ASA BIOP SWG: Clinical Trial Efficiency Enhancement Initiative
This repository contains advanced statistical methods and simulation tools for enhancing clinical trial operations through data-driven optimization.
Status: 6 of 6 core tasks completed | Last Updated: December 2024
A comprehensive clinical trial simulation engine with country-specific parameters for realistic modeling of:
- Site Infrastructure: Realistic site networks with staggered activation
- Patient Modeling: Gamma-Poisson arrivals, screening, demographics, dropout
- Randomization: Block randomization with flexible stratification for drug supply optimization
- Visit Scheduling: Protocol-defined visits with compliance modeling
- Enrollment Forecasting: Bayesian parameter estimation with uncertainty quantification
- Visit Forecasting: Bayesian visit compliance modeling with temporal decline
- Dose Forecasting: Advanced dosing protocols with multi-vial optimization (75% wastage reduction)
- 31 country-specific parameter types across 7 tasks
- Complete end-to-end simulation from site generation to drug consumption
- Advanced forecasting with Bayesian methods and JAGS integration
- Multi-vial optimization reducing drug wastage from 35% to 7-10%
- Real-time monitoring with cost optimization scenarios
# Run complete clinical trial simulation
source("CSC/examples/complete_simulation_pipeline.R")
# Generates comprehensive simulation with:
# - 45 sites across 4 countries
# - ~2,400 patients with realistic demographics
# - Complete randomization and visit scheduling
# - Advanced drug consumption forecasting-
Clone the repository
git clone https://github.com/efficiencyplustrials/efficiencyplustrials.github.io.git cd efficiencyplustrials.github.io -
Navigate to CSC project
cd CSC -
Run the complete simulation
source("examples/complete_simulation_pipeline.R")
- Site Generation: 45 sites with country-specific parameters
- Patient Enrollment: ~2,400 patients with realistic screening and demographics
- Visit Compliance: Country-specific patterns (Japan: 95%, Germany: 92%, USA: 88%, China: 85%)
- Drug Optimization: 75% reduction in wastage through multi-vial optimization
- Enrollment Forecasting: Bayesian sigmoid curve estimation with seasonal adjustments
- Visit Forecasting: Compliance modeling with temporal decline patterns
- Dose Forecasting: Multiple protocols with dynamic vial optimization
- JAGS integration for parameter estimation
- Hierarchical modeling for country-specific parameters
- Uncertainty quantification with credible intervals
- Multi-vial optimization using dynamic programming
- Real-time wastage monitoring with alert systems
- Cost optimization scenarios for budget planning
- 31 parameter types covering all aspects of clinical trials
- Realistic population characteristics by region
- Regulatory and operational differences by country
- Resource planning with accurate visit volume projections
- Drug supply optimization with minimal wastage
- Site performance monitoring and capacity planning
- Enrollment timeline forecasting with uncertainty bounds
- Country-specific compliance patterns for regulatory submissions
- Risk assessment and mitigation strategies
- Budget forecasting with drug consumption optimization
- Scenario planning for different operational approaches
- ROI analysis for efficiency improvements
This project is part of the ASA BIOP Statistical Working Group initiative. Contributions are welcome through:
- Issue reporting for bugs or enhancement requests
- Pull requests for new features or improvements
- Documentation improvements and examples
This project is licensed under the MIT License - see the LICENSE file for details.
- ASA BIOP SWG: American Statistical Association Biopharmaceutical Section
- Project Documentation: CSC Documentation
- Live Demo: Efficiency+ Trials Website
Enhancing clinical trial efficiency through advanced statistical methods and data-driven optimization.