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Randomization is a key component of a successful controlled clinical trial. Many restricted and minimization randomization methods have been developed to balance the distributions of covariates across treatment arms to remove potential confounding effects. While the restricted randomization methods would not work well if the number of covariates is large, the theoretical base of the minimization methods needs more justifications. We propose a Bayesian Covariate-Adaptive Randomization (BayCAR) method that not only has meaningful interpretations on its biasing randomization probabilities, but also achieves desirable overall and marginal balances, especially when the number of covariates to be balanced is large.

The main function is FUN.BCAR.

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