  Balancing influential covariates is crucial for valid treatment comparisons
in clinical studies. While covariate-adaptive randomization is commonly used to
achieve balance, its performance can be inadequate when the number of baseline
covariates is large. It is therefore essential to identify the influential
factors associated with the outcome and ensure balance among these critical
covariates. In this article, we propose a novel covariate-adjusted
response-adaptive randomization that integrates the patients' responses and
covariates information to select sequentially significant covariates and
maintain their balance. We establish theoretically the consistency of our
covariate selection method and demonstrate that the improved covariate
balancing, as evidenced by a faster convergence rate of the imbalance measure,
leads to higher efficiency in estimating treatment effects. Furthermore, we
provide extensive numerical and empirical studies to illustrate the benefits of
our proposed method across various settings.
