Kernel-based adaptive randomization toward balance in continuous and discrete covariates

Abstract

Covariate balance among different treatment arms is critical in clinical trials, as confounding effects can be effectively eliminated when patients in different arms are alike. To balance the prognostic factors across different arms, we propose a new dynamic scheme for patient allocation. Our approach does not require discretizing continuous covariates to multiple categories, and can handle both continuous and discrete covariates naturally. This is achieved through devising a statistical measure to characterize the similarity between a new patient and all the existing patients in the trial. Under the similarity weighting scheme, we develop a covariate-adaptive biased coin design and establish its theoretical properties, thus improving the original Pocock-Simon design. We conduct extensive simulation studies to examine the design operating characteristics and we illustrate our method with a data example. The new approach is thereby demonstrated to be superior to existing methods in terms of performance.