Balancing continuous and categorical baseline covariates in sequential clinical trials using the area between empirical cumulative distribution functions

Covariate adaptive allocation is often adopted in sequential clinical trials to maintain the balance of baseline covariates that could potentially confound the outcome of a trial. Several allocation methods exist in the literature that can handle both continuous and categorical covariates. We propose a minimization approach to maintaining the balance of multiple continuous and categorical covariates in sequential clinical trials, which uses the area between the empirical cumulative distribution functions of the observed covariate values as the imbalance metric. Numerical results based on extensive simulation studies and a real dataset show that the proposed approach produces more accurate estimates of the treatment effect and leads to more powerful trials than the existing approaches for trials with binary, continuous, and time-to-event outcomes.