--- license: cc-by-nc-4.0 task_categories: - tabular-classification - tabular-regression - time-series-forecasting language: - en tags: - synthetic - healthcare - clinical-trials - rct - randomized-controlled-trial - phase-iii - phase-ii - biostatistics - pharma - drug-development - power-analysis - sample-size - treatment-effect - adverse-events - ctcae - ich-e9 - consort - cohens-d - hazard-ratio - odds-ratio - progression-free-survival - arps - itt-population - pp-population - dropout-modeling - mar-mnar - biomarker-interaction - simulation pretty_name: HLT-003 Synthetic Clinical Trial Dataset — 3 Endpoint Types + Power Sweep (Sample Preview) size_categories: - 1K ⚠️ **PRIVACY & SYNTHETIC NATURE** > Every record in this dataset is **100% synthetic**. **No real trial participant data, no PHI, no re-identifiable records.** Trial parameters (effect sizes, response rates, dropout rates, AE profiles) match published clinical trial design conventions but the participants are computationally generated. --- ## What's in this sample ### Three sample trials — one per endpoint type | File | Trial | Phase | Endpoint | Arms | N | Size | |---|---|---|---|---|---|---| | `cardiology_ldl_trial.csv` | LDL-C reduction (lipid-lowering therapy) | III | CONTINUOUS | 3 | 800 | 117 KB | | `rheumatology_acr20_trial.csv` | ACR20 response (rheumatoid arthritis biologic) | III | BINARY | 3 | 800 | 109 KB | | `oncology_pfs_trial.csv` | Progression-free survival (targeted oncology) | II | TTE | 2 | 600 | 80 KB | ### Configuration templates | File | Description | |---|---| | `cardiology_ldl_config.json` | Full JSON config for the cardiology trial | | `rheumatology_acr20_config.json` | Full JSON config for the rheumatology trial | | `oncology_pfs_config.json` | Full JSON config for the oncology trial | | `trial_config_schema.json` | Complete config API documentation | ### Power analysis | File | Description | |---|---| | `power_sweep_results.csv` | Power curve at 7 sample sizes (200-2500) × 50 simulations per n, small effect (d=0.10) | ### Documentation | File | Description | |---|---| | `simulation_audit_log.txt` | Reproducibility audit log from latest trial run | | `validation_scorecard.json` | Wrapper-authored 10-metric scorecard with sources | **Total:** ~348 KB across 10 files. --- ## Schema (all 3 trial CSVs share the same 28-column schema) ### Trial & participant identity `participant_id`, `trial_id`, `therapeutic_area`, `trial_phase`, `arm_id`, `arm_label`, `treatment_effect_d` ### Demographics `age`, `sex`, `bmi`, `disease_severity_score`, `severity_tertile` (Low/Moderate/High), `biomarker_positive`, `elderly_flag` ### Analysis populations (ICH E9) `ITT_flag` (Intent-to-Treat), `PP_flag` (Per-Protocol), `dropout_flag` ### Endpoint fields (only the trial's endpoint type is populated; others are NaN) **CONTINUOUS:** `baseline_ep`, `primary_ep_w24`, `primary_cfb_w24` **BINARY:** `binary_response_w24` **TTE:** `tte_event_week`, `tte_event_flag`, `tte_censored_flag` ### Safety (CTCAE v5.0) `ae_total_count`, `ae_grade3plus_count`, `ae_sae_count`, `ae_any_flag` --- ## Coverage **3 endpoint types** — CONTINUOUS (mean change from baseline), BINARY (response rate with odds ratios), TTE (time-to-event with hazard ratios) **Therapeutic areas demonstrated** — Cardiology, Rheumatology, Oncology (full product supports 12 areas including Neurology, Metabolic, Respiratory, Psychiatry, Infectious Disease, Rare Disease, Ophthalmology, Dermatology, GI) **4 trial phases supported** — I, II, III, IV **3 blinding modes** — OPEN, SINGLE, DOUBLE **4 randomization schemes** — SIMPLE, BLOCK, STRATIFIED, MINIMIZATION **3 dropout mechanisms** — MCAR (Missing Completely At Random), MAR (Missing At Random, logit model with severity/age/arm coefficients), MNAR (Missing Not At Random fraction) **5 AE severity