Journal of Biometrics & Biostatistics

A Bayesian Response-adaptive Covariate-adjusted Randomization Design for Clinical Trials

Abstract

Jianchang Lin, Li-An Lin and Serap Sankoh

Accordingly to FDA draft guidance (2010), adaptive randomization (e.g. response-adaptive (RA) randomization) has become popular in clinical research because of its flexibility and efficiency, which also have the 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 randomization procedure 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 where Ning and Huang (2010) approach requires polytomizing continuous covariates and Yuan (2011) approach uses fixed allocation probability to adjust covariates imbalance.