Sequential Monitoring of Covariate-Adaptive Randomized Clinical Trials With Non-Parametric Approaches

The importance of covariate adjustment in clinical trials has been underscored by the U.S. FDA's guidance. Inference, with or without covariates, after implementing covariate adaptive randomization (CAR), is garnering increased interest. This paper investigates the sequential monitoring of covariate-adaptive randomized clinical trials through non-parametric methods, a critical advancement for enhancing the precision and efficiency of medical research. CAR, which incorporates baseline patient characteristics into the randomization process, aims to mitigate the risk of confounding and improve the balance of covariates across treatment groups, thereby addressing patients' heterogeneity. Although CAR is known for its benefits in reducing biases and enhancing statistical power, its integration into sequentially monitored clinical trials-a standard practice-poses methodological challenges, particularly in controlling the type I error rate. By employing a non-parametric approach, we demonstrate through theoretical proofs and numerical analyses that our methods effectively control the type I error rate and surpass traditional randomization and analysis methods. This paper not only fills a gap in the literature on sequential monitoring of CAR without model misspecification but also proposes practical solutions for enhancing trial design and analysis, thereby contributing significantly to the field of clinical research.