grades** — CTCAE v5.0 Grade 1 (mild) through Grade 5 (death) **Biomarker × treatment interaction modeling** — predictive biomarker positive fraction with arm-specific effect modification **Subgroup effects** — elderly (≥65) AE multipliers, severity tertile stratification --- ## Calibration source story The full HLT-003 generator is grounded in industry-standard clinical trial methodology: - **ICH E9 (1998)** — Statistical Principles for Clinical Trials (ITT/PP analysis populations, primary endpoint analysis conventions) - **CONSORT 2010** — Reporting standards for randomized trials - **Cohen (1988)** — Statistical Power Analysis (effect size conventions) - **ICH E6(R2)** — Good Clinical Practice (randomization, blinding standards) - **CTCAE v5.0 (NCI)** — Common Terminology Criteria for Adverse Events - **Felson et al. (1995)** — ACR Response Criteria (rheumatoid arthritis) - **Cox (1972)** — Proportional hazards model (TTE analysis) - **Kaplan-Meier (1958)** — Survival analysis conventions - **Cholesterol Treatment Trialists' Collaboration** — LDL-C reduction baselines for statin trials ### Sample-scale validation scorecard | Metric | Observed | Target | Tolerance | Status | Source | |---|---|---|---|---|---| | CONTINUOUS placebo CFB calibration | -6.78 | -8.0 | ±2.0 | ✅ PASS | CTT Collaboration | | CONTINUOUS treatment effect separation | 16.94 | 16.0 | ±4.0 | ✅ PASS | Cohen (1988) | | BINARY placebo response rate | 21.1% | 22% | ±6% | ✅ PASS | Felson et al. (1995) | | BINARY high-dose response rate | 58.3% | 53% | ±10% | ✅ PASS | Felson et al. (1995) | | TTE event rate separation | 6.3% | 10% | ±8% | ✅ PASS | Cox (1972) | | Overall dropout rate | 8.9% | 10% | ±6% | ✅ PASS | ICH E9 | | AE grade distribution validity | 100% | 100% | ±5% | ✅ PASS | CTCAE v5.0 | | ITT population completeness | 100% | 100% | — | ✅ PASS | ICH E9 | | Randomization balance | 99.7% | ≥95% | ±5% | ✅ PASS | ICH E6(R2) GCP | | Endpoint field type compliance | 100% | 100% | — | ✅ PASS | Data hygiene invariant | **Grade: A+ (100/100) — verified across 6 random seeds (42, 7, 123, 2024, 99, 1).** ### Power curve (small effect d=0.10, 2-arm continuous) ``` n=200 → power = 0.28 n=400 → power = 0.58 n=600 → power = 0.70 n=800 → power = 0.74 n=1200 → power = 0.92 ← crosses 80% threshold n=1800 → power = 1.00 n=2500 → power = 1.00 ``` Classic S-shaped power curve — the kind of output biostatisticians use for trial sample-size justification. --- ## Loading examples ### Pandas — explore the 3 trials ```python import pandas as pd cont = pd.read_csv("cardiology_ldl_trial.csv") binr = pd.read_csv("rheumatology_acr20_trial.csv") tte = pd.read_csv("oncology_pfs_trial.csv") print("CONTINUOUS trial — mean change from baseline by arm:") print(cont.groupby("arm_label")["primary_cfb_w24"].agg(["mean", "std", "count"])) print("\nBINARY trial — response rates by arm:") print(binr.groupby("arm_label")["binary_response_w24"].mean()) print("\nTTE trial — event vs censored by arm:") print(tte.groupby("arm_label")[["tte_event_flag", "tte_censored_flag"]].sum()) ``` ### Hugging Face Datasets ```python from datasets import load_dataset ds = load_dataset("xpertsystems/hlt003-sample", data_files={ "continuous": "cardiology_ldl_trial.csv", "binary": "rheumatology_acr20_trial.csv", "tte": "oncology_pfs_trial.csv", "power": "power_sweep_results.csv", }) print(ds) ``` ### Reproducing a primary analysis (CONTINUOUS endpoint) ```python import pandas as pd from scipy import stats df = pd.read_csv("cardiology_ldl_trial.csv") itt = df[df["ITT_flag"] == 1] arm_placebo = itt[itt["arm_id"] == 0]["primary_cfb_w24"].dropna() arm_high = itt[itt["arm_id"] == 2]["primary_cfb_w24"].dropna() t, p = stats.ttest_ind(arm_placebo, arm_high) print(f"Placebo CFB mean: {arm_placebo.mean():.2f} mg/dL (n={len(arm_placebo)})") print(f"100mg CFB mean: {arm_high.mean():.2f} mg/dL (n={len(arm_high)})") print(f"Treatment effect: {arm_high.mean() - arm_placebo.mean():.2f} mg/dL") print(f"t={t:.2f}, p={p:.4g}") ``` ### Reproducing a Kaplan-Meier curve (TTE endpoint) ```python import pandas as pd import matplotlib.pyplot as plt # Optional: pip install lifelines for proper KM curves from lifelines import KaplanMeierFitter df = pd.read_csv("oncology_pfs_trial.csv") fig, ax = plt.subplots(figsize=(8, 5)) for arm_id, arm_df in df.groupby("arm_id"): kmf = KaplanMeierFitter() kmf.fit(arm_df["tte_event_week"], event_observed=arm_df["tte_event_flag"], label=arm_df["arm_label"].iloc[0]) kmf.plot_survival_function(ax=ax) ax.set_xlabel("Weeks"); ax.set_ylabel("Progression-Free Survival") plt.show() ``` ### Using a JSON config to design your own trial ```python import json # Load one of the included configs as a starting point with open("cardiology_ldl_config.json") as f: config = json.load(f) # Modify for your trial design config["trial_id"] = "MY-TRIAL-001" config["n_participants"] = 1500 config["arms"][1]["treatment_effect_cohens_d"] = 0.55 # smaller effect config["dropout_mcar_rate"] = 0.08 # higher base dropout with open("my_custom_trial.json", "w") as f: json.dump(config, f, indent=2) # Then run the full HLT-003 generator: # python hlt003_simulation_engine.py --config my_custom_trial.json ``` --- ## Suggested use cases - **Power analysis & sample-size calculation** — reproduce the included power sweep with custom effect sizes, validate analytical power formulas (e.g. Cohen's `pwr` package) against Monte Carlo simulation - **Statistical test selection** — practice choosing t-test / Wilcoxon / chi-square / Fisher / log-rank / Cox on the appropriate endpoint type - **Missing data methodology** — develop and test MAR/MNAR imputation strategies on the dropout-flagged data - **ITT vs PP comparison** — quantify the bias from per-protocol analysis vs intent-to-treat - **Biomarker subgroup analysis** — test prespecified subgroup hypotheses on `biomarker_positive` - **Adverse event signal detection** — train classifiers on `ae_grade3plus_count` and `ae_sae_count` against arm/severity features - **Survival analysis** — fit Kaplan-Meier, Cox proportional hazards, accelerated failure time models on the TTE trial - **Trial design optimization** — compare 2-arm vs 3-arm designs, dose-ranging vs head-to-head, fixed vs adaptive randomization - **Educational use** — biostatistics coursework demonstrating endpoint types, randomization, dropout, AE reporting - **Pharma data pipeline testing** — validate ETL pipelines, SDTM/ADaM converters, e-CRF systems against schema-compliant synthetic data --- ## Sample vs. full product | Aspect | This sample | Full HLT-003 product | |---|---|---| | Trials demonstrated | 3 (one per endpoint type) | Unlimited via JSON config | | Patients per trial | 600-800 | Up to 50,000 | | Power sweep | 7 n values × 50 sims | 9+ n values × 200 sims + matplotlib chart | | Therapeutic areas | 3 (Cardio, Rheum, Onc) | All 12 (incl. Neuro, Metabolic, Resp, Psych, ID, Rare, Ophth, Derm, GI) | | Phases | II, III | I, II, III, IV | | Schema | identical | identical | | Config API | identical | identical | | License | CC-BY-NC-4.0 | Commercial license | The full product unlocks unlimited custom trial configurations, larger participant counts for adaptive-design simulations, full power-sweep chart output, and commercial use. **Contact us for the full product.** --- ## Limitations & honest disclosures - **Sample is preview-only.** 3 trials × 600-800 patients is enough to demonstrate the JSON config API, endpoint types, and validation scorecard, but is **not statistically sufficient** for serious drug development simulation work. The full product supports up to 50K patients per trial. - **`treatment_effect_cohens_d` is a generator parameter, not the textbook post-trial Cohen's d.