Adaptive Trial Simulator

Source: https://github.com/aipoch/medical-research-skills

Adaptive Trial Simulator

Statistical simulation platform for designing and validating adaptive clinical trial designs in silico. Enables optimization of interim analysis strategies, sample size adaptation, and early stopping rules while maintaining Type I error control.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py --design group_sequential --n-simulations 50
python scripts/main.py --design adaptive_reestimate --n-simulations 25 --optimize

When to Use

• Use this skill when the task is to Design and simulate adaptive clinical trials with interim analyses.
• Use this skill for protocol design tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when the response must stay inside the documented task boundary instead of expanding into adjacent work.

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Features

• Design Simulation: Monte Carlo validation of adaptive designs
• Sample Size Re-estimation: Adapt sample size based on interim data
• Early Stopping Rules: Futility and efficacy boundary optimization
• Type I Error Control: Validate alpha spending strategies
• Multi-Arm Designs: Drop-the-loser and seamless Phase II/III
• Power Optimization: Identify designs with maximum power efficiency

Usage

Basic Usage

# Run standard group sequential design
python scripts/main.py

# Adaptive design with sample size re-estimation
python scripts/main.py --design adaptive_reestimate

# Optimize design parameters
python scripts/main.py --optimize

Parameters

Advanced Usage

# Full adaptive design with visualization
python scripts/main.py \
  --design adaptive_reestimate \
  --n-simulations 50000 \
  --sample-size 250 \
  --effect-size 0.35 \
  --interim-looks 2 \
  --spending-function obrien_fleming \
  --visualize \
  --output adaptive_results.json

Design Types

Spending Functions

Output Example

{
  "design_config": {
    "design_type": "adaptive_reestimate",
    "sample_size_per_arm": 200,
    "effect_size": 0.3,
    "alpha": 0.05,
    "target_power": 0.8
  },
  "simulation_results": {
    "power": 0.8234,
    "type_i_error": 0.0481,
    "expected_sample_size": 385.2,
    "early_stop_rate": {
      "efficacy": 0.1523,
      "futility": 0.0841
    }
  }
}

Technical Difficulty: HIGH

References

• references/audit-reference.md - Audit-ready assumptions, supported design modes, and fallback boundaries for constrained runs

⚠️ AI independent acceptance status: manual inspection required This skill requires:

• Python 3.8+ environment
• NumPy, SciPy, and Matplotlib packages
• Understanding of clinical trial statistics

Dependencies

pip install -r requirements.txt

Requirements

numpy>=1.20.0
scipy>=1.7.0
matplotlib>=3.4.0

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access
• Output does not expose sensitive information
• Input parameters validated
• Error messages sanitized
• Dependencies audited

Prerequisites

pip install -r requirements.txt
python scripts/main.py --help

Evaluation Criteria

Success Metrics

• Simulations run without errors
• Type I error controlled at nominal level
• Power estimates are accurate
• Visualizations generated correctly

Test Cases

1. Basic Simulation: Default parameters → Valid results
2. Different Designs: All design types → Appropriate behavior
3. Optimization Mode: --optimize flag → Finds optimal parameters
4. Visualization: --visualize flag → Charts generated

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-15
• Known Issues: Type checking warnings with numpy arrays
• Planned Improvements:
  • Bayesian adaptive designs
  • Multi-arm multi-stage (MAMS) support
  • Enhanced visualization options

References

Available in references/:

• Adaptive design statistical theory
• Regulatory guidance documents
• Alpha spending function literature
• Sample size re-estimation methods

Limitations

• Statistical Complexity: Requires biostatistics expertise
• Simulation Time: Large simulations may take hours
• Simplified Models: Does not capture all real-world complexities
• Regulatory Consultation: Results should be validated with regulators

⚠️ DISCLAIMER: This tool provides simulation results for research and planning purposes only. All clinical trial designs should be reviewed by qualified biostatisticians and regulatory experts before implementation.

Output Requirements

Every final response should make these items explicit when they are relevant:

• Objective or requested deliverable
• Inputs used and assumptions introduced
• Workflow or decision path
• Core result, recommendation, or artifact
• Constraints, risks, caveats, or validation needs
• Unresolved items and next-step checks