CBARA: Covariate-Balanced-and-Adjusted Response-Adaptive Randomization

TL;DR

The paper introduces CBARA, a novel adaptive randomization method for clinical trials that combines covariate adjustment and response adaptation, improving balance and robustness without requiring a correct model.

Contribution

It extends covariate-adaptive randomization by integrating response-adaptive features, providing a robust, model-free approach that enhances treatment balance and ethical considerations.

Findings

CBARA improves covariate balance for observed and unobserved covariates.

Theoretical properties of CBARA are established using a pseudo-Markov chain framework.

CBARA maintains consistency of the allocation ratio while enhancing robustness.

Abstract

We propose the covariate-balanced-and-adjusted response-adaptive randomization (CBARA) procedure for adaptive design in clinical trials, which integrates the complementary strengths of covariate-adjusted response-adaptive randomization (CARA) and covariate-adaptive randomization (CAR). The CBARA procedure updates the target allocation ratio according to observed responses and patient covariate profiles without requiring a correctly specified model, thereby retaining CARA's ethical and efficiency considerations while improving robustness. In addition, the CBARA procedure extends the CAR principle from fixed target allocation ratios to covariate-adjusted adaptive target allocation ratios, yet still pursues balance in treatment allocation with respect to covariate features. This integration is enabled by a newly defined imbalance vector and three interrelated components: the allocation function, parameter estimation and update mechanism. We establish the asymptotic properties of covariate imbalance and the estimators under the CBARA procedure. The results demonstrate that the CBARA procedure can improve balance for both observed and unobserved covariates while preserving the consistency of the allocation ratio. The theoretical analysis is developed through a pseudo-Markov chain framework, where a new discrepancy measure for transition kernels is introduced to handle the continuity of Poisson equation solutions with respect to parameters.