Generate realistic clinical trial synthetic data including study definitions, sites, subjects, visits, adverse events, efficacy assessments, and disposition. Use when user requests: clinical trial data, CDISC/SDTM/ADaM datasets, trial cohorts (Phase I/II/III/IV), FDA submission test data, or specific therapeutic areas like oncology or biologics/CGT.
Scanned 2/12/2026
Install via CLI
Skills Directoryopenskills install mark64oswald/healthsim-workspace---
name: healthsim-trialsim
description: "Generate realistic clinical trial synthetic data including study definitions, sites, subjects, visits, adverse events, efficacy assessments, and disposition. Use when user requests: clinical trial data, CDISC/SDTM/ADaM datasets, trial cohorts (Phase I/II/III/IV), FDA submission test data, or specific therapeutic areas like oncology or biologics/CGT."
---
# TrialSim
**Status**: Active Development
TrialSim generates realistic synthetic clinical trial data for testing, training, and development purposes.
## For Claude
Use this skill when the user requests clinical trial data, CDISC-compliant datasets, or regulatory submission test data. This is the primary skill for generating realistic synthetic clinical trial data.
**When to apply this skill:**
- User mentions clinical trials, studies, or protocols
- User requests CDISC, SDTM, or ADaM datasets
- User specifies trial phases (Phase I, II, III, IV)
- User mentions FDA/EMA submission data or regulatory requirements
- User asks for adverse events, safety data, or efficacy endpoints
- User mentions specific therapeutic areas (oncology, cardiovascular, CNS)
- User requests SDTM domains (DM, AE, VS, LB, CM, EX, DS, MH)
**Key capabilities:**
- Generate complete study definitions with protocol parameters
- Create multi-site, multi-country trial configurations
- Produce subject-level longitudinal data with realistic patterns
- Generate safety data (adverse events, labs, vitals) with MedDRA/LOINC coding
- Create efficacy endpoints for various therapeutic areas
- Output CDISC-compliant formats (SDTM, ADaM)
For specific trial phases, therapeutic areas, or SDTM domains, load the appropriate skill from the tables below.
## Overview
TrialSim provides:
- Complete study lifecycle data (protocol to closeout)
- Multi-site, multi-country trial configurations
- Subject-level longitudinal data with realistic patterns
- Safety data (adverse events, labs, vitals)
- Efficacy endpoints (primary, secondary, exploratory)
- CDISC-compliant output (SDTM, ADaM)
## Trigger Phrases
Activate TrialSim when user mentions:
- "clinical trial" or "clinical study"
- "Phase I/II/III/IV" or "pivotal trial"
- "CDISC", "SDTM", "ADaM"
- "FDA submission data" or "regulatory data"
- "adverse events" or "safety data"
- "efficacy endpoints"
- Trial therapeutic areas (oncology, cardiology, etc.)
