model inspired by the structure of the human brain. It is composed of interconnected nodes, or \"neurons\" organized into layers: an input layer that receives data, one or more hidden layers that process and identify patterns in the data, and an output layer that presents the final network output. [FDA Digital Health and Artificial Intelligence Glossary - Educational Resource, 09/26/2024] See also machine learning, deep learning, artificial intelligence.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "new chemical entity (NCE)", "definition": "CDISC Definition: A drug that contains no active moiety that has been approved by the US FDA. [US FDA (CDER) guidance, New Chemical Entity Exclusivity Determinations for Certain Fixed Combination Drug Products Guidance for Industry, October 2014]. See also new molecular entity (NME), moiety.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "New Drug Application (NDA)", "definition": "CDISC Definition: An application to FDA for a license to market a new drug in the United States.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "new molecular entity (NME)", "definition": "CDISC Definition: A drug or biologic whose active ingredient contains no active moiety that has been previously approved by the US FDA. NOTE: Certain drugs are classified as new molecular entities (\"NMEs\") for FDA review administrative purposes. [After US FDA. (04/08/2024). Novel Drug Approvals at FDA. Retrieved from URL https://www.fda.gov/drugs/development-approval-process-drugs/novel-drug-approvals-fda#:~:text=Certain%20drugs%20are%20classified%20as%20new%20molecular%20entities,products%20frequently%20provide%20important%20new%20therapies%20for%20patients.%20Webpage%20access%202024/04/18] See also new chemical entity (NCE), moiety.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "new safety information", "definition": "CDISC Definition: Previously unknown safety information derived from: (A) a clinical trial, an adverse event report, a post-approval study, or peer-reviewed biomedical literature; (B) the post-market risk identification and analysis system (REMS); or, (C) other scientific data regarding, (i) a serious risk or unexpected serious risk associated with use of the drug since the drug was approved, since the REMS was required or last assessed, or (ii) the effectiveness of the approved REMS for the drug obtained since the last assessment of such strategy. [After 21 CFR, Part 505-1(b)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "NOEL (no observable effect level)", "definition": "CDISC Definition: The dose of an experimental drug given preclinically that does not produce an observable toxicity.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "nomenclature", "definition": "CDISC Definition: Application of naming conventions. Compare to vocabulary, terminology.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "non-confirmatory result", "definition": "CDISC Definition: In a trial, typically phase 3, results that fail to achieve statistical significance and therefore fail to confirm the preliminary evidence from other trials that a drug is safe and effective for use for the intended indication and population. NOTE: Non-confirmatory trial results provide useful scientific information. [After ICH E8] See also confirmatory trial.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "non-inferiority (NI) trial", "definition": "CDISC Definition: A type of controlled trial to demonstrate that the new treatment is not less effective than the active control by a specified amount. [After Non-Inferiority Clinical Trials to Establish Effectiveness. FDA Guidance for Industry. November 2016]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "non-interventional study", "definition": "CDISC Definition: A study where the medicinal product(s) is (are) prescribed in the usual manner in accordance with the terms of the marketing authorization. The assignment of the patient to a particular therapeutic strategy is not decided in advance by a trial protocol but falls within current practice and the prescription of the medicine is clearly separated from the decision to include the patient in the study. No additional diagnostic or monitoring procedures shall be applied to the patients and epidemiological methods shall be used for the analysis of collected data. [Clinical Trial Directive EC/20/2001 definitions]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "nonclinical study", "definition": "CDISC Definition: Biomedical studies not performed on human subjects. [ICH E6 (R2)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "not approvable letter", "definition": "CDISC Definition: An official communication from FDA to inform a sponsor of a marketing application that the important deficiencies described in the letter preclude approval unless corrected.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Notified Body (NB)", "definition": "CDISC Definition: A private institution charged by the Competent Authority with verifying compliance of medical devices (not drugs) with the applicable Essential Requirements stated in the Medical Device Directive. This process, called Conformity Assessment, has EU-wide validity once completed by the NB.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "null hypothesis", "definition": "CDISC Definition: The assertion that no true association or difference in the study outcome or comparison of interest between comparison groups exists in the larger population from which the study samples are obtained. NOTE: A null hypothesis (for example, \"subjects will experience no change in blood pressure as a result of administration of the test product\") is used to rule out every possibility except the one the researcher is trying to prove, and is used because most statistical methods are less able to prove something true than to provide strong evidence that it is false. The assertion that no true association or difference in the study outcome or comparison of interest between comparison groups exists in the larger population from which the study samples are obtained. See also research hypothesis. [from AMA Manual of Style]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Nuremberg Code", "definition": "CDISC Definition: A code of ethics set forth in 1947 for the conduct of medical research, with the express purpose of protecting human medical research subjects.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "objective", "definition": "CDISC Definition: The reason for performing a study in terms of the scientific questions to be answered by the analysis of data collected during the study. [After ICH E8] See also primary objective, secondary objective.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "observation", "definition": "CDISC Definition: An assessment of patient condition in data collected on an individual patient or group of patients. Note: In SDTM, an observation refers to a discrete piece of information collected during a study, e.g., measures used to assess an outcome. [SDTM] See also variable, outcome.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "observational study", "definition": "CDISC Definition: Study in which the researchers observe the effect of a risk factor (e.g., exposure), diagnostic test, treatment or other covariate within a study population, and where the investigator does not assign specific interventions. NOTE: Major subtypes of observational studies are cohort study, case-control study, and cross-sectional study. [After Observational studies: Cohort and Case-Control Studies, JW Song, KC Chung Plast Reconstru Surg, 2010 Dec; After A Dictionary of Epidemiology (5th ed.), Porta M, ed. (2014)., Oxford University Press, New York] See also investigational clinical trials, cohort study, case-control study, cross-sectional study.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "observer assessment", "definition": "CDISC Definition: An assessment of patient condition made by an observer (investigator, nurse, clinician, family member, etc.). NOTE: Distinguished from self-assessment. The observer relies on his or her judgment to assess the subject. an interviewer simply capturing subject self assessments is not making an observer assessment. Compare to PRO, proxy assessment.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "observer-reported outcome (ObsRO)", "definition": "CDISC Definition: A type of clinical outcome assessment. A measurement based on a report of observable signs, events or behaviors related to a patient's health condition by someone other than the patient or a health professional. [After BEST Resource]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "off-label", "definition": "CDISC Definition: Use of a medical product (such as a drug, biologic, or device) that is unapproved in the region of interest. Note: Not approved for the indication or not approved for the conditions mentioned in the approval (e.g., age group of subjects, dosage, or route of administration). [After FDA Investigational New Drug Safety Reporting Requirements for Human Drug and Biological Products and Safety Reporting Requirements for Bioavailability and Bioequivalence Studies in Humans, Final Rule Sept 2010; After EMA Glossary of Regulatory terms]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "official protocol title", "definition": "CDISC Definition: The formal descriptive name for the protocol that contains key elements of the study. NOTE: The official protocol title should include the study acronym, if applicable [WHO ICTRP]. The official protocol title should be sufficiently different from other protocol titles to create brevity with specificity [After NIH Protocol Template].", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "ontology", "definition": "CDISC Definition: An explicit formal specification of how to represent relationships among objects, concepts, and other entities that belong to a particular domain of experience or knowledge. See also terminology.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "open to enrollment", "definition": "CDISC Definition: The status of a study such that a subject can be enrolled into that study. NOTE: Registry terminology in common use is \"open to recruitment\"; however, recruitment can begin upon IRB approval of the site; whereas enrollment requires availability of study supplies, subject informed consent, etc., to allow participation of eligible subjects.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "open-label study", "definition": "CDISC Definition: A study in which subjects and investigators know which product each subject is receiving; opposite of a blinded or double-blind study. See blinding.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "operational model", "definition": "CDISC Definition: The set of CDISC data standards (including ODM and LAB) used to capture and archive data from clinical trials.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "opinion (in relation to independent ethics committee)", "definition": "CDISC Definition: The judgment and/or the advice provided by an independent ethics committee. [ICH E6 Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "original data", "definition": "CDISC Definition: The first recorded study data values. NOTE: FDA is allowing original documents and the original data recorded on those documents to be replaced by copies provided that the copies have been verified as identical in content and meaning. (see FDA Compliance Policy Guide 7150.13). [Modified from CSUICI] See also certified copy, source.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "other serious (important medical events)", "definition": "CDISC Definition: A category of important medical events that may not be immediately life-threatening, result in death, or hospitalization, but may jeopardize the patient or may require intervention to prevent one of the outcomes criteria events requiring assessment for potential regulatory reporting as a serious adverse event. Note: These \"Other serious\" events require medical and scientific judgement in evaluating the need for reporting as a serious adverse event. Examples include allergic bronchospasm (a serious problem with breathing) requiring treatment in an emergency room, serious blood dyscrasias (blood disorders) or seizures/convulsions that do not result in hospitalization. The development of drug dependence or drug abuse would also be examples of important medical events. [after FDA 310.305, ICH E2A] See also serious adverse event.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "outcome (of adverse event)", "definition": "CDISC Definition: Refers to the resolution of an adverse event. NOTE: often denoted using a pick list from a controlled terminology such as: Recovered/resolved, recovering/ resolving, not recovered/not resolved, recovered/resolved with sequelae, fatal, or unknown. [SDTM events class of observation]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "outcome measure", "definition": "CDISC Definition: Specific key measurement(s) or observation(s) used to determine the effect of experimental variables on the participants in a study, or for observational studies, to describe patterns of diseases or traits or associations with exposures, risk factors or treatment. (After BRIDG)", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "outcome of study", "definition": "CDISC Definition: The findings from a research study including data, statistical analyses, and clinical interpretation. [After ICH E3] See also clinical study report, outcome, result synopsis, study results.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "outcome", "definition": "CDISC Definition: A measureable characteristic that is influenced or affected by an individual's baseline state or an intervention, as in a clinical trial or other exposure. NOTE: Outcome can be a result of analysis and is more general than endpoint in that it does not necessarily relate to a planned objective of the study outcome (SDTM). [After BEST Resource] See also variable, observation.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "outcomes research", "definition": "CDISC Definition: Research concerned with benefits, financial costs, healthcare system usage, risks, and quality of life as well as their relation to therapeutic interventions. NOTE: Usually distinguished from research conducted solely to determine efficacy and safety. [Guyatt et al., 1993] See also pharmacoeconomics, quality of life.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "outliers", "definition": "CDISC Definition: Values outside of an expected range.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "overdose", "definition": "CDISC Definition: Administration of a quantity of a medicinal product given per administration or cumulatively, which is above the maximum recommended dose according to the authorised product information. [After, EU Guideline on good pharmacovigilance practices (GVP)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "p-value", "definition": "CDISC Definition: The probability that the observed data could have arisen by chance when the interventions did not differ. [After AMA Manual of Style] See also null hypothesis.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "packaging", "definition": "CDISC Definition: The material, both physical and informational, that contains or accompanies a marketed or investigational therapeutic agent once it is fully prepared for release to patients and/or subjects in clinical trials", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "pairing", "definition": "CDISC Definition: A method by which subjects are selected so that two subjects with similar characteristics (for example, weight, smoking habits) are assigned to a set, but one receives Treatment A and the other receives Treatment B. See also matched-pair design.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "palliative therapy", "definition": "CDISC Definition: Therapy administered to relieve the symptoms and reduce the suffering caused by advanced, progressive disease. [After NCI Thesaurus] See also treatment.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "pandemic", "definition": "CDISC Definition: An epidemic occurring worldwide, or over a very wide area, crossing international boundaries, and usually affecting a large number of people. [A dictionary of epidemiology, edited for the International Epidemiological Association by John M. Last, Oxford University Press 2001]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "parallel trial", "definition": "CDISC Definition: A trial design in which subjects are randomised to one of two or more arms, with each arm being allocated a different intervention for the duration of the study. NOTE: These interventions will include the investigational product at one or more doses and one or more controls, such as placebo, an active comparator, or both. [After ICH E9; after NIH National Center for Advancing Translational Sciences, Toolkit for Patients-Focused Therapy Development, Glossary] See also randomized controlled trial (RCT), crossover trial.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "parameter", "definition": "CDISC Definition: A variable in a model, or a variable that wholly or partially characterizes a probability distribution (mathematics and statistics). NOTE: in clinical trials the term is often used synonymously with 'variable' for factual information (age, date of recovery), measurements, and clinical assessments. it is most appropriately linked to statistical conventions and as a numeric characteristic of a population. Parameters are rarely known and are usually estimated by statistical computation from samples. Thus the term is narrower than variable. [Parexel Barnett; ADaM; HyperStat Online] See also variable, outcome.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "participant", "definition": "CDISC Definition: A person or entity with a role in a clinical study. NOTE: Participants can be human subjects or study personnel. The term \"participant\" is used with growing frequency in some clinical and patient-facing documents like the informed consent form, Plain Language Summaries of study results, and publications. Subject or patient are terms used in regulatory guidelines, databases, other clinical research documents, or systems to refer to study participants. See also human subject, patient, study participant.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "password aging", "definition": "CDISC Definition: A practice applying to multi-user computer systems where the validity of a password expires after a certain pre-set period. NOTE: FDA requires that passwords that are part of electronic signatures be \"periodically checked, recalled or revised,\" but does not mandate password aging. [After NIST, 21 CFR 11]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "patient file", "definition": "CDISC Definition: One that contains demographic, medical, and treatment information about a patient or subject. It may be paper- or computer-based or a mixture of computer and paper records.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "patient", "definition": "CDISC Definition: Person under a physician's care for a particular disease or condition. NOTE: A subject in a clinical trial is not necessarily a patient, but a patient in a clinical trial is a subject. Although often used interchangeably as a synonym for subject, a healthy volunteer is not a patient. See also human subject, clinical research subject, healthy volunteer, participant.