A family of Bayesian prognostic and predictive covariate-adjusted response-adaptive randomization designs.

Abstract

The prudent use of covariates to enhance the efficiency and ethics of clinical trials has garnered significant attention, particularly following the FDA's 2023 guidance on adjusting for covariates. This article introduces a Bayesian covariate-adjusted response-adaptive design aimed at distinguishing between prognostic and predictive covariates during randomization and analysis. The proposed design allocates more patients to the superior treatment based on predictive covariates while maintaining balance across prognostic covariate levels, without sacrificing the power to detect treatment effects. Predictive covariates, which identify patients more likely to benefit from a treatment, and prognostic covariates, which predict overall clinical outcomes, are crucial for personalized medicine and ethical rigor in clinical trials. The Bayesian covariate-adjusted response-adaptive design leverages these covariates to enhance precision and ensure balanced comparison groups, addressing patient heterogeneity and improving treatment efficacy. Our approach builds on the foundation of response-adaptive randomization designs, incorporating Bayesian methodologies to manage the complexities of adaptive designs and control the Type I error rate. Comprehensive numerical studies demonstrate the advantages of our design in achieving ethical, efficient, and balancing goals.