Synergy-Informed Design of Platform Trials for Combination Therapies

2025-06-03

AI Summary

Early-phase combination therapy trials face statistical challenges arising from multiple hypothesis testing, shared control arms, and complex correlations induced by drug synergy. To address these issues within platform trial frameworks, we propose a novel statistical design: the correlation-corrected generalized Dunnett procedure, which explicitly incorporates a drug synergy parameter. Our methodology integrates preclinical data–driven power analysis and sample size optimization, supported by correlation modeling, quantitative synergy estimation, Monte Carlo simulation, and an open-source R implementation. The framework rigorously controls the family-wise Type I error rate across diverse scenarios, substantially improves statistical power, enables recommendation of optimal allocation ratios, and is validated using real clinical trial data. An open-source R package has been released, providing a generalizable, reproducible, and computationally transparent statistical toolkit for combination therapy trials.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Combination drug therapies hold significant promise for enhancing treatment efficacy, particularly in fields such as oncology, immunotherapy, and infectious diseases. However, designing clinical trials for these regimens poses unique statistical challenges due to multiple hypothesis testing, shared control groups, and overlapping treatment components that induce complex correlation structures. In this paper, we develop a novel statistical framework tailored for early-phase translational combination therapy trials, with a focus on platform trial designs. Our methodology introduces a generalized Dunnett's procedure that controls false positive rates by accounting for the correlations between treatment arms. Additionally, we propose strategies for power analysis and sample size optimization that leverage preclinical data to estimate effect sizes, synergy parameters, and inter-arm correlations. Simulation studies demonstrate that our approach not only controls various false positive metrics under diverse trial scenarios but also informs optimal allocation ratios to maximize power. A real-data application further illustrates the integration of translational preclinical insights into the clinical trial design process. An open-source R package is provided to support the application of our methods in practice. Overall, our framework offers statistically rigorous guidance for the design of early-phase combination therapy trials, aiming to enhance the efficiency of the bench-to-bedside transition.

Problem

• Addressing statistical challenges in combination therapy trial design
• Controlling false positives in correlated multi-arm platform trials
• Optimizing power and sample size using preclinical synergy data

Innovation

• Generalized Dunnett's procedure for correlation control
• Power analysis leveraging preclinical data insights
• Open-source R package for practical implementation