- SDTM domains (DM, AE, VS, LB, CM, EX, DS)
## Quick Links
### Core Skills
| Topic | Skill | Description |
|-------|-------|-------------|
| Domain Knowledge | [clinical-trials-domain.md](clinical-trials-domain.md) | Core trial concepts, phases, regulatory |
| Recruitment | [recruitment-enrollment.md](recruitment-enrollment.md) | Screening funnel, enrollment patterns |
### Trial Phase Skills
| Phase | Skill | Description |
|-------|-------|-------------|
| Phase 1 | [phase1-dose-escalation.md](phase1-dose-escalation.md) | FIH, dose escalation, MTD (3+3, BOIN, CRM) |
| Phase 2 | [phase2-proof-of-concept.md](phase2-proof-of-concept.md) | POC, dose-ranging, futility (Simon's, MCP-Mod) |
| Phase 3 | [phase3-pivotal.md](phase3-pivotal.md) | Pivotal registration trials, NDA/BLA |
### SDTM Domain Skills
| Domain | Skill | Description |
|--------|-------|-------------|
| DM | [domains/demographics-dm.md](domains/demographics-dm.md) | Subject demographics, treatment arms |
| AE | [domains/adverse-events-ae.md](domains/adverse-events-ae.md) | Adverse events with MedDRA coding |
| VS | [domains/vital-signs-vs.md](domains/vital-signs-vs.md) | Vital sign measurements |
| LB | [domains/laboratory-lb.md](domains/laboratory-lb.md) | Laboratory results with LOINC |
| CM | [domains/concomitant-meds-cm.md](domains/concomitant-meds-cm.md) | Concomitant medications with ATC |
| EX | [domains/exposure-ex.md](domains/exposure-ex.md) | Study drug exposure, dose modifications |
| DS | [domains/disposition-ds.md](domains/disposition-ds.md) | Subject disposition, discontinuation |
| MH | [domains/medical-history-mh.md](domains/medical-history-mh.md) | Medical history, comorbidities |
| Domain Index | [domains/README.md](domains/README.md) | All SDTM domains overview |
### Therapeutic Areas
| Area | Skill | Key Endpoints |
|------|-------|---------------|
| Oncology | [therapeutic-areas/oncology.md](therapeutic-areas/oncology.md) | RECIST, ORR, PFS, OS |
| Cardiovascular | [therapeutic-areas/cardiovascular.md](therapeutic-areas/cardiovascular.md) | MACE, CV outcomes |
| CNS | [therapeutic-areas/cns.md](therapeutic-areas/cns.md) | Cognitive scales, imaging |
| CGT | [therapeutic-areas/cgt.md](therapeutic-areas/cgt.md) | CAR-T, gene therapy |
### Real World Evidence
| Topic | Skill | Description |
|-------|-------|-------------|
| RWE Overview | [rwe/overview.md](rwe/overview.md) | RWE concepts, data sources |
| Synthetic Controls | [rwe/synthetic-control.md](rwe/synthetic-control.md) | External control arm generation |
## Output Formats
| Format | Skill | Use Case |
|--------|-------|----------|
| SDTM | [../../formats/cdisc-sdtm.md](../../formats/cdisc-sdtm.md) | Regulatory submission |
| ADaM | [../../formats/cdisc-adam.md](../../formats/cdisc-adam.md) | Statistical analysis |
| Dimensional | [../../formats/dimensional-analytics.md](../../formats/dimensional-analytics.md) | BI dashboards, analytics |
| JSON | Default | API integration |
| CSV | [../../formats/csv.md](../../formats/csv.md) | Spreadsheet analysis |
## Data Models & References
| Resource | Location | Description |
|----------|----------|-------------|
| Canonical Models | [../../references/data-models.md#trialsim-models](../../references/data-models.md#trialsim-models) | 15 entity schemas (Subject, Study, Site, AE, etc.) |
| Dimensional Schema | [../../formats/dimensional-analytics.md#trialsim-clinical-trial-analytics](../../formats/dimensional-analytics.md#trialsim-clinical-trial-analytics) | Star schema for BI (7 dims, 6 facts) |
| Code Systems | [../../references/code-systems.md](../../references/code-systems.md) | MedDRA, LOINC, ATC |
## Core Entities
TrialSim uses 15 canonical entity schemas. See [Data Models Reference](../../references/data-models.md#trialsim-models) for complete JSON schemas.