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "patient-reported outcome (PRO)", "definition": "CDISC Definition: A type of clinical outcome assessment. A measurement based on a report that comes directly from the patient (i.e., study subject) about the status of a patient's health condition without amendment or interpretation of the patient's response by a clinician or anyone else. NOTE: A PRO can be measured by self-report or by interview provided that the interviewer records only the patient's response. Symptoms or other unobservable concepts known only to the patient can only be measured by PRO measures. PROs can also assess the patient perspective on functioning or activities that may also be observable by others. [After BEST Resource]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "peer review", "definition": "CDISC Definition: Primarily, the critical assessment by experts (who are usually not part of the editorial staff) of manuscripts submitted to journals. NOTE: Because unbiased, independent, critical assessment is an intrinsic part of all scholarly work, including scientific research, peer review is an important extension of the scientific process. [After ICMJE Recommendations]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "per-protocol analysis set", "definition": "CDISC Definition: The set of data generated by the subset of subjects who complied with the protocol sufficiently to ensure that these data would be likely to exhibit the effects of treatment according to the underlying scientific model. [ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "performance outcome (PerfO)", "definition": "CDISC Definition: A PerfO is a measurement based on a task(s) performed by a patient according to instructions that is administered by a health care professional. NOTE: Performance outcomes require patient cooperation and motivation. These include measures of gait speed (e.g., timed 25 foot walk test), memory recall, or other cognitive testing (e.g., digit symbol substitution test). [After 1. FDA Clinical Outcome Assessment (COA) Glossary; 2. After BEST Resource]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "performed activity", "definition": "CDISC Definition: Clinical trial events as they actually occurred (as compared with events planned in the protocol).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "period effect", "definition": "CDISC Definition: An effect occurring during a period of a trial in which subjects are observed and no treatment is administered.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "permanent data", "definition": "CDISC Definition: Data that become or are intended to become part of an electronic record in relation to a regulatory submission. NOTE: Any changes made to such permanent data are recorded via an audit trail so that prior values are not obscured.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "permissible values", "definition": "CDISC Definition: Limited universe of options for data items. (e.g., drop-down menus, codelists, pick lists).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "personal data retention", "definition": "CDISC Definition: The act of maintaining or holding personal data and those obligations on the part of controllers to retain personal data for certain specified purposes. [After European Data Protection Supervisor, Glossary, https://www.edps.europa.eu/data-protection/data-protection/glossary/d_en, Accessed 2025-02-27] See also personal data, processing (personal data), data controller.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "personally identifiable information (PII)", "definition": "CDISC Definition: Any information about an individual maintained by an agency (or group) including but not limited to, education, financial transactions, medical history, and criminal or employment history, which can be used to distinguish or trace an individual's identity, such as name, social security number, date and place of birth, mother's maiden name, biometric records, etc., including any other personal information that is linked or linkable to an individual. Used in US [NIST Special publication 800-122] See also personal data.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "pharmaceutical product", "definition": "CDISC Definition: Qualitative and quantitative composition of a medicinal product in the dose form authorized by the regulatory authority for administration to patients, and as represented with any corresponding regulated product information. NOTE: A medicinal product may contain one or more pharmaceutical products. In many instances, the pharmaceutical product is the manufactured item. However, there are instances where the manufactured item undergoes further preparation before being administered to the patient (as the pharmaceutical product). [After ISO 11615:2017, 3.1.60]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "pharmacodynamics", "definition": "CDISC Definition: Branch of pharmacology that studies reactions between drugs and living structures, including the physiological responses to pharmacological, biochemical, physiological, and therapeutic agents.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "pharmacoeconomics", "definition": "CDISC Definition: Branch of economics that applies cost-benefit, cost-utility, cost-minimization, and cost-effectiveness analyses to assess the utility of different pharmaceutical products or to compare drug therapy to other treatments.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "pharmacogenetic test", "definition": "CDISC Definition: An assay intended to study interindividual variations in DNA sequence related to drug absorption and disposition or drug action. Compare to pharmacogenomic test.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "pharmacogenetics", "definition": "CDISC Definition: Study of the way drugs interact with genetic makeup or the study of genetic response to a drug.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "pharmacogenomic test", "definition": "CDISC Definition: An assay intended to study interindividual variations in whole genome or candidate gene maps, biomarkers, and alterations in gene expression or inactivation that may be correlated with pharmacological function and therapeutic response. Compare to pharmacogenetic test.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "pharmacogenomics", "definition": "CDISC Definition: Science that examines inherited variations in genes that dictate drug response and explores the ways such variations can be used to predict whether a person will respond favorably, adversely, or not at all to an investigational product.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "pharmacokinetics", "definition": "CDISC Definition: Study of the processes of bodily absorption, distribution, metabolism, and excretion (ADME) of medicinal products.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "pharmacology", "definition": "CDISC Definition: Science that deals with the characteristics, effects, and uses of drugs and their interactions with living organisms.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "pharmacovigilance", "definition": "CDISC Definition: Process and science of monitoring the safety of medicines and taking action to reduce their risks and increase their benefits. NOTE: Pharmacovigilance is a key public health function that comprises: collecting and managing data on the safety of medicines; looking at the data to detect 'signals' (any new or changing safety issue); evaluating the data and making decisions with regard to safety issues; acting to protect public health (including regulatory action);communicating with stakeholders; auditing of both the outcomes of action taken and the key processes involved. [After IDMP] See also postmarketing surveillance.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "phase (within a study)", "definition": "CDISC Definition: A stage in the sequence of activities in a clinical study (e.g., Screening, Randomization, Treatment, Follow-up). See also arm, visit, phase (of clinical development), epoch.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "phase 1", "definition": "CDISC Definition: The initial introduction of an investigational new drug into humans. Phase 1 studies are closely monitored and are most often conducted in normal healthy volunteer subjects but in specific cases also in patients. NOTE: These studies are designed to determine the metabolism and pharmacologic actions of the drug in humans, the side effects associated with increasing doses, and, if possible, to gain early evidence on effectiveness. During Phase 1, sufficient information about the drug's pharmacokinetics and pharmacological effects should be obtained to permit the design of well-controlled, scientifically valid Phase 2 studies. The total number of subjects and patients included in Phase 1 studies varies with the drug, but is generally in the range of 20 to 80. Phase 1 studies also include studies of drug metabolism, structure-activity relationships, and mechanism of action in humans, as well as studies in which investigational drugs are used as research tools to explore biological phenomena or disease processes. [after ICH E8; After ICH Topic E8 NOTE FOR GUIDANCE ON GENERAL CONSIDERATIONS FOR CLINICAL TRIALS, CPMP/ICH/291/95 March 1998] See also phase.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "phase 2", "definition": "CDISC Definition: Phase that includes the controlled clinical trials conducted to evaluate the safety and efficacy of the drug in a limited number of patients with the disease or condition under study. Objectives can be dose-ranging (dose-response, frequency of dosing), type of patients, or numerous other characteristics of safety and efficacy. [After 21 CRF Part 312.21 Phases of an investigation] See also phase, phase 2a, phase 2b.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "phase 2a", "definition": "CDISC Definition: Early Phase 2 trials that focus on a proof-of-concept assessment of efficacy and safety in a small number of patients. [After FDA Guidance for industry end of Phase 2a meetings, September 2009] See also phase, phase 2, phase 2b.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "phase 2b", "definition": "CDISC Definition: Later Phase 2 trials, in transition to Phase 3, where the study populations more closely reflect the population, dosage, and condition for intended use. [Clarification of FDA Guidance for industry end of Phase 2a meetings, September 2009; Discussion in Peter B. Gilbert. SOME DESIGN ISSUES IN PHASE 2B VERSUS PHASE 3 PREVENTION TRIALS FOR TESTING EFFICACY OF PRODUCTS OR CONCEPTS. Stat Med. 2010 May 10; 29(10): 1061-1071.] See also phase, phase 2, phase 2a.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "phase 3", "definition": "CDISC Definition: Phase that includes the controlled clinical trials intended to confirm safety and effectiveness, evaluate the overall benefit-risk relationship, and to provide substantial evidence for regulatory approval and labeling. NOTE: Phase 3 studies usually include from several hundred to several thousand subjects. [After ICH E8; Demonstrating Substantial Evidence of Effectiveness for Human Drug and Biological Products Draft Guidance for Industry. December 2019] See also phase, phase 3b.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "phase 3b", "definition": "CDISC Definition: Later Phase 3 trial done near the time of approval to elicit additional findings. NOTE: Dossier review may continue while associated Phase 3b trials are conducted. These trials may be required as a condition of regulatory authority approval. Phase 3a is in common usage but not reflected in regulatory guidance. See also phase, phase 3.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "phase 4", "definition": "CDISC Definition: Post-approval studies to delineate additional information about the drug's risks, benefits, and optimal use that may be requested by regulatory authorities in conjunction with marketing approval. NOTE: Phase 4 studies could include, but would not be limited to, studying different doses or schedules of administration than were used in Phase 2 studies, use of the drug in other patient populations or other stages of the disease, or use of the drug over a longer period of time. [after FDA CDER handbook, ICH E8] See also phase.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "phase 5", "definition": "CDISC Definition: Postmarketing surveillance to monitor product safety and efficacy. See also outcomes research, phase, postmarketing surveillance.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "phase", "definition": "CDISC Definition: A stage in the clinical research and development of a therapy from initial clinical trials to post-approval studies. NOTE: Clinical trials are generally categorized into four (sometimes five) phases. A therapeutic intervention may be evaluated in two or more phases simultaneously in different trials, and some trials may overlap two different phases. [21 CFR section 312.21; After ICH Topic E8 NOTE FOR GUIDANCE ON GENERAL CONSIDERATIONS FOR CLINICAL TRIALS, CPMP/ICH/291/95 March 1998] See also Phase 0-5, epoch (if reference is to a single trial), phase (within a study), clinical research and development.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "placebo", "definition": "CDISC Definition: A pharmaceutical preparation that does not contain the investigational agent and is generally prepared to be physically indistinguishable from the preparation containing the investigational product.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "placebo-controlled study", "definition": "CDISC Definition: A type of study in which a group receiving an experimental treatment is compared with a control group receiving placebo. [After 21 CFR 314.12; After ICH E10] See also placebo.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "plain language writing", "definition": "CDISC Definition: Writing in a way that helps readers understand the content in a document the first time they read it. Note: Plain writing is intended to be clear, concise, well-organized, and follow other best practices appropriate to the topic or field and the intended audience. [After Plain Writing Act of 2010, FDA]. See also health literacy.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "platform trial design", "definition": "CDISC Definition: A type of trial design under a master protocol framework that tests multiple, targeted therapies that may be adapted over the course of the study. NOTE: Platform trials often include an adaptive design that may eliminate or add treatments based on interim analysis. These trials may also include elements of basket or umbrella trials and may have no pre-determined end date. [After Woodcock J, LaVange LM. Master Protocols to Study Multiple Therapies, Multiple Diseases, or Both. N Engl J Med. 2017 Jul 6;377(1):62-70.; After US FDA, Master Protocols: Efficient Clinical Trial Design Strategies to Expedite Development of Oncology Drugs and Biologics Guidance for Industry, 2022] See also master protocol, adaptive design.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "platform trial", "definition": "CDISC Definition: A type of trial conducted under a master protocol and designed to test multiple, targeted therapies that may be adapted over the course of the study. NOTE: Platform trials often include an adaptive design that may eliminate or add treatments based on interim analysis. These trials may also include elements of basket or umbrella trials and may have no pre-determined end date. [After Woodcock J, LaVange LM. Master Protocols to Study Multiple Therapies, Multiple Diseases, or Both. N Engl J Med. 2017 Jul 6;377(1):62-70.; After US FDA, Master Protocols: Efficient Clinical Trial Design Strategies to Expedite Development of Oncology Drugs and Biologics Guidance for Industry, 2022] See also master protocol, adaptive design, platform trial design.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "population", "definition": "CDISC Definition: Any finite or infinite collection of subjects from which a sample is drawn for a study to obtain estimates for values that would be obtained if the entire population were sampled. [AMA style Manual]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "positive test result", "definition": "CDISC Definition: The finding of the test indicates the criteria for the condition tested were met. NOTE: The test condition and the applied criteria are dependent on the specific case, as defined in the test design. The test results must be validated by comparison to a recognized reference standard.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "postmarketing commitment (PMC)", "definition": "CDISC Definition: Studies that a sponsor has agreed to conduct, but that are not required by a statue or regulation. [FDA Webpage Postmarketing Requirements and Commitments: Introduction, 01/12/2016] See also postmarketing requirement. Compare to postmarketing requirement (PMR).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "postmarketing requirement (PMR)", "definition": "CDISC Definition: FDA-required postmarketing studies or clinical trials. [FDAAA; 21 CFR Part 314, subpart h; 21 CFR Part 601, subpart e] Compare to postmarketing commitment (PMC).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "postmarketing surveillance", "definition": "CDISC Definition: Ongoing safety monitoring of marketed drugs. See also Phase 4 studies, Phase 5 studies, pharmacovigilance.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "pragmatic trial", "definition": "CDISC Definition: A trial that compares health interventions in a diverse population representing clinical practice. These trials inform a clinical or policy decision by providing evidence for adoption of the intervention into real-world clinical practice. NOTE: These trials may or may not be randomized and can be large simple studies. [After GetReal - Project No. 115546l, WP1: Deliverable D1.3, Glossary of Definitions of Common Terms; Ford I, Norrie J. Pragmatic trials. N Engl J Med. 2016;375:454-63.] See also Real-World Data (RWD), Real-World Evidence (RWE), confirmatory trial.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "pre-approval access", "definition": "CDISC Definition: A potential pathway for a patient with an immediately life-threatening condition or serious disease or condition to gain access to an investigational medical product (drug, biologic, or medical device) for treatment outside of clinical trials when no comparable or satisfactory alternative therapy options are available. NOTE: The intent is treatment, as opposed to research. Individual, Intermediate-size, and Widespread Use Expanded Access, also Emergency IND, are all programs administered under FDA guidelines. Additionally, the US Right-to-Try Act, which is independent of FDA, expands access. [FDA Expanded Access: Information for Physicians]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "pre-market approval application (PMA)", "definition": "CDISC Definition: An application to FDA for a license to market a new device in the United States.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "preamble", "definition": "CDISC Definition: A section preceding the text of a final FDA regulation published in the Federal Register. NOTE: \"The preamble is to contain a thorough and comprehensible explanation of the reasons for the Commissioner's decision on each issue\" raised in comments submitted in response to the proposed regulation. [After 21CFR10.40]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "preclinical studies", "definition": "CDISC Definition: Animal studies that support Phase 1 safety and tolerance studies and must comply with good laboratory practice (GLP). NOTE: Data about a drug's activities and effects in animals help establish boundaries for safe use of the drug in subsequent human testing (clinical studies or trials).