A Phase II Trial Design with Bayesian Adaptive Covariate-Adjusted Randomization

Abstract

Adaptive randomization (e.g. response-adaptive (RA) randomization) has become popular in phase II clinical research because of its flexibility and efficiency, which also have the patient centric advantage of assigning fewer patients to inferior treatment arms. However, these designs lack a mechanism to actively control the imbalance of prognostic factors, i.e. covariates that substantially affect the study outcome. Improving the balance of patient characteristics among the treatment arms could potentially increases the statistical power of the trial. We propose a phase II clinical trial design that is response-adaptive and that also actively balances the covariates across treatment arms. We then incorporate this method into a sequential RA randomization design such that the resulting design skews the allocation probability to the better treatment arm, and also controls the imbalance of the prognostic factors across the arms. The proposed method extends the existing randomization procedures which either requires polytomizing continuous covariates or uses fixed allocation probability to adjust covariates imbalance. Simulation studies are also conducted to examine the operating characteristics of the design with existing approaches to illustrate the recommendation for clinical practice.