Inclusion Criteria Generator

Generate and optimize clinical trial subject inclusion/exclusion criteria to balance scientific rigor with recruitment feasibility.

Use Cases

• Protocol Design: Create initial eligibility criteria for new clinical trials
• Criteria Optimization: Refine existing criteria to improve enrollment without compromising safety/efficacy
• Competitive Analysis: Analyze eligibility patterns across similar trials
• Recruitment Strategy: Identify and mitigate barriers to enrollment
• Feasibility Assessment: Evaluate if proposed criteria are realistic for target population

Usage

CLI Usage

# Generate criteria from study design
python scripts/main.py generate \
  --indication "Type 2 Diabetes" \
  --phase "Phase 2" \
  --population "adults" \
  --duration "24 weeks" \
  --output criteria.json

# Optimize existing criteria
python scripts/main.py optimize \
  --input current_criteria.json \
  --enrollment-target 200 \
  --current-enrollment 120 \
  --output optimized_criteria.json

# Analyze criteria complexity
python scripts/main.py analyze \
  --input criteria.json \
  --output analysis_report.json

# Compare with competitor trials
python scripts/main.py benchmark \
  --input criteria.json \
  --condition "Type 2 Diabetes" \
  --output benchmark_report.json

Python API

from scripts.main import CriteriaGenerator, CriteriaOptimizer

# Generate new criteria
generator = CriteriaGenerator()
criteria = generator.generate(
    indication="Type 2 Diabetes",
    phase="Phase 2",
    population="adults",
    study_duration="24 weeks",
    endpoints=["HbA1c reduction", "weight change"]
)

# Optimize existing criteria
optimizer = CriteriaOptimizer()
optimized = optimizer.optimize(
    criteria=existing_criteria,
    enrollment_target=200,
    current_enrollment=120,
    retention_rate=0.85
)

# Analyze criteria complexity
analysis = optimizer.analyze_complexity(criteria)

Input Format

Study Design Parameters

{
  "indication": "Type 2 Diabetes Mellitus",
  "phase": "Phase 2",
  "population": "adults",
  "age_range": {"min": 18, "max": 75},
  "study_duration": "24 weeks",
  "treatment_type": "oral",
  "primary_endpoints": ["HbA1c change from baseline"],
  "safety_considerations": ["cardiovascular risk"],
  "concomitant_meds_allowed": ["metformin"]
}

Existing Criteria Format

{
  "inclusion_criteria": [
    {
      "id": "I1",
      "criterion": "Age 18-75 years",
      "rationale": "Adult population per regulatory guidance",
      "category": "demographics"
    }
  ],
  "exclusion_criteria": [
    {
      "id": "E1",
      "criterion": "HbA1c < 7.0% or > 11.0%",
      "rationale": "Ensure measurable treatment effect",
      "category": "disease_severity"
    }
  ]
}

Output Format

Generated/Optimized Criteria

{
  "inclusion_criteria": [
    {
      "id": "I1",
      "criterion": "Age 18-75 years, inclusive",
      "category": "demographics",
      "rationale": "Adult population; upper limit for safety",
      "priority": "required",
      "impact": "low"
    }
  ],
  "exclusion_criteria": [
    {
      "id": "E1",
      "criterion": "HbA1c < 7.5% or > 10.5% at screening",
      "category": "disease_severity",
      "rationale": "Optimal range for detecting treatment effect",
      "priority": "required",
      "impact": "medium",
      "flexibility": "widen by 0.5% if enrollment slow"
    }
  ],
  "optimization_notes": [
    "Widened HbA1c range from 7.0-11.0% to 7.5-10.5% based on feasibility data"
  ],
  "recruitment_metrics": {
    "estimated_screen_success_rate": 0.35,
    "estimated_enrollment_rate": 0.65,
    "key_barriers": ["HbA1c upper limit", "concomitant medication restrictions"]
  }
}

Criteria Categories

Category	Description	Examples
demographics	Age, sex, race, ethnicity	Age 18-75, women of childbearing potential
disease_severity	Disease stage, severity markers	HbA1c range, tumor stage, NYHA class
medical_history	Prior conditions, comorbidities	No cardiovascular events within 6 months
concomitant_meds	Allowed/prohibited medications	Stable metformin dose allowed
laboratory	Lab value requirements	eGFR > 30 mL/min, normal liver function
lifestyle	Diet, exercise, habits	Non-smoker, willing to maintain diet
compliance	Ability to participate	Able to provide informed consent
safety	Risk minimization criteria	No history of severe hypoglycemia

Optimization Strategies

Common Modifications

Issue	Strategy	Example
Narrow age range	Widen limits	18-70 → 18-75 years
Restrictive lab values	Adjust thresholds	eGFR > 60 → eGFR > 30 mL/min
Comorbidity exclusions	Add time limits	Exclude "current" vs "history of"
Medication washouts	Shorten periods	4 weeks → 2 weeks
Geographic barriers	Add telemedicine	Include remote visits option

Retention Considerations

• Minimize visit frequency when possible
• Allow window periods for visit timing
• Provide transportation assistance language
• Consider patient-reported outcome burden

Technical Details

• Difficulty: Medium
• Standards: ICH E6(R2) GCP, CDISC Protocol Representation Model
• Data Sources: ClinicalTrials.gov eligibility patterns, literature feasibility data
• Dependencies: None (pure Python)

References

• references/criteria_templates.json - Templates by therapeutic area
• references/optimization_guidelines.md - Best practices for criteria optimization
• references/common_pitfalls.md - Frequent eligibility design mistakes
• references/regulatory_guidance.md - FDA/EMA guidance on eligibility criteria
• references/feasibility_data.json - Screen failure rates by criterion type

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python scripts with tools	High
Network Access	External API calls	High
File System Access	Read/write data	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Data handled securely	Medium

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• API requests use HTTPS only
• Input validated against allowed patterns
• API timeout and retry mechanisms implemented
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no internal paths exposed)
• Dependencies audited
• No exposure of internal service architecture

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support

Parameters

Parameter	Type	Default	Description
--indication	str	Required	Therapeutic indication
--phase	str	Required
--population	str	"adults"	Target population
--duration	str	""	Study duration
--output	str	Required	Output file path
--age-min	int	18	Minimum age
--age-max	int	75	Maximum age
--input	str	Required	Input criteria JSON file
--enrollment-target	int	Required	Target enrollment
--current-enrollment	int	Required	Current enrollment
--output	str	Required	Output file path
--input	str	Required	Input criteria JSON file
--output	str	Required	Output file path
--input	str	Required	Input criteria JSON file
--condition	str	Required	Medical condition
--output	str	Required	Output file path