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "prevalence", "definition": "CDISC Definition: The number of the existing cases of disease or injury in a defined population at a given point in time. NOTE: The relation between incidence and prevalence varies among diseases. There may be low incidence and a high prevalence - as for diabetes - or a high incidence and a low prevalence - as for the common cold. [After Basic Epidemiology, R. Bonita and others, WHO 2006; After Principles of Epidemiology in Public Health Practice, Third Edition. An Introduction to Applied Epidemiology and Biostatistics, Lesson 3: Measures of Risk, CDC 2012] Compare to incidence. See also morbidity rate, morbidity, mortality, incidence rate.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "primary completion date", "definition": "CDISC Definition: The date that the final subject was examined or received an intervention for the purposes of final collection of data for the primary outcome [measure], whether the clinical trial concluded according to the pre-specified protocol or was terminated. NOTE: The primary completion date may or may not be the same as the study completion date. [ClinicalTrials.gov]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "primary objective", "definition": "CDISC Definition: The main scientific question(s) the study is designed to answer. [After ICH E8; ICH E6 6.3] See also objective, secondary objective, exploratory objective.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "primary outcome variable", "definition": "CDISC Definition: An outcome variable specified in the protocol to be of greatest importance to the primary objective of the trial, usually the one used in the sample size calculation. NOTE: Differences between groups in the primary and secondary variable(s) are believed to be the result of the group-specific interventions. [CONSORT Statement] See also primary objective, outcome, endpoint.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "principal investigator", "definition": "CDISC Definition: The study investigator who has the primary responsibility for the conduct of a study and for the study-related personnel at the participating site(s). NOTE: While the term is defined inconsistently within some guidance, in common usage, the term is used as defined above and the accountabilities are assigned by the sponsor. [After ICH E6 and WHO]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "privacy breach", "definition": "CDISC Definition: A privacy breach is the loss of, unauthorized access to, or disclosure of, personal information. [Office of the Privacy Commissioner of Canada] See also serious breach.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "probability", "definition": "CDISC Definition: The number of times an event is expected to occur in a study group divided by the number of individuals being studied. [After AMA Manual of Style]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "processing (personal data)", "definition": "CDISC Definition: Any operation or set of operations that are performed on personal data or on sets of personal data, whether or not by automated means. NOTE: Examples are: collection, recording, organization, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction. [After Article 4 GDPR Definitions] See also personal data, data controller, data processor, subprocessor.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "product dose", "definition": "CDISC Definition: The amount of a product administered in a single dose at a point in time. Usually expressed as a weight, volume, or a number of items (e.g., dosage forms) administered. The expression refers to the substance(s) contained in the Product.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "progression-free survival", "definition": "CDISC Definition: The length of time during and after treatment in which a patient is living with a disease that does not get worse. Progression-free survival may be used in a clinical study or trial to help find out how well a new treatment works. [NCI]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "PROMIS", "definition": "CDISC Definition: NIH-sponsored project for the development and evaluation of PRO item banks and computer adaptive testing for pain, fatigue, physical function, social function, and emotional well-being. [NIH]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "prophylaxis", "definition": "CDISC Definition: Practices or interventions used to maintain health and prevent disease or injury. NOTE: Involves limiting the chances of illness, injuries, or reduced health status from occurring (primary prevention) and, when diseases occur, supporting people to manage them as effectively as possible in order to prevent progression or recurrence (secondary prevention). Prevention is achieved by applying vaccines, behavioral changes, life style changes, improved nutrition, etc. [After Prevention is better than cure, UK Department of Health and Social Care, Nov 5th 2018. After Primary, secondary and tertiary prevention, Institute for Work & Health, Toronto April 2015]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "proprietary name", "definition": "CDISC Definition: A commercial name granted by a naming authority for use in marketing a drug/device product. [SPL; FDA Best Practices in Developing Proprietary Names for Human Prescription Drug Products, Guidance for Industry, December 2020] See also generic name, international nonproprietary name (INN), established name, medicinal product name, compendial name.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "prospective study", "definition": "CDISC Definition: A study with planned observations collected predominantly after the start of the study (i.e. forward-looking). Note: Examples are interventional clinical trials, including clinical trials with an adaptive trial design. [After ClinicalTrials.gov] See also retrospective study, interventional clinical trial, observational study, adaptive design, clinical study.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "protected personal data (PPD)", "definition": "CDISC Definition: Data relating to an identified or identifiable natural person ('data subject'); an identifiable person is one who can be identified, directly or indirectly, in particular by reference to an identification number or to one or more factors specific to his physical, physiological, mental, economic, cultural, or social identity. NOTE: In a clinical trial setting, data refers to collected information. [After EU Directive 95/46/EC] See also processing (personal data), sensitive personal data, data controller, data processor, subprocessor.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "protocol amendment(s)", "definition": "CDISC Definition: A written description of a change(s) to or formal clarification of a protocol. NOTE: If a protocol modification is substantial, it may require notification to the regulatory authority. For example, substantial impacts on the safety or rights of the subjects or on the reliability and robustness of the data generated in the clinical trial. [ICH E3; ICH E6 (R2) Glossary 1.45]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "protocol approval (Sponsor)", "definition": "CDISC Definition: Sponsor action at the completion of protocol development that is marked when the signature of the last reviewer on the protocol approval form has been obtained, signifying that all reviewer changes to the protocol have been incorporated. NOTE: Approval by the sponsor usually initiates secondary approvals by IRBs, regulatory authorities, and sites. Protocol amendments usually also require a cycle of approval by sponsor and study staff prior to taking effect.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "protocol deviation", "definition": "CDISC Definition: A variation from processes or procedures defined in a protocol. Deviations usually do not preclude the overall evaluability of subject data for either efficacy or safety, and are often acknowledged and accepted in advance by the sponsor. NOTE: Good clinical practice recommends that deviations be summarized by site and by category as part of the report of study results so that the possible importance of the deviations to the findings of the study can be assessed. Compare to protocol violation. [See ICH E3]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Protocol Identifying Number", "definition": "CDISC Definition: Any of one or more unique codes that refers to a specific protocol. NOTE: There may be multiple numbers (National number, coop group number). [EudraCT]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "protocol referenced documents", "definition": "CDISC Definition: Documents that optionally supplement the ICH GCP recommended sections of a protocol giving background information and rationale for the trial. [After ICH E6 1.44] See also protocol.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "protocol title", "definition": "CDISC Definition: The name of a study protocol. NOTE: In most cases the protocol title is the same as the study title but in certain cases the titles may be different. See also official protocol title, public protocol title, master protocol.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "protocol violation", "definition": "CDISC Definition: A significant departure from processes or procedures that were required by the protocol. Violations often result in data that are not deemed evaluable for a per-protocol analysis, and may require that the subject(s) who violate the protocol be discontinued from the study. Compare to protocol deviation.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "protocol", "definition": "CDISC Definition: A document that describes the objective(s), design, methodology, statistical considerations, and organization of a trial. The protocol usually also gives the background and rationale for the trial, but these could be provided in other protocol referenced documents. Throughout the ICH GCP Guideline the term protocol refers to protocol and protocol amendments. [ICH E6 Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "proxy (as an origin of outcome measures)", "definition": "CDISC Definition: A proposed standardized qualifier variable to describe the origin of observations of the Findings class resulting from outcomes measures. Proxy describes outcome data furnished by someone other than the patient and distinguishes the origin of the outcome from a self-report (PRO) directly from the patient. NOTE: The term proxy helps qualify outcomes measures that record feelings and symptoms reported by the patient but not recorded directly. [CDISC (extension of SDTM based on Table 2 Patrick, D.L., 2003)] See also observer assessment.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "proxy respondent", "definition": "CDISC Definition: Someone other than the patient who is responding about the patient on behalf of the patient, not as an observer. [Patrick, D.L., 2003; DIA ePRO Workgroup] Compare to observer assessment.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "proxy-reported outcome", "definition": "CDISC Definition: A measurement based on a report by someone other than the patient reporting as if he or she is the patient. NOTE: A proxy-reported outcome is not a patient-reported outcome (PRO). FDA does not consider a proxy-reported outcome as a valid endpoint. [After FDA Clinical Outcome Assessment (COA) Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "pseudonymization", "definition": "CDISC Definition: A privacy preservation technique that both replaces the direct association with a data subject and adds an association between a particular set of characteristics relating to the data subject and one or more pseudonyms. Typically, pseudonymization is implemented by replacing direct identifiers (like the subject's name) with a pseudonym such as a randomly generated value. [ISO/TS 25237:2008]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "psychometric reliability", "definition": "CDISC Definition: The degree to which a psychometric 'instrument' is free from random error either by testing the homogeneity of content on multi-item tests with internal consistency evaluation or testing the degree to which the instrument yields stable scores over time. NOTE: Reliability pertains to questions concerning whether an instrument is accurate, repeatable, and sensitive. Reliability is distinguished from validation, which answers whether the instrument (e.g., questionnaire) actually measure the selected \"construct\" (latent variable). For example a balance (scale) is easily understood as a possibly valid instrument to measure body weight. Its reliability would be assessed by measuring the sensitivity, repeatability, and accuracy of the balance. The validity of using the balance for a particular purpose could then be established by comparing the measured reliability to the reliability required for that purpose. [After Patrick, D.L., 2003] Compare to psychometric validation. See also validation, instrument.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "psychometric validation", "definition": "CDISC Definition: The specialized process of validating questionnaires used in outcomes research to show that they measure what they purport to measure. NOTE: Several types of validity are distinguished. For example, [Guyatt et al., 1993; DIA ePRO Workgroup] See also validation; compare to psychometric reliability.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "psychometrics", "definition": "CDISC Definition: The science of assessing the measurement characteristics of scales that assess human psychological characteristics.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "public protocol title", "definition": "CDISC Definition: The brief descriptive name for the protocol that is intended for the public in easily understood language. NOTE: Public title may also be referred to as a short title or brief title. [Segen's Medical Dictionary] See also official protocol title, protocol title.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "qualitative variable", "definition": "CDISC Definition: One that cannot be measured on a continuum and represented in quantitative relation to a scale (race or sex, for example). Data that fit into discrete categories according to their attributes.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "quality assurance (QA)", "definition": "CDISC Definition: All those planned and systematic actions that are established to ensure that the trial is performed and the data are generated, documented (recorded), and reported in compliance with good clinical practice (GCP) and the applicable regulatory requirement(s). [ICH E6 R2 Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "quality control (QC)", "definition": "CDISC Definition: The operational techniques and activities undertaken within the quality assurance system to verify that the requirements for quality of the trial related activities have been fulfilled. [ICH E6 R2 Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "quality of life (QoL)", "definition": "CDISC Definition: A broad ranging concept that incorporates an individual's physical health, psychological state, level of independence, social relationships, personal beliefs, and their relationships to salient features of the environment. NOTE: Quality of life is one way to measure the benefits or negative impacts of an \"improvement\" measured in terms of a physiological or psychological symptom. QoL research seeks to quantify what an intervention means to a patient's sense that their life has changed. NOTE: See also definition from FDA eCOA Glossary. [WHO Group, 1994]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "quantitative variable", "definition": "CDISC Definition: One that can be measured and reported numerically to reflect a quantity or amount, ideally on a continuum.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "query management", "definition": "CDISC Definition: Ongoing process of data review, discrepancy generation, and resolving errors and inconsistencies that arise in the entry and transcription of clinical trial data.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "query resolution", "definition": "CDISC Definition: The closure of a query usually based on information contained in a data clarification.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "query", "definition": "CDISC Definition: A request for clarification on a data item collected for a clinical trial; specifically a request from a sponsor or sponsor's representative to an investigator to resolve an error or inconsistency discovered during data review.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "questionnaire", "definition": "CDISC Definition: A set of questions or items shown to a respondent in order to get answers for research purposes. [PRO Draft Guidance] See also instrument, survey.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "race", "definition": "CDISC Definition: An arbitrary classification of a taxonomic group that is a division of a species. It usually arises as a consequence of geographical isolation within a species and is characterized by shared heredity, physical attributes and behavior, and in the case of humans, by common history, nationality, or geographic distribution. (NCI)", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "radiopharmaceutical medicinal product", "definition": "CDISC Definition: Any medicinal product which, when ready for use, contains one or more radionuclides (radioactive isotopes) included for a medicinal purpose. [DIRECTIVE 2001/83/EC Article 1.(11)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "random allocation", "definition": "CDISC Definition: Assignment of subjects to treatment (or control) groups in an unpredictable way. NOTE: in a blinded study, assignment sequences are concealed, but available for disclosure in the event a subject has an adverse experience.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "random number table", "definition": "CDISC Definition: Table of numbers with no apparent pattern used in the selection of random samples for clinical trials.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "random sample", "definition": "CDISC Definition: Members of a population selected by a method designed to ensure that each person in the target group has an equal chance of selection.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "randomization", "definition": "CDISC Definition: The process of assigning trial subjects to treatment or control groups using an element of chance to determine the assignments in order to reduce bias. NOTE: Randomization can be executed according to imposed rules to achieve desired distribution. For example, unequal randomization is used to allocate subjects into groups at a differential rate, e.g., three subjects may be assigned to a treatment group for every one assigned to the control group. [ICH E6 1.48] See also balanced study.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "randomized controlled trial (RCT)", "definition": "CDISC Definition: A well-controlled clinical trial in which subjects are assigned to treatment or control groups according to randomization principles. See randomization. [After FDA and Clinical Drug Trials : A Short History, S.White Junod, 2008; CONSORT statement] See also randomization, clinical trial, controlled study, adequate and well-controlled studies.