adaptive-trial-simulator
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.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/aipoch-ai/adaptive-trial-simulatorAdaptive 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
# 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
| 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 |
--spending-function | str | obrien_fleming | No | Alpha spending function |
--reestimate-method | str | promising_zone | No | Sample size re-estimation method |
--output | str | results.json | No | Output file path |
--visualize | flag | False | No | Generate visualization charts |
--optimize | flag | False | No | Search for optimal design 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
| Design Type | Description | Use Case |
|---|---|---|
| Group Sequential | Fixed interim looks with stopping boundaries | Standard adaptive trials |
| Adaptive Re-estimate | Sample size adjustment based on interim data | Uncertain effect size |
| Drop the Loser | Multi-arm trials dropping inferior arms | Phase II dose selection |
Spending Functions
| Function | Characteristics | Early Boundary |
|---|---|---|
| O'Brien-Fleming | Conservative early | High Z-scores early |
| Pocock | Aggressive early | Lower Z-scores throughout |
| Power Family | Moderate (ρ=3) | Balanced approach |
Output Example
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-aipoch-ai-adaptive-trial-simulator": {
"enabled": true,
"auto_update": true
}
}
}Tags
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