### Entity Overview
| Entity | SDTM Domain | Description |
|--------|-------------|-------------|
| Subject | DM | Trial participant (extends Person) |
| Study | TS | Protocol definition |
| Site | - | Investigational site |
| TreatmentArm | TA | Study arm definition |
| VisitSchedule | TV | Protocol visits |
| ActualVisit | SV | Subject visit occurrence |
| Randomization | DM/SE | Subject randomization |
| AdverseEvent | AE | Safety events with MedDRA |
| Exposure | EX | Study drug dosing |
| ConcomitantMed | CM | Prior/concomitant meds with ATC |
| TrialLab | LB | Lab results with LOINC |
| EfficacyAssessment | RS/TR | Response assessments |
| MedicalHistory | MH | Pre-existing conditions |
| DispositionEvent | DS | Subject disposition |
| ProtocolDeviation | DV | Protocol deviations |
### Key Entity Examples
**Study:**
```json
{
"study_id": "ABC-123-001",
"protocol_title": "A Phase 3, Randomized, Double-Blind Study...",
"phase": "Phase 3",
"therapeutic_area": "Oncology",
"indication": "Non-Small Cell Lung Cancer",
"sponsor": "Example Pharma Inc.",
"status": "Ongoing"
}
```
**Subject (with cross-product linking):**
```json
{
"subject_id": "0001",
"usubjid": "ABC-123-001-001-0001",
"site_id": "001",
"patient_ref": "MRN-12345",
"screening_date": "2024-01-15",
"randomization_date": "2024-01-22",
"treatment_arm": "TRT",
"status": "Active"
}
```
## Integration with Other Products
TrialSim integrates with other HealthSim products for complete clinical trial data:
| From | To | Integration Pattern |
|------|-----|---------------------|
| PatientSim | TrialSim | Patient → Subject (add consent, randomization, protocol visits) |
| NetworkSim | TrialSim | Provider → Investigator (add credentials, training, delegation log) |
| PopulationSim | TrialSim | Demographics → Recruitment pool (geographic, demographic eligibility) |
### Cross-Product: PatientSim
Trial subjects are patients with additional trial-specific data:
- [../patientsim/oncology/](../patientsim/oncology/) - Oncology trial subjects
- [../patientsim/heart-failure.md](../patientsim/heart-failure.md) - CV outcomes trial subjects
- [../patientsim/behavioral-health.md](../patientsim/behavioral-health.md) - CNS trial subjects
- [../patientsim/diabetes-management.md](../patientsim/diabetes-management.md) - Metabolic trial subjects
> **Integration Pattern:** Use PatientSim for baseline clinical characteristics. TrialSim adds protocol-specific assessments (RECIST, NYHA class changes), randomization, and SDTM-formatted data.
### Cross-Product: PopulationSim Integration
PopulationSim v2.0 provides **embedded real-world data** for evidence-based trial planning, site selection, and diversity compliance. When geographies are specified, TrialSim uses actual CDC PLACES, SVI, and ADI data to ground feasibility estimates and enrollment projections.
#### Data-Driven Trial Planning Pattern
**Step 1: Look up real population data for potential sites**
```
# For site feasibility in Houston metro (Harris County, FIPS: 48201)
Read from: skills/populationsim/data/county/places_county_2024.csv
→ DIABETES_CrudePrev: 12.1% (for diabetes trial)
→ CHD_CrudePrev: 6.4% (for CV outcomes trial)
→ CANCER_CrudePrev: 6.2% (for oncology trial)
→ TotalPopulation: 4,731,145
Read from: skills/populationsim/data/county/svi_county_2022.csv
→ RPL_THEMES: 0.68 (moderate-high vulnerability)
→ EP_MINRTY: 72.1% (supports diversity requirements)
```
**Step 2: Apply to site feasibility estimation**
```json
{
"site_feasibility": {
"county_fips": "48201",
"county_name": "Harris County, TX",
"indication": "Type 2 Diabetes",
"eligible_population": {