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "raw data", "definition": "CDISC Definition: Data as originally collected. Distinct from derived. Raw data includes records of original observations, measurements, and activities (such as laboratory notes, evaluations, data recorded by automated instruments) without conclusions or interpretations. Researcher's records of subjects/patients, such as patient medical charts, hospital records, X-rays, and attending physician's notes. NOTE: These records may or may not accompany an application to a Regulatory authority, but must be kept in the researcher's file. See also eSource, source data, source documents.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "RCRIM", "definition": "CDISC Definition: Regulated Clinical Research and information Management, which is a Technical Committee within HL7 (an acronym pronounced \"arcrim\").", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Real-World Data (RWD)", "definition": "CDISC Definition: Data relating to patient health status and/or the delivery of health care routinely collected from sources other than traditional clinical trials. NOTE: Examples of sources include data derived from electronic health records (EHRs); medical claims and billing data; data from product and disease registries; patient-generated data, including from in-home-use settings; and data gathered from other sources that can inform on health status, such as mobile devices. [After 21 U.S.C. 355g(b)).5 and Framework for FDA's Real-World Evidence Program December 2018; FDA Draft Guidance, Data Standards for Drug and Biological Product Submissions Containing Real-World Data, OCTOBER 2021] See also Real-World Evidence (RWE)", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Real-World Evidence (RWE)", "definition": "CDISC Definition: The clinical evidence derived from analysis of Real-World Data (RWD) regarding the usage and potential benefits or risks of a medical product. [After FDA Guidance: Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices. August 31, 2017; IMI-GetReal Glossary Workgroup, 2016 GetReal - Project No. 115546, WP1: Deliverable D1.3; FDA Draft Guidance, Data Standards for Drug and Biological Product Submissions Containing Real-World Data, OCTOBER 2021] See also Real-World Data (RWD).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "rechallenge", "definition": "CDISC Definition: To reintroduce a previously withdrawn or temporarily discontinued medical intervention to the same participant. NOTE: Rechallenge requirements may be described in the protocol. [After ICH M11]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "reconstruction (of a study)", "definition": "CDISC Definition: For eClinical trials FDA expects archival trial records to support review of the data as well as the processes used for obtaining and managing the data so that the trustworthiness of results obtained can be evaluated. NOTE: Reconstruction from records should support evaluation of the operation and validity of computerized systems and the conformance of the systems to applicable regulations during design and execution of the trial as well as during the period of record retention. [from CSUCT VI D, 21 CFR Parts 11, 312]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "record retention", "definition": "CDISC Definition: The act of maintaining or holding records for future use, often under the policies and procedures of a formally established regulatory records retention program, for some specified period of time. [After Rutgers University Institutional Planning and Operations, Records Management Definitions, https://ipo.rutgers.edu/business-services/records-management/definitions, accessed 2025-02-27; After ICH E6; After US FDA 21 CFR Part 11] See also personal data, processing (personal data).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "record", "definition": "CDISC Definition: In a regulated environment, documented information in any format that is subject to the requirements for data integrity, and should be controlled and maintained. NOTE: The requirements for data integrity are covered by the ALCOA plus principles. [After 21 CFR Part 11, Parts 210, 211, and 212; 21 CFR 312.61 and 312.62] See also data integrity, ALCOA plus, electronic record, control of electronic records, EHR (electronic health record), electronic personal health record (ePHR), EMR (electronic medical record), trustworthy (electronic records), source data, source document.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "recruitment (investigators)", "definition": "CDISC Definition: Process used by sponsors to identify, select, and arrange for investigators to serve in a clinical study.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "recruitment (subjects)", "definition": "CDISC Definition: Process used by investigators to find and enroll appropriate subjects (those selected on the basis of the protocol's inclusion and exclusion criteria) into a clinical study.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "recruitment period", "definition": "CDISC Definition: Time period during which subjects are or are planned to be enrolled in a clinical trial", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "recruitment target", "definition": "CDISC Definition: Number of subjects that must be recruited as candidates for enrollment into a study to meet the requirements of the protocol. in multicenter studies, each investigator has a recruitment target.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Reference information Model (RIM)", "definition": "CDISC Definition: An information model used as the ultimate defining reference for all HL7 standards. [HL7]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "reference member state (RMS)", "definition": "CDISC Definition: A classification of a Member States in the Mutual Recognition Procedure (MRP) in the European authorization route resulting in a mutually recognized product. The first Member State that has authorized the product in the RMS. [After Heads of Medicines Agencies (HMA) website http://www.hma.eu/medicinesapprovalsystem .html] See also Mutual Recognition Procedure (MRP) and Concerned Member State (CMS).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "regenerative medicine advanced therapy (RMAT) designation", "definition": "CDISC Definition: An FDA designation for regenerative medicine therapies to treat, modify, reverse, or cure serious conditions that are eligible for FDA's expedited programs if they meet the criteria for such programs. [After http://www.fda.gov/BiologicsBloodVaccines/GuidanceComplianceRegulatoryInformation/Guidances/default.htm] See also regenerative medicine therapy (RMT), regenerative medicine.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "regenerative medicine therapy (RMT)", "definition": "CDISC Definition: A treatment to repair or replace damaged cells, tissues, or organs, including cell therapies, therapeutic tissue engineering products, human cell and tissue products, and combination products using any such therapies or products. NOTE: RMT may include human gene therapies, genetically modified cells that lead to a sustained effect on cells or tissues, xenogeneic cell products, and any combination product where the biological product constituent part is a regenerative medicine therapy (biologic-device, biologic-drug, or biologic device-drug). [After S.H.Park, et al. In Situ Tissue Regeneration: Host Cell Recruitment and Biomaterial Design. Chapter 12. 2016; https://www.fda.gov/vaccines-blood-biologics/cellular-gene-therapy-products/resources-related-regenerative-medicine-therapies] See also regenerative medicine, regenerative medicine advanced therapy (RMAT) designation, cell therapy, gene therapy.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "regenerative medicine", "definition": "CDISC Definition: A broad field of medicine that endeavors to create living functional human cells, tissues, and organs to repair or replace tissues or organ function lost due to age, disease, damage, or congenital defects. [After S.H.Park, et al. In Situ Tissue Regeneration: Host Cell Recruitment and Biomaterial Design. Chapter 12. 2016; https://www.fda.gov/vaccines-blood-biologics/cellular-gene-therapy-products/resources-related-regenerative-medicine-therapies] See also regenerative medicine therapy (RMT), regenerative medicine advanced therapy (RMAT) designation, cell therapy, gene therapy.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "registry", "definition": "CDISC Definition: A data bank of information on clinical trials for drugs for serious or life-threatening diseases and conditions. NOTE: The registry should contain basic information about each trial sufficient to inform interested subjects (and their healthcare practitioners) how to enroll in the trial. [FDAMA 113]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "regulatory application", "definition": "CDISC Definition: Application made to a health authority to investigate, market, or license a new product or indication.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "regulatory authorities", "definition": "CDISC Definition: Bodies having the power to regulate. NOTE: In the ICH GCP guideline the term includes the authorities that review submitted clinical data and those that conduct inspections. These bodies are sometimes referred to as competent authorities. [ICH]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "relative risk", "definition": "CDISC Definition: A measure of the risk of an event happening in one group or situation, compared to the risk of the same event happening in another group or situation. [After NCI Thesaurus; After FDA Investigational New Drug Safety Reporting Requirements for Human Drug and Biological Products and Safety Reporting Requirements for Bioavailability and Bioequivalence Studies in Humans, Final Rule Sept 2010; After EMA Glossary of Regulatory Terms]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "remote clinical trial", "definition": "CDISC Definition: A trial designed to reduce or eliminate travel by subjects to an investigative site for treatment and completion of study related procedures by implementing virtual visits (e.g., via electronic communication). [After CTTI Recommendations: Decentralized Clinical Trials, September 2018] See also virtual, decentralized clinical trial.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "repeat rule", "definition": "CDISC Definition: Guide for repeating activities specified in protocol, including such features as the number of cycles and the criteria for stopping.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "replacement", "definition": "CDISC Definition: The act of enrolling a new study subject to compensate for a subject who is no longer participating.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "report", "definition": "CDISC Definition: A document that presents information in a structured format intended for a specific purpose and recipient. See also final report, interim clinical trial/study report, monitoring report, document (HL7), clinical study (trial) report.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "rescue medications", "definition": "CDISC Definition: Medicinal products identified in the protocol as those that may be administered to subjects when the efficacy of the investigational medicinal product (IMP) is not satisfactory, the effect of the IMP is too great and is likely to cause a hazard to the patient, or to manage an emergency situation. [After EU-CTR Recommendations from the expert group on clinical trials for the implementation of Regulation (EU) No 536/2014' dd 28 June 2017]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "research hypothesis", "definition": "CDISC Definition: A supposition or proposal based on observations or facts that requires further investigation or exploration to answer a research question. [After Shreffler J, Huecker MR. Hypothesis Testing, P Values, Confidence Intervals, and Significance. [Updated 2023 Mar 13]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK557421/]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "residual risk", "definition": "CDISC Definition: In assessing the risk of re-identifying a trial participant, the risk that remains after controls are taken into account (the net risk or after controls). [Institute of Medicine report, Appendix B]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "response option", "definition": "CDISC Definition: One of several choices to be available for selection in response to a prompt, question or instruction (i.e., a stem) in a PRO item. See also common data element, stem.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "result synopsis", "definition": "CDISC Definition: The brief report prepared by biostatisticians summarizing primary (and secondary) efficacy results and key demographic information.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "results posting (results submission)", "definition": "CDISC Definition: The process of submitting and updating summary information about the results of a clinical study to a structured, publicly accessible, Web-based results database, such as the ClinicalTrials.gov results database. [ClinicalTrials.gov]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "results posting date (results submission date)", "definition": "CDISC Definition: The date and time the summary information about the results of the clinical study are submitted to a structured, publicly accessible, Web-based results database, such as the ClinicalTrials.gov results database. [ClinicalTrials.gov]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "retrospective data capture", "definition": "CDISC Definition: Capture of clinical trial data is retrospective when it is recalled from memory rather than captured contemporaneously in real-time. NOTE: Retrospective capture is important in PROs because of \"recall bias\" and other errors documented in psychological research comparing contemporaneous self-reported assessments and those that rely on recall from memory.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "retrospective study", "definition": "CDISC Definition: A study with planned observations collected predominantly before study start (i.e., backward-looking). NOTE: Examples are case-control studies or retrospective cohort studies when the observations from the selected subjects occurred before study start. [After ClinicalTrials.gov] See also prospective study, observational study, adaptive design, clinical study.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "RHIO (Regional Health Information Organization)", "definition": "CDISC Definition: A group of organizations with a business stake in improving the quality, safety and efficiency of healthcare delivery. RHIOs are the building blocks of the proposed National Health Information Network (NHIN) initiative.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Risk Evaluation and Mitigation Strategy (REMS)", "definition": "CDISC Definition: A drug safety program of the U.S. Food and Drug Administration (FDA), which can be required for certain medications with serious safety concerns to help ensure the benefits of the medication outweigh its risks. NOTE: REMS are designed to reinforce medication use behaviors and actions that support the safe use of that medication. [US FDA, Risk Evaluation and Mitigation Strategies (REMS), https://www.fda.gov/drugs/drug-safety-and-availability/risk-evaluation-and-mitigation-strategies-rems, Accessed 2025-05-22] See also risk-benefit assessment.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "risk", "definition": "CDISC Definition: In clinical trials, the probability of harm or discomfort for subjects. NOTE: Acceptable risk differs depending on the condition for which a product is being tested. A product for sore throat, for example, will be expected to have a low incidence of troubling side effects. However, the possibility of unpleasant side effects may be an acceptable risk when testing a promising treatment for a life-threatening illness.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "risk-based monitoring", "definition": "CDISC Definition: Study monitoring that focuses on preventing or mitigating important and likely risks to investigation quality, including risks to human subject protection and data integrity. [After FDA Guidance: A Risk-Based Approach to Monitoring of Clinical Investigations Questions and Answers, 2019] See also monitoring.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "risk-benefit assessment", "definition": "CDISC Definition: A qualitative and analytic evaluation of the potential harm effects and possible positive effects. NOTE: Assessments can be performed in the context of an intervention and/or the individual or population participating in a clinical trial. [After FDA Guidance on Benefit-Risk Assessment for New Drug and Biological Products, October 2023] See also Risk Evaluation and Mitigation Strategy (REMS).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "risk-benefit ratio", "definition": "CDISC Definition: A quantitative assessment of an activity's relative risks and benefits to the individual. NOTE: The term 'risk' refers to the possibility of experiencing a harm. [After NCI Thesaurus; After Coleman CH. Risk-Benefit Analysis. In: Laurie G, Dove E, Ganguli-Mitra A, et al., eds. The Cambridge Handbook of Health Research Regulation. Cambridge Law Handbooks. Cambridge University Press; 2021:130-138.; US FDA Benefit-Risk Assessment for New Drug and Biological Products, Guidance for Industry, October 2023] See also relative risk.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "role (CDISC classifier)", "definition": "CDISC Definition: Classifier for variables that describe \"observations\" in the SDTM. Role is a metadata attribute that determines the type of information conveyed by an observation-describing variable and standardizes rules for using the describing variable. [SDTM]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "route of administration (ROA)", "definition": "CDISC Definition: The way in which a pharmaceutical product is taken into, or makes contact with, the body. [After ISO 11615:2017, 3.1.76] See also administration (substance), administrable dosage form.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "SAFE", "definition": "CDISC Definition: BioPharma(TM) Digital Identity and Signature Standard.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "safety and tolerability", "definition": "CDISC Definition: The safety of a medical product concerns the medical risk to the subject, usually assessed in a clinical trial by laboratory tests (including clinical chemistry and hematology), vital signs, clinical adverse events (diseases, signs, and symptoms), and other special safety tests (e.g., ECGs, ophthalmology). The tolerability of the medical product represents the degree to which overt adverse effects can be tolerated by the subject. [ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "safety", "definition": "CDISC Definition: Relative freedom from harm. In clinical trials, this refers to an absence of harmful side effects resulting from use of the product and may be assessed by laboratory testing of biological samples, special tests and procedures, psychiatric evaluation, and/or physical examination of subjects.