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.
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.
# 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
| 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 |
# 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 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 |
| 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 |
{
"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
}
}
}
⚠️ AI自主验收状态: 需人工检查
This skill requires:
pip install -r requirements.txt
numpy>=1.20.0
scipy>=1.7.0
matplotlib>=3.4.0
| Risk Indicator | Assessment | Level |
|---|---|---|
| Code Execution | Python scripts with mathematical calculations | Medium |
| Network Access | No network access | Low |
| File System Access | Writes simulation results | Low |
| Instruction Tampering | Statistical parameters could affect results | Medium |
| Data Exposure | No sensitive data exposure | Low |
pip install -r requirements.txt
python scripts/main.py --help
Available in references/:
⚠️ 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.