| name | adaptive-trial-simulator |
| description | Design and simulate adaptive clinical trials with interim analyses, sample size re-estimation, and early stopping rules. Evaluate Type I error control, power, and expected sample size via Monte Carlo simulation before trial initiation. |
| version | 1.0.0 |
| category | Clinical |
| tags | ["clinical-trials","adaptive-design","statistics","simulation","biostatistics"] |
| author | AIPOCH |
| license | MIT |
| status | Draft |
| risk_level | Medium |
| skill_type | Tool/Script |
| owner | AIPOCH |
| reviewer | |
| last_updated | 2026-02-15 |
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.
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
python scripts/main.py
python scripts/main.py --design adaptive_reestimate
python scripts/main.py --optimize
Parameters
| Parameter | Type | Default | Required | Description |
|---|
--design | str | group_sequential | No | Trial design type |
--n-simulations | int | 10000 | No | Number of Monte Carlo simulations |
--sample-size | int | 200 | No | Initial sample size per arm |
--effect-size | float | 0.3 | No | Effect size (Cohen's d) |
--alpha | float | 0.05 | No | Type I error rate |
--power | float | 0.80 | No | Target statistical power |
--interim-looks | int | 1 | No | Number of interim analyses |