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "sample size adjustment", "definition": "CDISC Definition: An interim check conducted on blinded data to validate the sample size calculations or reevaluate the sample size. [After ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "sample size calculation", "definition": "CDISC Definition: A statistical calculation to determine the number of subjects required for the primary analysis, which should be large enough to provide a reliable answer to the questions addressed and should be determined by the primary objective of the trial. [After ICH E9, 3.5]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "sample size", "definition": "CDISC Definition: A subset of a larger population, selected for investigation to draw statistically valid conclusions or make estimates about the larger population. NOTE: This number is presented in the protocol and statistical analysis plan. [After ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "schedule of activities", "definition": "CDISC Definition: A standardized representation of planned clinical trial activities including interventions (e.g., administering drug, surgery) and study administrative activities (e.g., obtaining informed consent, distributing clinical trial material and diaries, randomization) as well as assessments. See also schedule of assessments. Compare to study design schematic.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "schedule of assessments", "definition": "CDISC Definition: A tabular representation of planned protocol events and activities, in sequence. [after E3 Annexes IIIa and IIIb] Compare to study design schematic.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "screen failure", "definition": "CDISC Definition: At screening, when a potential subject does not meet study eligibility criteria. See also screening (of subjects). [After Segen's Medical Dictionary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "screen/screening (of substances)", "definition": "CDISC Definition: Screening is the process by which substances are evaluated in a battery of tests or assays (screens) designed to detect a specific biological property or activity. It can be conducted on a random basis in which substances are tested without any preselection criteria or on a targeted basis in which information on a substance with known activity and structure is used as a basis for selecting other similar substances on which to run the battery of tests. [SQA]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "screening (of sites)", "definition": "CDISC Definition: Determining the suitability of an investigative site and personnel to participate in a clinical trial.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "screening (of subjects)", "definition": "CDISC Definition: A process of active evaluation for potential participation in a trial, including whether the protocol inclusion and exclusion criteria are met. [After FDA GLOSSARY OF TERMS ON CLINICAL TRIALS FOR PATIENT ENGAGEMENT ADVISORY COMMITTEE MEETING] See also screen failure.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "screening (period)", "definition": "CDISC Definition: A period in a clinical study during which subjects are evaluated for participation in the study. See also screening (of subjects)", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "screening trials", "definition": "CDISC Definition: A type of study designed to assess or examine methods of identifying a condition (or risk factors for a condition) in people who are not yet known to have the condition (or risk factor). (Clinicaltrials.gov)", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "script", "definition": "CDISC Definition: A program or a sequence of instructions that are interpreted or carried out by another program or by a person.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "secondary objective", "definition": "CDISC Definition: The supportive or ancillary scientific question(s) the study is designed to answer. [After ICH E8] See also objective, primary objective, exploratory objective.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "secondary outcome variable", "definition": "CDISC Definition: Data on secondary outcomes are used to evaluate additional effects of the intervention. The primary outcome is the outcome of greatest importance. [after CONSORT statement] See also outcome, endpoint.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "secondary sponsor", "definition": "CDISC Definition: Additional individuals, organizations or other legal persons, if any, that have agreed with the primary sponsor to take on responsibilities of sponsorship. [WHO, CTR item 6]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "self-evident change", "definition": "CDISC Definition: A data discrepancy that can be easily and obviously resolved on the basis of existing information on the CRF (e.g., obvious spelling errors or the patient is known to be a male and a date of last pregnancy is provided). See also discrepancy.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "semantic interoperability", "definition": "CDISC Definition: The ability of data shared by systems to be understood at the level of fully defined domain concepts. [ISO 18308]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "semantic", "definition": "CDISC Definition: In the context of a technical specification, semantic refers to the meaning of an element as distinct from its syntax. syntax can change without affecting semantics. [HL7]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "SEND (standard for the exchange of nonclinical data)", "definition": "CDISC Definition: The CDISC standard for the exchange of nonclinical data whose focus is on data collected from animal toxicology studies. [CDISC]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "sensitive data", "definition": "CDISC Definition: Any piece of information that can be used to identify or cause harm to an individual person. NOTE: Examples include history of alcoholism, drug abuse, risky behavior, or venereal disease. Elements considered sensitive data between US and EU may differ. [After HIPAA; After EU Directive 95/46/EC; After Protection of Personal Data in Clinical Documents - A Model Approach, PHUSE] See also personal data, processing (personal data).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "sensitivity (medical test)", "definition": "CDISC Definition: The proportion of positive tests out of all tests for subjects with a condition (true-positive rate). NOTE: Sensitivity represents the likelihood that a subject with the disease or other condition will have a positives test result. [After Diagnostic Testing Accuracy: Sensitivity, Specificity, Jacob Shreffler; Martin R. Huecker, Predictive Values and Likelihood Ratios, StatPearls Publishing, 2024 Jan; After Understanding Medical Tests and Test Results in Merck Manuals, Brian Mandell at Case Western University]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "serious adverse drug reaction", "definition": "CDISC Definition: Adverse drug reaction that at any dose of the drug: results in death, is life-threatening, requires inpatient hospitalization or prolongation of existing hospitalization, results in persistent or significant disability/ incapacity, or is a congenital anomaly/ birth defect. NOTE: FDA 21 CFR 310.305 defines an adverse drug experience to include any adverse event, \"whether or not considered to be drug-related.\" CDISC recognizes that current usage incorporates the concept of causality. [1. WHO Technical Report 498(1972); 2. After ICH E2A, B] See ICH E6 definition and serious and severe definitions.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "serious adverse event (SAE)", "definition": "CDISC Definition: Adverse event that: results in death, is life-threatening, requires inpatient hospitalization or prolongation of existing hospitalization, results in persistent or significant disability/ incapacity, or is a congenital anomaly/ birth defect. NOTE: For further information, see the ICH Guideline for Clinical safety Data Management: Definitions and standards for expedited Reporting. [After ICH E2A, B] Compare to serious adverse drug reaction.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "serious adverse experience (SAE)", "definition": "CDISC Definition: Any experience that suggests a significant hazard, contra-indication, side effect or precaution. See also serious adverse event.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "serious breach", "definition": "CDISC Definition: A breach of Clinical Trial Regulation (EU) No 536/2014 or of the version of the protocol applicable at the time of the breach, which is likely to affect to a significant degree the safety and rights of a subject or the reliability and robustness of the data generated in the clinical trial. [Article 52 of Regulation (EU) 536/2014 and Guideline for the notification of serious breaches of Regulation (EU) No 536/2014 or the clinical trial protocol] See also privacy breach.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "serious risk", "definition": "CDISC Definition: Risk of a serious adverse drug experience. [505-1(b) of FD&C Act (21 USC. 355-1(b)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "server", "definition": "CDISC Definition: A computer that controls a central repository of data, files, and/ or applications that can be accessed and/or manipulated in some manner by client computers. NOTE: A file server hosts files for use by client machines. A web server supports browser-based use of central applications.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "severe", "definition": "CDISC Definition: A term for grading intensity on a relative scale describing a symptom, outcome, or event that is of a high level of intensity. Note: The term is often used to describe the intensity (severity) of a specific event (as in mild, moderate, or severe myocardial infarction or a Grade 3 adverse event in oncology). 'Severe' is different from 'serious,' which is based on patient/event outcome or action and serves as a guide for defining regulatory reporting obligations. The distinction is important to maintain when translating the concepts. [After ICH E2A, E2B; After CIOMS Cumulative glossary with a focus on pharmacovigilance. Geneva, Switzerland: Council for International Organizations of Medical Sciences (CIOMS), 2023.; After CTCAE] See also serious adverse event and serious adverse drug reaction.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "sex", "definition": "CDISC Definition: Phenotypic expression of chromosomal makeup that defines a study subject as male, female, or other. Compare to gender. [The NCI Thesaurus contains biomedical terminologies that NCI does not own or control. This concept contains gender-related content that does not comply with Executive Order 14168.]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "side effect", "definition": "CDISC Definition: Any action or effect of a drug or treatment other than the intended effect. Negative or adverse effects may include headache, nausea, hair loss, skin irritation, or other physical problems. Experimental drugs must be evaluated for both immediate and long-term side effects. [After Spilker, B. Guide to Clinical Trials. Lippincott Williams & Wilkins. 2000. Page xxiv; Finding and Learning about Side Effects (adverse reactions), July 2018; What are side effects?, August 2018] See also adverse reaction, treatment effect, therapeutic effect.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "sign", "definition": "CDISC Definition: An observation by a medical professional obtained from examination, test result, or questionnaire that indicates a patient may have a disease. NOTE: Some examples of signs are fever, swelling, skin rash, high blood pressure, and high blood glucose. [After NCI Glossary] See also diagnosis, symptom.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "signal of a serious risk", "definition": "CDISC Definition: Information related to a serious adverse drug experience associated with use of a drug and derived from-(a) a clinical trial; (b) adverse event reports; (c) a post-approval study; (d) peer-reviewed biomedical literature; (e) data derived from the post-market REMs. [505-1(b) of FD&C Act (21 USC. 355-1(b)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "single-blind study", "definition": "CDISC Definition: A study in which one party, either the investigator or the subject, does not know which medication or placebo is administered to the subject; also called single-masked study. See also blind study, double-blind study, triple-blind study.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "single-entity product", "definition": "CDISC Definition: A product composed of two or more regulated components (i.e., drug/device, biologic/device, drug/biologic, or drug/device/biologic) that are physically, chemically, or otherwise combined or mixed and produced as a single entity. [After 21 CFR 3.2 (e) FAQ] See also combination product, co-packaged product, cross-labeled product.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "site investigator", "definition": "CDISC Definition: A person responsible for the conduct of the clinical trial at a trial site. If a trial is conducted by a team of individuals at a trial site, the investigator is the responsible leader of the team and may be called the principal investigator. [ICH E6 1.35. 2.] See also investigator, coordinating investigator, investigator/institution, principal investigator, sponsor-investigator, sub-investigator.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "SNOMED (Systematized Nomenclature of Medicine)", "definition": "CDISC Definition: A structured nomenclature and classification of the terminology used in human and veterinary medicine developed by the College of Pathologists and American Veterinary Medical Association. NOTE: Terms are applied to one of eleven independent systematized modules and presented in a multiaxial and hierarchical structure.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "social circumstances", "definition": "CDISC Definition: A set of concepts that results from or is influenced by criteria or activities associated with the social environment of a person. [NCI]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "software as a medical device (SaMD)", "definition": "CDISC Definition: Software intended to be used for the performance of one or more medical purposes, without being part of a hardware medical device. [After \"Software as a Medical Device\": Possible Framework for Risk Categorization and Corresponding Considerations Authoring Group: IMDRF Software as a Medical Device (SaMD) Working Group Date: 18 September 2014]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "software validation", "definition": "CDISC Definition: Confirmation by examination and provision of objective evidence that software specifications conform to user needs and intended uses, and that the particular requirements implemented through software can be consistently fulfilled. NOTE: Validating software thus should include evaluation of the suitability of the specifications to \"ensure user needs and intended uses can be fulfilled on a consistent basis\" (21 CFR 820.20). General Principles of software Validation; Final Guidance for industry and FDA staff, Jan 11, 2002. ISOIEC/IEEE 12207:1995 3.35; 21 CFR 820.20; 21 CFR 11.10(a); ISO 9000-3; Huber, l. (1999) See also validation, verification. Verification usually concerns confirmation that specified requirements have been met, but typically refers to the tracing of requirements and evidence of conformance in the individual phases or modules rather than suitability of the complete product. Validation is, \"the evaluation of software at the end of the software development process to ensure compliance with the user requirements\" (ANSI/ASQC A3-1978) and should not be thought of as an \"end-to-end\" verification. See also validation.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "software verification", "definition": "CDISC Definition: The process that provides objective evidence that the design outputs of a particular phase of the software development life cycle meet all of the specified requirements for that phase. NOTE: Software verification looks for consistency, completeness, and correctness of the software and its supporting documentation, as it is being developed, and provides support for a subsequent conclusion that software is validated [After 1. FDA General Principles of Software Validation; 2. ANSI/ASQC A3-1978; 3. ISO/IEC 17025:2017]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "software", "definition": "CDISC Definition: Computer programs, procedures, rules, and any associated documentation pertaining to the operation of a system.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "source data verification", "definition": "CDISC Definition: The process of ensuring that data that have been derived from source data accurately represent the source data.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "source data", "definition": "CDISC Definition: All information in original records and certified copies of original records of clinical findings, observations, or other activities in a clinical trial necessary for the reconstruction and evaluation of the trial. Source data are contained in source documents (original records or certified copies). [ICH E6; CSUCT]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "source document verification (SDV)", "definition": "CDISC Definition: The process by which the information reported by an investigator is compared with the source records or original records to ensure that it is complete, accurate, and valid. [Schuyl and Engel, 1999; Khosla et al., Indian J. Pharm 32:180-186, 2000] See also data validation.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "source documents", "definition": "CDISC Definition: Original documents, data, and records (e.g., hospital records, clinical and office charts, laboratory notes, memoranda, subjects' diaries or evaluation checklists, pharmacy dispensing records, recorded data from automated instruments, copies or transcriptions certified after verification as being accurate copies, microfiches, photographic negatives, microfilm or magnetic media, x-rays, subject files, and records kept at the pharmacy, at the laboratories, and at medicotechnical departments involved in the clinical trial). See also eSource document, source, original data, certified copy. [ICH; CSUICI]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "source", "definition": "CDISC Definition: The specific permanent record(s) upon which a user will rely for the reconstruction and evaluation of a clinical investigation. NOTE: The term identifies records planned (designated by the protocol) or referenced as the ones that provide the information underlying the analyses and findings of a clinical investigation. Accuracy, suitability, and trustworthiness are not defining attributes of \"source.\" The term is also sometimes used as shorthand for source documents and/or source data. [After ICH E6, CSUICI] See also source document, source data, original data, certified copy.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "special populations", "definition": "CDISC Definition: Subsets of study populations of particular interest included in clinical trials to ensure that their specific characteristics are considered in interpretation of data (e.g., geriatric). [FDA]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "special purpose domain", "definition": "CDISC Definition: In the context of the Study Data Tabulation Model (SDTM), a higher level categorization of the subject-level non-observational domains, which are not classified under the SDTM general observation classes. Examples include trial design domains, relationship domains, etc. [Based on SDTM and SDTM Implementation Guide, www.CDISC.org] See also domain, general observational class.