xpertsystems/hlt003-sample

HLT-003 — Synthetic Clinical Trial Dataset (Sample Preview)

A free, schema-identical preview of the full HLT-003 commercial product from XpertSystems.ai.

A fully synthetic parametric clinical trial simulator demonstrating all three primary endpoint types (CONTINUOUS, BINARY, TTE) plus a sample-size power sweep. Built on JSON-configurable trial designs with ICH E9 / CONSORT 2010 / CTCAE v5.0 conventions for randomization, blinding, dropout modeling, and adverse event reporting.

⚠️ 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.

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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.

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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

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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

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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.

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Loading examples

Pandas — explore the 3 trials

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

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)

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)

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

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

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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

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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.

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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.

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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

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Companion datasets in the Healthcare vertical

• HLT-001 — Synthetic Patient Population (5K patients × 79 cols, CDC/NHANES calibrated)
• HLT-002 — 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.

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Citation

If you use this dataset, please cite:

@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}
}

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Contact

• Web: https://xpertsystems.ai
• Email: 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.