# 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"]
  }
}

# Python dependencies
pip install -r requirements.txt