** The generator uses it as a multiplier in its CFB formula (`active_bonus = -|placebo_cfb| × d × 2.5`); the actual post-trial Cohen's d for the comparison depends on the residual SD. For example, the cardiology config's d=0.75 produces an actual post-trial Cohen's d of ~1.5 (Δ=15 vs σ_residual=10.5). Use the power sweep (which simulates the actual statistical test) for true power calculations. - **Power sweep uses only 50 simulations per n.** Production-grade power analyses use 1,000-10,000 sims per n. The included sweep is a demonstration; the full product runs 200+ sims by default with optional escalation. - **No matplotlib chart in the sample.** The full product generates a PNG power curve chart with 80%/90% threshold lines and the minimum-n-for-80%-power annotation. Excluded from this preview for HF size hygiene. - **Single-visit endpoint** (no repeated measures). Each participant has one CFB value at "week 24" — no per-visit longitudinal endpoint trajectories. The full product can be extended with visit-level repeated measures. - **Synthetic, not derived from real trial data.** Effect sizes, dropout patterns, and AE profiles follow published clinical trial conventions but do NOT reflect any specific real trial. Use synthetic data for design, methodology development, and ETL testing; validate final designs against published trial literature and prior real trials. - **No FDA SDTM/ADaM CDISC compliance in the sample CSVs.** The wide-format CSV is a flat preview format, not CDISC-conformant DM/AE/LB/QS domain tables. CDISC mapping is available in the full product (contact for SDTM/ADaM ETL). - **Endpoint-type field hygiene is enforced**: only the trial's endpoint type populates its fields; other endpoint fields are NaN. This is by design (data hygiene invariant) and is validated as one of the 10 scorecard metrics. - **No multi-region pooling complexity.** The sample treats sites as independent; the full product supports multi-region trial design with region-specific stratification. --- ## Ethical use guidance This dataset is designed for: - Biostatistics method development and educational use - Trial design simulation and power analysis - ETL pipeline testing against schema-compliant synthetic data - Pharma data engineering and validation This dataset is **not appropriate for**: - Submission as evidence for regulatory filings (use real trial data) - Drug efficacy claims of any kind - Predicting outcomes for any specific real intervention - Training models that produce clinical recommendations --- ## Companion datasets in the Healthcare vertical - [HLT-001](https://huggingface.co/datasets/xpertsystems/hlt001-sample) — Synthetic Patient Population (5K patients × 79 cols, CDC/NHANES calibrated) - [HLT-002](https://huggingface.co/datasets/xpertsystems/hlt002-sample) — Synthetic EHR Dataset (4K encounters + FHIR R4 bundles, HL7/ICD-10/RxNorm/LOINC) - **HLT-003** — Synthetic Clinical Trial Dataset (you are here) Use **HLT-001 + HLT-002 + HLT-003 together** for full population-to-encounter-to-trial healthcare ML workflows. --- ## Citation If you use this dataset, please cite: ```bibtex @dataset{xpertsystems_hlt003_sample_2026, author = {XpertSystems.ai}, title = {HLT-003 Synthetic Clinical Trial Dataset (Sample Preview)}, year = 2026, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/xpertsystems/hlt003-sample} } ``` --- ## Contact - **Web:** [https://xpertsystems.ai](https://xpertsystems.ai) - **Email:** [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai) - **Full product catalog:** Cybersecurity, Insurance & Risk, Materials & Energy, Oil & Gas, Healthcare, and more **Sample License:** CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0) **Full product License:** Commercial — please contact for pricing.