Covariate-adaptive designs with missing covariates in clinical trials

Many covariate-adaptive randomization procedures have been proposed and implemented to balance important covariates in clinical trials. These methods are usually based on fully observed covariates. In practice, the covariates of a patient are often partially missing. We propose a novel covariate-adaptive design to deal with missing covariates and study its properties. For the proposed design, we show that as the number of patients increases, the overall imbalance, observed margin imbalance and fully observed stratum imbalance are bounded in probability. Under certain covariate-dependent missing mechanism, the proposed design can balance missing covariates as if the covariates are observed. Finally, we explore our methods and theoretical findings through simulations.