Generate and optimize clinical trial subject inclusion/exclusion criteria to balance 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.
Generate and optimize clinical trial subject inclusion/exclusion criteria to balance scientific rigor with recruitment feasibility.
# 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
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)
{
"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"]
}
{
"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"
}
]
}
{
"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"]
}
}
| 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 |
| 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 |
references/criteria_templates.json - Templates by therapeutic areareferences/optimization_guidelines.md - Best practices for criteria optimizationreferences/common_pitfalls.md - Frequent eligibility design mistakesreferences/regulatory_guidance.md - FDA/EMA guidance on eligibility criteriareferences/feasibility_data.json - Screen failure rates by criterion type| 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 |
# Python dependencies
pip install -r requirements.txt
| 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 |