"total_population": 4731145,
"disease_prevalence": 0.121,
"prevalent_patients": 572467,
"age_eligible_18_75": 458974,
"funnel_to_screenable": 0.05,
"annual_screenable": 22949
},
"diversity_metrics": {
"minority_percentage": 0.721,
"meets_fda_diversity_guidance": true
},
"data_provenance": {
"source": "CDC_PLACES_2024",
"data_year": 2022
}
}
}
```
**Step 3: Generate realistic enrollment projections**
- Site catchment based on real prevalence (not national averages)
- Diversity enrollment reflecting actual demographics
- Screening-to-randomization rates adjusted for SVI (access barriers)
#### Embedded Data Sources for Trial Planning
| Source | File | Use in TrialSim |
|--------|------|-----------------|
| CDC PLACES County | `populationsim/data/county/places_county_2024.csv` | Disease prevalence for feasibility |
| CDC PLACES Tract | `populationsim/data/tract/places_tract_2024.csv` | Catchment area analysis |
| SVI County | `populationsim/data/county/svi_county_2022.csv` | Diversity planning, access barriers |
| SVI Tract | `populationsim/data/tract/svi_tract_2022.csv` | Site-level vulnerability context |
| Geography Crosswalk | `populationsim/data/crosswalks/cbsa_definitions.csv` | Metro area site clustering |
#### Trial-Specific Applications
| Application | Data Used | TrialSim Integration |
|-------------|-----------|---------------------|
| **Site Feasibility** | PLACES disease prevalence + population | Eligible patient pool sizing |
| **Diversity Planning** | SVI EP_MINRTY, demographics | FDA diversity guidance compliance |
| **Enrollment Projection** | PLACES + SVI access indicators | Screening/randomization rates |
| **Site Selection** | Multi-county PLACES comparison | Optimal site network design |
| **Catchment Analysis** | Tract-level PLACES | Drive-time eligible population |
#### Example: Data-Grounded Phase III Site Selection
**Request:** "Identify top 5 US counties for a Phase III NASH trial based on patient availability"
**Data Lookup Process:**
```
Query places_county_2024.csv for:
- High OBESITY_CrudePrev (NASH proxy)
- High DIABETES_CrudePrev (comorbidity)
- Large TotalPopulation (volume)
Query svi_county_2022.csv for:
- EP_MINRTY (diversity potential)
- EP_UNINSUR (access consideration)
```
**Output with Provenance:**
```json
{
"recommended_sites": [
{
"rank": 1,
"county_fips": "48201",
"name": "Harris County, TX",
"obesity_prevalence": 0.328,
"diabetes_prevalence": 0.121,
"population": 4731145,
"minority_pct": 0.721,
"estimated_eligible": 45000,
"diversity_score": "excellent"
}
],
"data_provenance": {
"sources": ["CDC_PLACES_2024", "CDC_SVI_2022"],
"methodology": "prevalence_weighted_ranking"
}
}
```
#### Integration with Trial-Support Skills
| PopulationSim Skill | TrialSim Application | Data Source |
|---------------------|---------------------|-------------|
| [data-lookup.md](../populationsim/data-access/data-lookup.md) | Exact prevalence for feasibility | CDC PLACES 2024 |
| [county-profile.md](../populationsim/geographic/county-profile.md) | Site catchment demographics | PLACES + SVI |
| [svi-analysis.md](../populationsim/sdoh/svi-analysis.md) | Diversity and access analysis | CDC SVI 2022 |
| [feasibility-estimation.md](../populationsim/trial-support/feasibility-estimation.md) | Protocol feasibility funnel | All sources |
| [diversity-planning.md](../populationsim/trial-support/diversity-planning.md) | FDA diversity compliance | SVI demographics |
> **Key Principle:** When planning trials, always ground feasibility and diversity estimates in real PopulationSim data. This enables evidence-based site selection and realistic enrollment projections.