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "specificity (medical test)", "definition": "CDISC Definition: The proportion of negative tests out of all tests for subjects who do not have a disease or condition (true-negative rate). NOTE: Specificity represents the likelihood that a subject without the disease or other condition will have a negative test result. [After Diagnostic Testing Accuracy: Sensitivity, Specificity, Jacob Shreffler; Martin R. Huecker, Predictive Values and Likelihood Ratios, StatPearls Publishing, 2024 Jan; After Understanding Medical Tests and Test Results in Merck Manuals, Brian Mandell at Case Western University]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "specified substance", "definition": "CDISC Definition: Substance defined by groups of elements that describes multi-substance materials or specifies further information on substances relevant to the description of Medicinal Products. NOTE: This could include grade, units of measure, physical form, constituents, manufacturer, critical manufacturing processes (e.g. extraction, synthetic or recombinant processes), specification and the analytical methods used to determine whether a substance is in compliance with a specification. [After ISO 11615:2017, 3.1.77]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "sponsor", "definition": "CDISC Definition: An individual, company, institution, or organization that takes responsibility for the initiation, management, and/or financing of a clinical study. [After ICH E6, WHO, 21 CFR 50.3 (e), and after IDMP] See also secondary sponsor.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "sponsor-investigator", "definition": "CDISC Definition: An individual who both initiates and conducts, alone or with others, a clinical trial and under whose immediate direction the investigational product is administered to, dispensed to, or used by a subject. NOTE: The term does not include any person other than an individual (i.e., it does not include a corporation or an agency). The obligations of a sponsor-investigator include both those of a sponsor and those of an investigator. [21 CFR 50.3f] [ICH E6] See also coordinating investigator, investigator, investigator/institution, principal investigator, site investigator, sponsor-investigator, sub-investigator.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "standard deviation", "definition": "CDISC Definition: Indicator of the relative variability of a variable around its mean; the square root of the variance.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "standard of care", "definition": "CDISC Definition: A guideline for medical management and treatment.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "standard operating procedures (SOPs)", "definition": "CDISC Definition: Detailed, written instructions to achieve uniformity of the performance of a specific function. [ICH]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "standard treatment", "definition": "CDISC Definition: A treatment currently in wide use and approved by FDA or other health authority, considered to be effective in the treatment of a specific disease or condition.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "standard", "definition": "CDISC Definition: A repeatable written norm, pattern, or model that is generally accepted by agreement, established or approved by an authority, or widely accepted and used by custom. [After https://dictionary.cambridge.org/us/dictionary/english/standard, https://www.fda.gov/media/124694/download]. See also data standards, CDISC standards, Study Data Standardization Plan, and Standards Development Organization.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Standards Development Organization (SDO)", "definition": "CDISC Definition: A domestic or international organization that plans, develops, establishes, or coordinates standards by using procedures that incorporate the attributes of openness, balance of interests, due process, an appeals process, and consensus. [After Office of Management and Budget (OMB) Circular A-119]. See also standard, data standards, CDISC standards, and Study Data Standardization Plan.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "statistical analysis plan", "definition": "CDISC Definition: A document that contains a more technical and detailed elaboration of the principal features of the analysis described in the protocol, and includes detailed procedures for executing the statistical analysis of the primary and secondary variables and other data. [ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "statistical distribution", "definition": "CDISC Definition: A group of ordered values; the frequencies or relative frequencies of all possible values of a characteristic. [AMA Manual of Style]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "statistical method", "definition": "CDISC Definition: The particular mathematical tests and techniques that are to be used to evaluate the clinical data in a trial. [After FDA Guidance for Industry, E9 Statistical Principles for Clinical Trials, SEPTEMBER 1998]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "statistical power", "definition": "CDISC Definition: A measure of the likelihood that a significance test will detect an effect or difference in a sample if the effect or difference exists in the full population. NOTE: The power calculation is a function of factors such as sample size, effect size, and significance level. It is dependent upon the assumption that the differences between the compared treatments are unbiased estimates of the same quantity. [After McHugh ML. Power analysis in research. Biochem Med (Zagreb). 2008;18:263-274; After ICH E9] See also statistical significance, sample size, effect size.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "statistical significance", "definition": "CDISC Definition: The likelihood that an event occurs by chance (e.g., the null hypothesis is rejected). NOTE: For example, one may say \"significant at the 5% level\", which is usually represented as \"p <= 0.05\". This implies that there is a 95% probability that the effect did not occur by chance. [After AMA Manual of Style; After Principles of Epidemiology in Public Health Practice, Third Edition, An Introduction to Applied Epidemiology and Biostatistics, Oct 2006, Updated May 2012, US DHHS]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "stem", "definition": "CDISC Definition: The prompt, question, or instruction in a PRO item. See also response option, item.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "stochastic", "definition": "CDISC Definition: Involving a random variable; involving chance or probability.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "stopping rules", "definition": "CDISC Definition: A statistical criterion that, when met by the accumulating data, indicates that the trial can or should be stopped early to avoid putting participants at risk unnecessarily or because the intervention effect is so great that further data collection is unnecessary.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "stratification", "definition": "CDISC Definition: Grouping defined by important prognostic factors measured at baseline. [ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "structured data", "definition": "CDISC Definition: Data that have been organized into discrete fields, and may be enumerated, numeric, or codified.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "structured health record information", "definition": "CDISC Definition: Structured health record information is organized into discrete fields, and may be enumerated, numeric, or codified. Examples of structured health information include: patient address (non-codified, but discrete field); diastolic blood pressure (numeric); coded result observation; coded diagnosis; patient risk assessment questionnaire with multiple-choice answers. Context may determine whether or not data are unstructured, e.g., a progress note might be standardized and structured in some eHR-s (e.g., subjective/objective/ assessment/Plan) but unstructured in others. [HL7 eHR-s FM Glossary of Terms, 2010].", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "structured product label (SPL)", "definition": "CDISC Definition: The structured product labeling (SPL) specification is an HL7 ANSI-approved document markup standard that specifies the structure and semantics for the exchange of product information. [HL7]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study arm", "definition": "CDISC Definition: A planned pathway through the study to which subjects are assigned, and that describes treatments, exposures, controls, and/or observations. [After BRIDG] See also control, control group.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study compensation", "definition": "CDISC Definition: Money or other forms of payment for participating in a study. NOTE: Compensation can either be offered in monetary forms, i.e., as payment, or non-monetary forms provided as goods or services. [https://health.ec.europa.eu/system/files/2023-06/payment_compensation_template_en.pdf; Payment and Reimbursement to Research Subjects, FDA, 2018] See also study reimbursement.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study completion date", "definition": "CDISC Definition: The date on which the final data for a clinical study were collected because the last study participant made the final visit to the study location (that is, last subject, last visit, or as otherwise defined in the study protocol). NOTE: See also study completion date data element on ClinicalTrials.gov.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study completion", "definition": "CDISC Definition: As defined in the protocol, the point at which all protocol-required activities have been executed. NOTE: According to EU CTR, this should be a clear and unambiguous definition of the end of the clinical trial in question and, if it is not the date of the last visit of the last subject, a specification of the estimated end date and a justification thereof should be included. [REGULATION (EU) No 536/2014 Article 2.26]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Study Data Standardization Plan (SDSP)", "definition": "CDISC Definition: A document that describes the data standardization strategy for clinical and nonclinical studies within a development program. NOTE: A Study Data Standardization Plan is intended to include historical, current, and planned information about the use of study data standards for studies to conform with the current technical formats, and terminologies described in the FDA Data Standards Catalog which applies to CDER, CBER, and CDRH. [After http://www.phusewiki.org/wiki/images/e/ea/SDSP_Template.pdf, https://www.fda.gov/industry/fda-resources-data-standards/study-data-standards-resources, https://www.fda.gov/media/102719/download] See also standards, data standards, CDISC standards, and Standards Development Organization.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study description", "definition": "CDISC Definition: Representation of key elements of study (e.g., control, blinding, dose, indication, configuration).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study design rationale", "definition": "CDISC Definition: Reason(s) for choosing the study design. NOTE: Reasons may include the choice of control, comparator or population, as well as the scientific or statistical rationale.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study design schematic", "definition": "CDISC Definition: A diagram that outlines the decision points (e.g. randomization, response evaluation) that define the different paths a participant could take through the study. This is typically a block diagram and may include epochs, timing of randomization, treatment arms, and duration of treatments. [CDISC Terminology; After ICH E3]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study design", "definition": "CDISC Definition: A strategy that specifies the structure of a study in terms of the planned activities (including timing) and statistical analysis approach intended to meet the objectives of the study. NOTE: Additional elements may include choice of control group(s), method of allocating treatments, blinding methods, and minimization of bias. [After Pocock, Clinical Trials: a Practical approach; After ICH E8; After ICH E9] See also Trial Design Model, arm, epoch, visit, parallel trial, crossover trial.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study feasibility", "definition": "CDISC Definition: The likelihood that a study will be completed as designed.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study life cycle", "definition": "CDISC Definition: The flow of events that characterize a research study from start to finish. [NCI]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study monitoring", "definition": "CDISC Definition: The act of overseeing the progress of a clinical study to ensure that it is conducted (and that events are recorded and reported) in accordance with the protocol, standard operating procedures (SOPs), good clinical practice (GCP), and the applicable regulatory requirement(s). [After ICH E6 Glossary] See also monitoring, subject monitoring, medical monitoring, study monitoring, data monitoring, risk based monitoring.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study participant", "definition": "CDISC Definition: A member of the clinical study population from whom data are being collected. NOTE: This new term is used with growing frequency in some clinical documents and patient-facing ones like the informed consent form, Plain Language Summaries of study results, and publications. Subject or patient are terms used in regulatory guidelines, databases, other clinical research documents, or systems to refer to study participants. See also human subject, patient, vulnerable subjects, data subject, clinical research subject, participant.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study population", "definition": "CDISC Definition: A group of individuals taken from the general population who share a set of common characteristics, such as age, sex, or health condition, precisely defined in the study protocol. This is a population to which the study results could be reasonably generalized. (CDISC Protocol Entities)", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study publication date", "definition": "CDISC Definition: The date of the publication of scientific articles or abstracts about a clinical study. NOTE: Institute of Medicine (IOM) Report: The committee noted support for open and free access to scientific publications immediately upon publication, as well as the requirement of the U.S. Food and Drug Administration (FDA) to make a summary of clinical trial results available to the public. [ClinicalTrials.gov]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study reimbursement", "definition": "CDISC Definition: Money paid to the study subject/participant to offset personal expenses incurred during study participation, as agreed to by the sponsor and participant. See also study compensation.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study report completion date", "definition": "CDISC Definition: The date at which the study report is considered final and will not be subject to any further change prior to submission. NOTE: For interventional studies of adults the study report completion date should be one year from the end of the LPLV, or end of study; for pediatric interventional studies this date should be six months. For non-interventional studies the study report completion date should be one year from the end of the LPLV, end of study, or end of data collection. [EU CTR]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study results", "definition": "CDISC Definition: The findings from a research study to include data, statistical analyses, and clinical interpretation. [After ICH E3] See also clinical study report, outcome, result synopsis, outcome of study.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study start date", "definition": "CDISC Definition: The date on which the protocol-defined study start criteria are met. NOTE: The US FDA defines the study start date for clinical studies as the earliest date of informed consent among any subject that enrolled in the study. [US FDA, Providing Regulatory Submissions In Electronic Format - Standardized Study Data Guidance for Industry, June 2021] See study start. [US FDA, Providing Regulatory Submissions In Electronic Format - Standardized Study Data Guidance for Industry, June 2021]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study start", "definition": "CDISC Definition: The criteria for study start, as defined in the protocol, are met.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "study variable", "definition": "CDISC Definition: A term used in trial design to denote a variable to be captured on the CRF. See also variable.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "sub-investigator", "definition": "CDISC Definition: Any member of the clinical trial team designated and supervised by the investigator at a trial site to perform critical trial-related procedures and/or to make important trial-related decisions (e.g., associates, residents, research fellows). [After ICH E6] See also investigator, coordinating investigator, investigator/institution, principal investigator, site investigator, sponsor-investigator.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "subject completion", "definition": "CDISC Definition: The case where a subject ceases active participation in a trial because the subject has, or is presumed to have followed all appropriate conditions of a protocol.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "subject data event", "definition": "CDISC Definition: A subject visit or other encounter where subject data are collected, generated, or reviewed. [SDTM]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "subject identification code", "definition": "CDISC Definition: A unique identifier assigned by the investigator to each trial subject to protect the subject's identity and used in lieu of the subject's name when the investigator reports adverse events and/or other trial-related data. [ICH]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "subject monitoring", "definition": "CDISC Definition: Act of tracking, reporting, and review of a clinical trial subject's status and/ or performance of required activities per protocol. NOTE: Examples include monitoring compliance with treatment and scheduled tasks, tracking measures of symptoms, self reported feelings, and/or behaviors. Subject monitoring supports managing of patient safety and well being by site staff as defined in a protocol. Compare with medical device, medical monitoring.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "subject trial contact", "definition": "CDISC Definition: Any activity, anticipated in the study protocol, involving a subject and pertaining to collection of data. See visit.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "subject-reported outcome (SRO)", "definition": "CDISC Definition: An outcome reported directly by a subject in a clinical trial. [Patrick, D.l., 2003] See also patient-reported outcome (PRO).