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

# 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

{
  "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

⚠️ AI自主验收状态: 需人工检查

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

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

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.