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

CODEBLOCK0

Parameters

Parameter	Type	Default	Required	Description
INLINECODE0	str	group_sequential	No	Trial design type
INLINECODE1

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

CODEBLOCK1

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

CODEBLOCK2

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

CODEBLOCK3

Requirements

CODEBLOCK4

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

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

Prerequisites

CODEBLOCK5

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. 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

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⚠️ 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.