## Development Status
| Component | Status |
|-----------|--------|
| SKILL.md (this file) | ✅ Complete |
| clinical-trials-domain.md | ✅ Complete |
| recruitment-enrollment.md | ✅ Complete |
| phase3-pivotal.md | ✅ Complete |
| domains/ (DM, AE, VS, LB, CM, EX, DS, MH) | ✅ Complete |
| therapeutic-areas/ | ✅ Complete |
| rwe/ | ✅ Complete |
| phase1-dose-escalation.md | ✅ Complete |
| phase2-proof-of-concept.md | ✅ Complete |
## Related Skills
- [PatientSim](../patientsim/SKILL.md) - Clinical patient data
- [MemberSim](../membersim/SKILL.md) - Claims integration
- [Code Systems](../../references/code-systems.md) - Standard terminologies
## Output Formats
TrialSim supports multiple output formats:
| Format | Use Case | Skill Reference |
|--------|----------|-----------------|
| **Canonical JSON** | Internal processing, API integration | [data-models.md](../../references/data-models.md#trialsim-models) |
| **CDISC SDTM** | Regulatory submission, FDA/EMA | [cdisc-sdtm.md](../../formats/cdisc-sdtm.md) |
| **CDISC ADaM** | Analysis datasets, statistical programming | [cdisc-adam.md](../../formats/cdisc-adam.md) |
| **Dimensional (Star Schema)** | Analytics, BI dashboards, DuckDB/Databricks | [dimensional-analytics.md](../../formats/dimensional-analytics.md#trialsim-clinical-trial-analytics) |
### Dimensional Analytics
For trial operations analytics and BI dashboards, request dimensional output:
```
Generate Phase III trial with 100 subjects as star schema for DuckDB
```
This produces:
- **Dimensions**: dim_study, dim_site, dim_subject, dim_treatment_arm, dim_visit_schedule, dim_meddra, dim_lab_test
- **Facts**: fact_enrollment, fact_visit, fact_adverse_event, fact_exposure, fact_efficacy, fact_lab_result
See [dimensional-analytics.md](../../formats/dimensional-analytics.md#trialsim-clinical-trial-analytics) for full DDL and example queries.
## Usage Examples
### Example 1: Basic Phase 3 Trial
**Prompt:** "Generate a Phase 3 oncology trial with 200 subjects"
**Output:**
```json
{
"study": {
"study_id": "ONCO-2025-001",
"protocol_title": "A Phase 3, Randomized, Double-Blind, Placebo-Controlled Study of ABC-123 in Advanced NSCLC",
"phase": "Phase 3",
"therapeutic_area": "Oncology",
"indication": "Non-Small Cell Lung Cancer",
"design": "Randomized, Double-Blind, Placebo-Controlled",
"primary_endpoint": "Overall Survival",
"target_enrollment": 200,
"arms": [
{ "arm_id": "A", "name": "ABC-123 + SOC", "allocation_ratio": 1 },
{ "arm_id": "B", "name": "Placebo + SOC", "allocation_ratio": 1 }
]
},
"sites": [
{ "site_id": "001", "name": "Memorial Cancer Center", "country": "USA", "enrolled": 25 },
{ "site_id": "002", "name": "University Oncology", "country": "USA", "enrolled": 22 }
],
"subjects": [
{
"subject_id": "001-0001",
"site_id": "001",
"arm": "A",
"status": "Active",
"randomization_date": "2025-01-15"
}
]
}
```
### Example 2: Adverse Events with MedDRA
**Prompt:** "Generate adverse events for a 50-subject immunotherapy trial"
**Output:**
```json
{
"domain": "AE",
"adverse_events": [
{
"USUBJID": "IO-001-0023",
"AESEQ": 1,
"AETERM": "Fatigue",
"AEDECOD": "Fatigue",
"AEBODSYS": "General disorders and administration site conditions",
"AESEV": "MILD",
"AESER": "N",
"AEREL": "POSSIBLY RELATED",
"AESTDTC": "2025-02-10",
"AEENDTC": "2025-02-18",
"AEOUT": "RECOVERED/RESOLVED"
