healthsim-trialsim

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.

Safety Guardrails

All data generated by TrialSim is synthetic and fictional. It must never be used for real clinical decisions, patient care, or regulatory submissions without explicit disclaimers.

• Synthetic data only: All subjects, sites, adverse events, and results are generated/simulated. Always confirm this when presenting output.
• No clinical advice: Never recommend treatments, prescribe medications, or interpret safety signals as if data were real. If asked "should this patient receive X?", remind the user this is synthetic test data.
• Real codes, synthetic entities: Use real standard code systems (ICD-10, CPT, LOINC, SNOMED CT, MedDRA, RxNorm, NDC, NPI, ATC, HCPCS) for coding accuracy, but all people, sites, and events are fictional.
• Do NOT generate: Real patient identifiers, actual investigator names, real site addresses, or any data that could be confused with actual clinical trial records.

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	Core trial concepts, phases, regulatory
Recruitment	recruitment-enrollment.md	Screening funnel, enrollment patterns

Trial Phase Skills

Phase	Skill	Description
Phase 1	phase1-dose-escalation.md	FIH, dose escalation, MTD (3+3, BOIN, CRM)
Phase 2	phase2-proof-of-concept.md	POC, dose-ranging, futility (Simon's, MCP-Mod)
Phase 3	phase3-pivotal.md	Pivotal registration trials, NDA/BLA

SDTM Domain Skills

Domain	Skill	Description
DM	domains/demographics-dm.md	Subject demographics, treatment arms
AE	domains/adverse-events-ae.md	Adverse events with MedDRA coding
VS	domains/vital-signs-vs.md	Vital sign measurements
LB	domains/laboratory-lb.md	Laboratory results with LOINC
CM	domains/concomitant-meds-cm.md	Concomitant medications with ATC
EX	domains/exposure-ex.md	Study drug exposure, dose modifications
DS	domains/disposition-ds.md	Subject disposition, discontinuation
MH	domains/medical-history-mh.md	Medical history, comorbidities
RS/TR	therapeutic-areas/oncology.md	Tumor results / disease response (RECIST)
Domain Index	domains/README.md	All SDTM domains overview

Therapeutic Areas

Area	Skill	Key Endpoints
Oncology	therapeutic-areas/oncology.md	Tumor response (RECIST), ORR, PFS, OS
Cardiovascular	therapeutic-areas/cardiovascular.md	MACE, CV outcomes
CNS	therapeutic-areas/cns.md	Cognitive scales, imaging
CGT	therapeutic-areas/cgt.md	CAR-T, gene therapy

Real World Evidence

Topic	Skill	Description
RWE Overview	rwe/overview.md	RWE concepts, data sources
Synthetic Controls	rwe/synthetic-control.md	External control arm generation

Output Formats

Format	Skill	Use Case
SDTM	../../formats/cdisc-sdtm.md	Regulatory submission
ADaM	../../formats/cdisc-adam.md	Statistical analysis
Dimensional	../../formats/dimensional-analytics.md	BI dashboards, analytics
JSON	Default	API integration
CSV	../../formats/csv.md	Spreadsheet analysis

Data Models & References

Resource	Location	Description
Canonical 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	Star schema for BI (7 dims, 6 facts)
Code Systems	../../references/code-systems.md	MedDRA, LOINC, ATC, ICD-10, SNOMED CT, RxNorm, NDC, NPI, HCPCS, CPT

Core Entities

TrialSim uses 15 canonical entity schemas. See Data Models Reference 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:

{
  "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):

{
  "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/ - Oncology trial subjects
• ../patientsim/heart-failure.md - CV outcomes trial subjects
• ../patientsim/behavioral-health.md - CNS trial subjects
• ../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 provides embedded real-world data (CDC PLACES, SVI, ADI) for evidence-based trial planning, site selection, and FDA diversity compliance. When geographies are specified, TrialSim grounds feasibility estimates and enrollment projections in actual prevalence and demographic data.

See populationsim-integration.md for detailed data-driven planning patterns, embedded data sources, and site selection examples.

Development Status

All skill files complete: core skills, Phase 1-3, SDTM domains (DM, AE, VS, LB, CM, EX, DS, MH), therapeutic areas, and RWE.

Related Skills

• PatientSim - Clinical patient data
• MemberSim - Claims integration
• Code Systems - Standard terminologies (ICD-10, CPT, LOINC, SNOMED, RxNorm, NDC, HCPCS)

Usage Examples

Example 1: Basic Phase 3 Trial

Prompt: "Generate a Phase 3 oncology trial with 200 subjects"

Output:

{
  "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:

{
  "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:

{
  "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:

{
  "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:

{
  "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"
    }
  ]
}

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Example 6: ADaM Datasets (ADSL + ADTTE)

Prompt: "Derive ADSL and ADTTE datasets from the SDTM data for this trial"

ADSL (subject-level analysis dataset with baseline characteristics):

{
  "dataset": "ADSL",
  "records": [
    {
      "STUDYID": "ONCO-2025-001", "USUBJID": "ONCO-2025-001-001-0001",
      "SUBJID": "0001", "SITEID": "001", "TRT01P": "ABC-123 + SOC",
      "AGE": 66, "SEX": "M", "RACE": "WHITE",
      "RFSTDTC": "2025-01-22", "RFENDTC": "2025-07-15",
      "EOSSTT": "COMPLETED", "DCSREAS": null,
      "BMIBL": 24.3, "ECOGBL": 1
    }
  ]
}

ADTTE (time-to-event analysis dataset):

{
  "dataset": "ADTTE",
  "records": [
    {
      "STUDYID": "ONCO-2025-001", "USUBJID": "ONCO-2025-001-001-0001",
      "PARAMCD": "PFS", "PARAM": "Progression-Free Survival",
      "AVAL": 182, "AVALU": "DAYS",
      "CNSR": 0, "EVNTDESC": "Disease Progression (RECIST)",
      "STARTDT": "2025-01-22", "ADT": "2025-07-23"
    },
    {
      "STUDYID": "ONCO-2025-001", "USUBJID": "ONCO-2025-001-001-0002",
      "PARAMCD": "OS", "PARAM": "Overall Survival",
      "AVAL": 365, "AVALU": "DAYS",
      "CNSR": 1, "EVNTDESC": "Censored (alive at cutoff)",
      "STARTDT": "2025-01-29", "ADT": "2026-01-29"
    }
  ]
}

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Generative Framework Integration

TrialSim integrates with the Generative Framework for specification-driven generation at scale.

Profile-Driven Generation

Use profile specifications to generate trial subject populations. The Profile Executor samples demographics meeting I/E criteria, generates baseline disease characteristics, applies randomization, and creates screening assessments.

Journey-Driven Generation

Attach protocol journey specifications to create visit sequences. The Journey Executor generates protocol visits at specified windows, creates assessments per schedule, applies visit variance, and handles 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