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "submission model", "definition": "CDISC Definition: A set of data standards (including SDTM, ADaM, and define.xml) for representing data that are submitted to regulatory authorities to support product marketing applications. NOTE: CDISC submission data consist of: tabulations that represent the essential data collected about patients; analysis data structured to support analysis and interpretation; and metadata descriptions.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "subprocessor", "definition": "CDISC Definition: An authorized third-party to carry out processing activities on a data processor's behalf. [Article 4 GDPR Definitions] See also personal data, processing (personal data), data processor.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "superiority trial", "definition": "CDISC Definition: A trial with the primary objective of showing that the response to the investigational product is superior to a comparative agent (active or placebo control). [ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "supplier (system)", "definition": "CDISC Definition: An organization that enters into a contract with the acquirer for the supply of a system (such as a software product, or software service) under the terms of a contract. [ISO/IEC/IEEE 12207:1995 3.30]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "surrogate endpoint", "definition": "CDISC Definition: An endpoint that is used in clinical trials as a substitute for a direct measure of how a patient feels, functions, or survives. A surrogate endpoint does not measure the clinical benefit of primary interest in and of itself, but rather is expected to predict that clinical benefit or harm based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence. [NIH-FDA BEST (Biomarkers, Endpoints, and other Tools) Resource]. See also endpoint.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "surrogate marker", "definition": "CDISC Definition: A measurement of a drug's biological activity that substitutes for a clinically meaningful endpoint. [After Russell Katz, Biomarkers and Surrogate Markers: An FDA Perspective, NeuroRx. 2004 Apr;1(2):189-95.]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "surrogate variable", "definition": "CDISC Definition: A variable that provides an indirect measurement of effect in situations where direct measurement of clinical effect is not feasible or practical. [ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "survey", "definition": "CDISC Definition: Any means (e.g., questionnaire, diary, interview script, group of items) that is used to collect PRO data. NOTE: survey refers to the content of the group of items and does not necessarily include the training and scoring documents generally not seen by respondents. [from ISOQOL comments on PRO Guidance] Compare to instrument.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "suspension (of a clinical trial)", "definition": "CDISC Definition: An interruption of the conduct of a clinical trial by a Member State of the EU. NOTE: Similar to FDA \"clinical hold\". [After EU CTR] See also clinical hold (of a clinical trial), termination (of a clinical trial), temporary halt (of a clinical trial).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "symptom", "definition": "CDISC Definition: A physical or mental experience or observation reported by a patient that may indicate a disease. NOTE: Some examples of symptoms are pain, fatigue, nausea, and anxiety. [After NCI Glossary] See also diagnosis, sign.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "synergistic effect", "definition": "CDISC Definition: An interaction between bioactive compounds or drugs that is deemed greater than the sum of each individual component. NOTE: The terms additivity, synergism, and antagonism should be used with care, unless the specific pharmacological pathways or mechanisms of action of the investigated drugs are known. [After Calzetta L, Koziol-White C. Pharmacological interactions: Synergism, or not synergism, that is the question. Curr Res Pharmacol Drug Discov. 2021 Aug 11;2:100046.] See also synergistic effect, antagonistic effect, drug interaction.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "synopsis", "definition": "CDISC Definition: Brief overview prepared at the conclusion of a study as a routine part of a regulatory submission, summarizing the study plan and results; includes numerical summary of efficacy and safety results, study objective, criteria for inclusion, methodology, etc. [after ICH E3]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "syntactic", "definition": "CDISC Definition: The order, format, content of clinical trial data and/or documents as distinct from their meaning. NOTE: Syntactic interoperability is achieved when information is correctly exchanged between two systems according to structured rules whether or not sensible meaning is preserved. See also semantic, semantic interoperability.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "synthetic data", "definition": "CDISC Definition: Data that are artificially created rather than being generated by actual events. NOTE: Data are often created with the help of algorithms and used for a wide range of activities, including as test data for new products and tools, for model validation, and in AI optimization. [After The Ultimate Guide to Synthetic Data in 2020, August 29, 2020]. See also artificial intelligence.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "system", "definition": "CDISC Definition: People, machines, software, applications, and/or methods organized to accomplish a set of specific functions or objectives. [ANSI]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "t-test", "definition": "CDISC Definition: A statistical test used to compare the means of two groups of test data.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "table of roles and responsibilities", "definition": "CDISC Definition: A cumulative record documenting operational access and authorizations of study personnel to electronic systems used in eClinical trials.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "tabulation dataset", "definition": "CDISC Definition: A dataset structured in a tabular format. NOTE: The CDISC Study Data Tabulation Model (SDTM) defines standards for tabulation datasets that fulfill FDA requirements for submitting clinical trial data.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "target enrollment", "definition": "CDISC Definition: The planned number of subjects intended to be enrolled within a study to reach a pre-specified sample size (in any cohort or the entire study). NOTE: Target enrollments are set so that statistical and scientific objectives of a trial will have a likelihood of being met as determined by agreement, algorithm, or other specified process. [After clinicaltrials.gov] See also accrual, target population.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "target population", "definition": "CDISC Definition: The group of people in the general population to which the study results can be generalized.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "technology provider", "definition": "CDISC Definition: A person, company, or other entity who develops, produces, and sells software applications and/or hardware for use in conducting clinical trials and/or in analyzing clinical trial data and or submitting clinical trial information for regulatory approval.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "temporary halt (of a clinical trial)", "definition": "CDISC Definition: An interruption not provided in the protocol of the conduct of a clinical trial by the sponsor with the intention of the sponsor to resume it. [After EU CTR] See also termination (of a clinical trial), clinical hold (of a clinical trial), suspension (of a clinical trial).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "term", "definition": "CDISC Definition: One or more words designating something. NOTE: In a controlled vocabulary, terms are considered to refer to an underlying concept having a single meaning. Concepts may be linked to several synonymous terms.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "termination (of a clinical trial)", "definition": "CDISC Definition: Discontinuation of a trial prior to plan as defined in the protocol. NOTE: Additional information can be found in Division of AIDS (DAIDS) Site Clinical Operations and Research Essentials (SCORE) Manual: Premature Termination or Suspension of a Clinical Trial, 19 January 2021. See also discontinuation, suspension (of a clinical trial), clinical hold (of a clinical trial), temporary halt (of a clinical trial).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "terminology", "definition": "CDISC Definition: Set of concepts, designations, and relationships for a specialized subject area. NOTE: In the context of clinical research in human subjects, a standardized, finite set of terms (e.g., CDISC Terminology, MedDRA codes) that denote patient findings, circumstances, events, and interventions. See also glossary, vocabulary. Contrast with nomenclature.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "therapeutic area", "definition": "CDISC Definition: A category for a disease, disorder, or other condition based on common characteristics and often associated with a medical specialty focusing on research and development of specific therapeutic interventions for the purpose of treatment and prevention. (After NCI)", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "therapeutic effect", "definition": "CDISC Definition: The intended beneficial effect of an intervention on the body or disease. NOTE: Contrast with side effect, which is an unintended effect. [After Zhang P, Wang F, Hu J, Sorrentino R. Exploring the relationship between drug side-effects and therapeutic indications. AMIA Annu Symp Proc. 2013 Nov 16;2013:1568-77.] See also treatment effect, side effect.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "therapeutic index", "definition": "CDISC Definition: The ratio of the dose that produces toxicity (denominator) to the dose that produces a clinically desired or effective response (numerator). NOTE: The therapeutic index is a measure of a drug's safety, because a larger value indicates a wide margin between doses that are effective and doses that are toxic. [After Finkel, R, Clark, M. A., Champe, P. C. & Cubeddu, L. X. (eds) Lippincott's Illustrated Reviews: Pharmacology 4th edn (Lippincott Williams & Wilkins, 2008).]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "token", "definition": "CDISC Definition: Physical key that provides access to a secure electronic system or location.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "traceability (data)", "definition": "CDISC Definition: The ability to track data from source data collection through final use in reporting or analysis to ensure data interoperability, integrity, and interpretability. See also data integrity.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "transcription", "definition": "CDISC Definition: Process of transforming dictated or otherwise documented information from one storage medium to another. NOTE: often refers explicitly to data that is manually transcribed from source docs or measuring devices to CRFs or to eCRFs.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "transition rule", "definition": "CDISC Definition: A guide that governs the allocation of subjects to operational options at a discrete decision point or branch (e.g., assignment to a particular arm, discontinuation) within a clinical trial plan. See branch.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "translation", "definition": "CDISC Definition: Converting information from one natural language to another while preserving meaning. Compare to mapping.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "translational research", "definition": "CDISC Definition: The multidirectional integration of basic research, patient-oriented research, and population-based research, with the long-term aim of improving the health of the public. NOTE: These studies are designed to translate basic science findings into clinically useful tools and applications and to ensure that new treatments and research knowledge reach the patients or populations for whom they are intended and are implemented correctly. [After Rubio DM, Schoenbaum EE, Lee LS, Schteingart DE, Marantz PR, Anderson KE, Platt LD, Baez A, Esposito K. Defining translational research: implications for training. Acad Med. 2010 Mar;85(3):470-5. and NCI Thesaurus]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "transmit", "definition": "CDISC Definition: To transfer data, usually electronically. NOTE: In eClinical investigations data are commonly transmitted from subjects to clinical study sites, within or among clinical study sites, contract research organizations, data management centers, and sponsors, or to regulatory authorities. [modified from CSUICI].", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "treatment benefit", "definition": "CDISC Definition: The impact of treatment as measured by survival or a COA of how patients feel or function. Direct evidence of treatment benefit is derived from clinical trial effectiveness endpoints that measure survival or a meaningful aspect of how a patient feels or functions in daily life. NOTE: Treatment benefit can be demonstrated by an advantage in either effectiveness or safety, or both. [After FDA Clinical Outcome Assessment (COA) Glossary] See also benefit summary, clinical benefit.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "treatment contrast interaction", "definition": "CDISC Definition: The situation in which a treatment contrast (e.g., difference between investigational product and control) is dependent on another factor (e.g., center). A quantitative interaction refers to the case where the magnitude of the contrast differs at the different levels of the factor, whereas for a qualitative interaction, the direction of the contrast differs for at least one level of the factor. [ICH E9 Glossary]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "treatment effect", "definition": "CDISC Definition: Any intended or unintended effect of the intervention on the body or disease. NOTE: In most clinical trials, the treatment effect of interest is a comparison (or contrast) of two or more treatments. [After ICH E9] See also therapeutic effect, side effect, treatment contrast interaction.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "treatment", "definition": "CDISC Definition: Medical care given to a patient to mitigate or cure an illness, injury, or reduced health status. NOTE: May include prescribed drugs, biologics, surgery, devices, and physical or psychotherapies, but not diagnostics or prophylaxis. See also intervention, diagnosis, adjuvant therapy, neoadjuvant therapy, palliative therapy.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "treatment-emergent adverse event", "definition": "CDISC Definition: An event that emerges during treatment, having been absent pretreatment, or worsens relative to the pretreatment state. [ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "trial design element", "definition": "CDISC Definition: A basic building block for time within a clinical trial comprising the following characteristics: a description of what happens to the subject during the element; a definition of the start of the element; a rule for ending the element.[CDISC PRM Project] See also epoch.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Trial Design Model", "definition": "CDISC Definition: Defines a standard structure for representing the planned sequence of events and the treatment plan of a trial. NOTE: A component of the SDTM that builds upon elements, arms epochs, visits; suitable also for syntactic interpretation by machines. [CDISC] See study design.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "trial site", "definition": "CDISC Definition: A physical location (e.g., healthcare organization, institution, or facility) directly involved in conducting or facilitating a particular clinical trial. NOTE: There may not be a physical location, see decentralized cluster. [After ICH E6 (R2)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "trial statistician", "definition": "CDISC Definition: A statistician who has a combination of education/ training and experience sufficient to implement the principles in the ICH E9 guidance and who is responsible for the statistical aspects of the trial. [ICH E9]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "triple-blind study", "definition": "CDISC Definition: A study in which knowledge of the treatment assignment(s) is concealed from the people who organize and analyze the data of a study as well as from subjects and investigators.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "trustworthy (electronic records)", "definition": "CDISC Definition: An attribute of records (data and documents) and signatures submitted to regulatory agencies referring to their suitability for making scientific findings of safety and efficacy that underlie public policy decisions pertaining to market authorization. Two key dimensions that determine the trustworthiness of eClinical trial data are data quality and data integrity. [after 21CFR Part 11]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "type 1 (or type I) error", "definition": "CDISC Definition: Error made when a null hypothesis is rejected but is actually true.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "type 2 (or type II) error", "definition": "CDISC Definition: Error made when an alternative hypothesis is rejected when it is actually true.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "type 3 (or type III) error", "definition": "CDISC Definition: Some statisticians use this designation for an error made when calling the less effective treatment the more effective treatment.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "type of comparison", "definition": "CDISC Definition: How treatment arms will be compared (e.g., safety, efficacy, PK/PD). May also include comparison to data from other studies or sources (e.g., historical control). [ICH E9, EudraCT (p.18)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "umbrella trial design", "definition": "CDISC Definition: A type of trial design under a master protocol designed to evaluate multiple investigational drugs administered as single drugs or as drug combinations in a single disease population. [After US FDA, Master Protocols: Efficient Clinical Trial Design Strategies to Expedite Development of Oncology Drugs and Biologics Guidance for Industry, 2022]. See also master protocol.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "umbrella trial", "definition": "CDISC Definition: A type of trial conducted under a master protocol and designed to test multiple investigational drugs administered as single drugs or as drug combinations in a single disease population. [After US FDA, Master Protocols: Efficient Clinical Trial Design Strategies to Expedite Development of Oncology Drugs and Biologics Guidance for Industry, 2022]. See also master protocol, adaptive design, umbrella trial design.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "unblinding", "definition": "CDISC Definition: Identification of the treatment assignment to the subject, investigators, and/or other trial personnel. [After EUPATI Toolbox: Within-trial decisions: Unblinding and termination. 