Inclusion Criteria Gen

scientific rigor with recruitment feasibility. Trigger when users need to design

eligibility criteria for new trials, optimize existing criteria for better enrollment,

analyze competitor trial eligibility patterns, or assess recruitment barriers.

Use cases: Protocol design, eligibility optimization, recruitment strategy,

competitive eligibility analysis, feasibility assessment.

' version: 1.0.0 category: Pharma tags:

Inclusion Criteria Generator

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

Use Cases

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

Technical Details

References

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

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

Test Cases

Lifecycle Status

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 |

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)

# Python dependencies
pip install -r requirements.txt