},
{
"USUBJID": "IO-001-0007",
"AESEQ": 1,
"AETERM": "Immune-mediated colitis",
"AEDECOD": "Colitis",
"AEBODSYS": "Gastrointestinal disorders",
"AESEV": "SEVERE",
"AESER": "Y",
"AESHOSP": "Y",
"AEREL": "RELATED",
"AEACN": "DRUG INTERRUPTED",
"AESTDTC": "2025-03-05",
"AEOUT": "NOT RECOVERED/NOT RESOLVED"
}
]
}
```
### Example 3: Screening and Enrollment
**Prompt:** "Generate screening data with 30% screen failure rate for 100 subjects"
**Output:**
```json
{
"screening_summary": {
"total_screened": 143,
"screen_passed": 100,
"screen_failed": 43,
"screen_failure_rate": 0.30
},
"screen_failures": [
{
"screening_id": "SCR-0015",
"failure_reason": "Inclusion criteria not met",
"failure_detail": "EGFR mutation negative",
"screening_date": "2025-01-20"
},
{
"screening_id": "SCR-0028",
"failure_reason": "Exclusion criteria met",
"failure_detail": "Prior immunotherapy within 6 months",
"screening_date": "2025-01-25"
}
]
}
```
### Example 4: DM Domain SDTM Output
**Prompt:** "Generate DM domain for 10 subjects as SDTM"
**Output:**
```json
{
"domain": "DM",
"records": [
{
"STUDYID": "ONCO-2025-001",
"DOMAIN": "DM",
"USUBJID": "ONCO-2025-001-001-0001",
"SUBJID": "0001",
"SITEID": "001",
"RFSTDTC": "2025-01-22",
"RFENDTC": null,
"BRTHDTC": "1958-05-15",
"AGE": 66,
"AGEU": "YEARS",
"SEX": "M",
"RACE": "WHITE",
"ETHNIC": "NOT HISPANIC OR LATINO",
"ARMCD": "TRT",
"ARM": "ABC-123 + SOC",
"COUNTRY": "USA"
}
]
}
```
### Example 5: Laboratory Results with LOINC
**Prompt:** "Generate LB domain with liver function tests for safety monitoring"
**Output:**
```json
{
"domain": "LB",
"records": [
{
"STUDYID": "SAFE-001",
"DOMAIN": "LB",
"USUBJID": "SAFE-001-001-0042",
"LBSEQ": 1,
"LBTESTCD": "ALT",
"LBTEST": "Alanine Aminotransferase",
"LBCAT": "CHEMISTRY",
"LBORRES": "32",
"LBORRESU": "U/L",
"LBSTRESN": 32,
"LBSTRESU": "U/L",
"LBSTNRLO": 7,
"LBSTNRHI": 56,
"LBNRIND": "NORMAL",
"LBLOINC": "1742-6",
"LBBLFL": "Y",
"VISITNUM": 2,
"VISIT": "BASELINE"
}
]
}
```
---
## Generative Framework Integration
TrialSim integrates with the [Generative Framework](../generation/SKILL.md) for specification-driven generation at scale.
### Profile-Driven Generation
Use profile specifications to generate trial subject populations:
```
"Generate 150 subjects for an oncology Phase 3 trial"
```
The Profile Executor will:
1. Sample demographics meeting I/E criteria
2. Generate baseline disease characteristics
3. Apply randomization to treatment arms
4. Create screening and baseline assessments
### Journey-Driven Generation
Attach protocol journey specifications to create visit sequences:
```
"Add a 6-cycle treatment protocol journey"
```
The Journey Executor will:
1. Generate protocol visits at specified windows
2. Create assessments per visit schedule
3. Apply visit variance within windows
4. Handle protocol deviations and early termination
### Cross-Domain Sync
When generating across products, TrialSim entities are automatically linked:
| TrialSim Entity | Links To |
|-----------------|----------|
| Subject | PatientSim Patient (via SSN) |
| Site | NetworkSim Facility |
| Investigator | NetworkSim Provider |
| Conmed | RxMemberSim Fill (if applicable) |
See: [../generation/executors/cross-domain-sync.md](../generation/executors/cross-domain-sync.md)
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