2023]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "unexpected adverse drug reaction", "definition": "CDISC Definition: An adverse drug reaction, whose nature, severity, specificity, or outcome is not consistent with the term or description used in the applicable product information (e.g., IB for an unapproved investigational product or PI/summary of product characteristics for an approved product, and/or scientific literature). [After ICH E6 (R2)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "unexpected serious risk", "definition": "CDISC Definition: A serious adverse drug experience that is not listed in the labeling of a drug, or that may be symptomatically or pathophysiologically related to an adverse drug experience identified in the labeling, but differs because of greater severity, specificity, or prevalence. [505-1(b) of FD&C Act (21 USC. 355-1(b)]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "uniform resource locator (URL)", "definition": "CDISC Definition: Address of a web page, for example, appliedclinicaltrialsonline.com.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "use case", "definition": "CDISC Definition: An explicit scenario designed to help in determining whether a system/process is capable of performing the functions required for a particular use. a use case might describe, for example, how a study coordinator would use a tablet computer to capture medical history data.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "use error (device)", "definition": "CDISC Definition: User action or lack of action that was different from that expected by the manufacturer and caused a result that (1) was different from the result expected by the user and (2) was not caused solely by device failure and (3) did or could result in harm. [FDA, Applying Human Factors and Usability Engineering to Medical Devices]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "user site testing (UST)", "definition": "CDISC Definition: Any testing that takes place outside of the developer's controlled environment. NOTE: Terms such as beta test, site validation, user acceptance test, installation verification, and installation testing have all been used to describe user site testing. User site testing encompasses all of these and any other testing that takes place outside of the developer's controlled environment. [from General Principles of software Validation; Final Guidance, section 5.2.6]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "vaccine effectiveness", "definition": "CDISC Definition: Vaccine protection measured in observational studies that include people with underlying medical conditions who have been administered vaccines by different health care providers under real-world conditions. [How Flu Vaccine Effectiveness and Efficacy are Measured, Questions & Answers, CDC January 29, 2016] See also vaccine efficacy, efficacy, effectiveness, randomized controlled trial (RCT).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "vaccine efficacy", "definition": "CDISC Definition: The proportional comparison of infection rate or other disease endpoints between vaccinated and unvaccinated groups measured in randomized controlled trials. NOTE: The method for calculating vaccine efficacy can be found here: https://www.cdc.gov/csels/dsepd/ss1978/lesson3/section6.html. Efficacy is a measurement made during a clinical trial, effectiveness is how well the vaccine works out in the real world. [After Greenwood et al., Proc R Soc Med. 1915; 8 (Sect Epidemiol State Med): 113-194, The Statistics of Anti-typhoid and Anti-cholera Inoculations, and the Interpretation of such Statistics in general. After Piero Ollario, The Lancet Infectious Diseases, Feb 17th, 2021] See also vaccine effectiveness, effectiveness, efficacy, randomized controlled trial (RCT).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "vaccine", "definition": "CDISC Definition: A medicinal product inducing immunity against disease, most often to prevent occurrence of a disease, (e.g., a preventative vaccine against infectious disease), but also to treat a disease, (e.g., a therapeutic vaccine against cancer). NOTE: The vaccines against infectious disease may contain various ingredients of diverse origin (such as inactivated or attenuated organisms, particular antigens related to the infectious agent, live recombinant vector against antigens in vivo and adjuvants) [After NCI Dictionary of Cancer Terms. After European Pharmacopoeia section 5.1.] See also treatment, prevention, prophylaxis, biological product, virulence.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "valid", "definition": "CDISC Definition: Well grounded on principles of evidence. [After FDA Glossary of Computerized System and Software Development Terminology]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "validation", "definition": "CDISC Definition: Process of establishing suitability to purpose. NOTE: Validation is accomplished by planning how to measure and/or evaluate suitability to purpose; then executing the plan and documenting the results. [ICH E6] See also software validation, data validation, psychometric validation, criterion validation (COA), content validation (COA), construct validation (COA).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "variable", "definition": "CDISC Definition: Any attribute, phenomenon, characteristic, or event that can have different qualitative or quantitative values. [After Statistical Language - What are Variables?, Australian Bureau of Statistics, October 2013] See also dependent variable, derived variable, global assessment variable, primary outcome variable, qualitative variable, quantitative variable, secondary outcome variable, study variable, supporting variables, surrogate variable.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "variance", "definition": "CDISC Definition: A measure of the variability in a sample or population. It is calculated as the mean squared deviation (MSD) of the individual values from their common mean. In calculating the MSD, the divisor n is commonly used for a population variance and the divisor n-1 for a sample variance.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "verification of data", "definition": "CDISC Definition: The checking of data for correctness or compliance with applicable standards, rules, and conventions. [FDA Glossary of Computerized system and software Development Terminology] See also source document verification (SDV).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "verification", "definition": "CDISC Definition: The act of reviewing, inspecting, testing, checking, auditing, or otherwise establishing and documenting whether items, processes, services, or documents conform to specified requirements. Compare to validation where suitability to purpose is also established.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "virtual", "definition": "CDISC Definition: Connected but not physically co-located. NOTE: Refers to visits or encounters between investigators and subjects where information exchange is mediated through telemedicine, video conference rather than by physical presence of individuals at a shared location. Trials with one or more virtual visits are virtual trials. Where all data capture and trial procedures are conducted virtually, a trial or other investigation may be called fully virtual. [After FDA Guidance on Conduct of Clinical Trials of Medical Products during COVID-19 Public Health Emergency Guidance for Industry, Investigators, and Institutional Review Boards March 2020 Updated on July 2, 2020] See also remote clinical trial, decentralized clinical trial.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "virulence", "definition": "CDISC Definition: The ability of an infectious agent to cause severe disease, measured as the proportion of persons with the disease who become severely ill or die. [Principles of Epidemiology in Public Health Practice, Third Edition. An Introduction to Applied Epidemiology and Biostatistics, Glossary, CDC 2014] See also morbidity, vaccine.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "visit", "definition": "CDISC Definition: A protocol-defined clinical encounter that encompasses planned and contingent study interventions, procedures, and assessments that may be performed on a subject. [SDTM]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "vocabulary", "definition": "CDISC Definition: The collection of terms, which refer to concepts, that are used by, understood by, or available for use by an individual or group within a language system. [After NCI Thesaurus]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "vulnerable subjects", "definition": "CDISC Definition: Individuals whose willingness to volunteer in a clinical trial may be unduly influenced by the expectation, whether justified or not, of benefits associated with participation, or of a retaliatory response from senior members of a hierarchy in case of refusal to participate. NOTE: Examples are members of a group with a hierarchical structure, such as medical, pharmacy, dental, and nursing students, subordinate hospital and laboratory personnel, employees of the pharmaceutical industry, members of the armed forces, and persons kept in detention. Other vulnerable subjects include patients with incurable diseases, persons in nursing homes, unemployed or impoverished persons, patients in emergency situations, ethnic minority groups, homeless persons, nomads, refugees, minors, and those incapable of giving consent. [After ICH E6 R2 Glossary] See also human subject, patient, human subject, data subject, clinical research subject, participant, study participant.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "Warning Letter", "definition": "CDISC Definition: A written communication from FDA notifying an individual or firm that the agency considers one or more products, practices, processes, or other activities to be in violation of the Federal FD&C Act, or other acts, and that failure of the responsible party to take appropriate and prompt action to correct and prevent any future repeat of the violation may result in administrative and/or regulatory enforcement action without further notice. [FDA]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "washout period", "definition": "CDISC Definition: The interval of time that a participant enrolled in a study must not receive a specified treatment(s) before starting a study intervention(s). NOTE: A washout may be required before joining a study or before changing treatments within a study. [After https://metastatictrialtalk.org/inside-clinical-trials/washout-period/]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "web browser", "definition": "CDISC Definition: A computer program that interprets HTML and other Internet languages and protocols and displays web pages on a computer monitor.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "web page", "definition": "CDISC Definition: A single page on a website, such as a home page.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "web scraping", "definition": "CDISC Definition: The automated process of programmatically and systematically collecting information on the web and processing it into more easily analyzable formats that can be serialized. [After NNLM, \"Web Scraping\", 05/25/22, https://www.nnlm.gov/guides/data-glossary/web-scraping] See also AI prompt, Generative AI (GenAI).", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "web server", "definition": "CDISC Definition: A computer server that delivers HTML pages or files over the World Wide Web. See also server.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "website", "definition": "CDISC Definition: A collection of web pages and other files. A site can consist of a single web page, thousands of pages, or custom created pages that draw on a database associated with the site.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "weighting", "definition": "CDISC Definition: An adjustment in a value based on scientific observations within the data.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "well-being (of the trial subjects)", "definition": "CDISC Definition: The physical and mental integrity of the subjects participating in a clinical trial. [ICH]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "withdrawal", "definition": "CDISC Definition: The subject-initiated act of discontinuing participation in a clinical study. NOTE: Withdrawal can range from the subject's complete withdrawal from study procedures and follow-up activities, to the subject's withdrawal from study-related interventions. [After Guidance on Withdrawal of Subjects from Research: Data Retention and Other Related Issues, September 21, 2010] See also discontinuation.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "within-subject differences", "definition": "CDISC Definition: In a crossover trial, variability in each subject is used to assess treatment differences.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "World Wide Web", "definition": "CDISC Definition: All the resources and users on the Internet that are using HTTP protocols. Also called the web and www.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "XML (eXtensible Markup Language)", "definition": "CDISC Definition: A set of rules for encoding documents and data in a format that is both human readable and machine readable. [After Study Data Technical Conformance Guide, Technical Specifications Document, March 2019; After W3C Extensible Markup Language (XML)] See also eXtensible markup language (XML) data element, Define-XML.", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "zoonosis", "definition": "CDISC Definition: An infectious disease that is transmissible from animals to humans. [Principles of Epidemiology in Public Health Practice, Third Edition. An Introduction to Applied Epidemiology and Biostatistics, Glossary, CDC 2014]", "sources": [ "CDISC Glossary.xlsx" ], "file": "CDISC Glossary.xlsx", "sheet": "Glossary Terminology 2025-09-26", "type": "excel" }, { "term": "The Multi", "definition": "Regional Clinical Trials Center of Brigham and Women's Hospital and Harvard", "sources": [ "https://mrctcenter.org/glossaryterm/clinical-research/" ], "file": "https://mrctcenter.org/glossaryterm/clinical-research/", "type": "web" }, { "term": "Post-Trial Responsibilities", "definition": "Continued Access", "sources": [ "https://mrctcenter.org/glossaryterm/clinical-research/" ], "file": "https://mrctcenter.org/glossaryterm/clinical-research/", "type": "web" }, { "term": "Proactive Safety Surveillance", "definition": "A Global Approach", "sources": [ "https://mrctcenter.org/glossaryterm/clinical-research/" ], "file": "https://mrctcenter.org/glossaryterm/clinical-research/", "type": "web" }, { "term": "Project", "definition": "Specific Websites", "sources": [ "https://mrctcenter.org/glossaryterm/clinical-research/" ], "file": "https://mrctcenter.org/glossaryterm/clinical-research/", "type": "web" }, { "term": "Health and Human Services", "definition": "What is Medical Research?", "sources": [ "https://mrctcenter.org/glossaryterm/clinical-research/" ], "file": "https://mrctcenter.org/glossaryterm/clinical-research/", "type": "web" }, { "term": "FDA", "definition": "The Drug Development Process Step 3: Clinical Research", "sources": [ "https://mrctcenter.org/glossaryterm/clinical-research/" ], "file": "https://mrctcenter.org/glossaryterm/clinical-research/", "type": "web" }, { "term": "The Future is Connected", "definition": "Standards and AI Powering Digital Transformation", "sources": [ "https://www.cdisc.org/" ], "file": "https://www.cdisc.org/", "type": "web" }, { "term": "Access CDISC end-to", "definition": "end Foundational and Therapeutic Area standards", "sources": [ "https://www.cdisc.org/" ], "file": "https://www.cdisc.org/", "type": "web" }, { "term": "Therapeutic areas", "definition": "latest updates", "sources": [ "https://www.ema.europa.eu/" ], "file": "https://www.ema.europa.eu/", "type": "web" }, { "term": "Post", "definition": "authorisation", "sources": [ "https://www.ema.europa.eu/" ], "file": "https://www.ema.europa.eu/", "type": "web" }, { "term": "Podcast", "definition": "Inside EMA", "sources": [ "https://www.ema.europa.eu/" ], "file": "https://www.ema.europa.eu/", "type": "web" }, { "term": "HealthNotHype", "definition": "EMA's first social media campaign with creators", "sources": [ "https://www.ema.europa.eu/" ], "file": "https://www.ema.europa.eu/", "type": "web" }, { "term": "First-in", "definition": "class treatment to delay onset of type 1 diabetes", "sources": [ "https://www.ema.europa.eu/" ], "file": "https://www.ema.europa.eu/", "type": "web" }, { "term": "Wiskott", "definition": "Aldrich syndrome", "sources": [ "https://www.ema.europa.eu/" ], "file": "https://www.ema.europa.eu/", "type": "web" }, { "term": "Enpr", "definition": "EMA) November 2025", "sources": [ "https://www.ema.europa.eu/" ], "file": "https://www.ema.europa.eu/", "type": "web" }, { "term": "Start date", "definition": "20 November 2025, 13:00 (CET)", "sources": [ "https://www.ema.europa.eu/" ], "file": "https://www.ema.europa.eu/", "type": "web" }, { "term": "End date", "definition": "21 November 2025, 12:50 (CET)", "sources": [ "https://www.ema.europa.eu/" ], "file": "https://www.ema.europa.eu/", "type": "web" }, { "term": "EMA multi", "definition": "stakeholder workshop on artificial intelligence (AI)", "sources": [ "https://www.ema.europa.eu/" ], "file": "https://www.ema.europa.eu/", "type": "web" }, { "term": "European Medicines Agency", "definition": "Medtech Europe bilateral meeting", "sources": [ "https://www.ema.europa.eu/" ], "file": "https://www.ema.europa.eu/", "type": "web" }, { "term": "A Custom Webtools Rating widget", "definition": "Hidden by field_ema_hide_rating for this node.", "sources": [ "https://www.ema.europa.eu/" ], "file": "https://www.ema.europa.eu/", "type": "web" }, { "term": "South", "definition": "East Asia", "sources": [ "https://www.who.int/" ], "file": "https://www.who.int/", "type": "web" }, { "term": "COVID", "definition": "19 dashboard", "sources": [ "https://www.who.int/" ], "file": "https://www.who.int/", "type": "web" }, { "term": "Lifetime toll", "definition": "840 million women faced partner or sexual violence", "sources": [ "https://www.who.int/" ], "file": "https://www.who.int/", "type": "web" }, { "term": "Read WHO Director", "definition": "General's speeches", "sources": [ "https://www.who.int/" ], "file": "https://www.who.int/", "type": "web" }, { "term": "November 2025 12", "definition": "00 – 13:00 CET", "sources": [ "https://www.who.int/" ], "file": "https://www.who.int/", "type": "web" }, { "term": "WHO EPI-WIN Webinar", "definition": "attributes of pathogen genomic data platforms supporting timely and equitable sharing", "sources": [ "https://www.who.int/" ], "file": "https://www.who.int/", "type": "web" }, { "term": "November 2025 13", "definition": "30 – 15:00 CET", "sources": [ "https://www.who.int/" ], "file": "https://www.who.int/", "type": "web" }, { "term": "From Data to Impact", "definition": "Advancing Healthcare Associated Infection Surveillance for Safer Care and a Healthier Future", "sources": [ "https://www.who.int/" ], "file": "https://www.who.int/", "type": "web" }, { "term": "Beyond HLM4", "definition": "Implementing the 2025 Political Declaration on NCDs and Mental Health", "sources": [ "https://www.who.int/" ], "file": "https://www.who.int/", "type": "web" }, { "term": "November 2025 18", "definition": "30 – 20:00 CET", "sources": [ "https://www.who.int/" ], "file": "https://www.who.int/", "type": "web" }, { "term": "Study record managers", "definition": "refer to the", "sources": [ "https://clinicaltrials.gov/" ], "file": "https://clinicaltrials.gov/", "type": "web" } ]