Withdrawn: 10 Inference for the Covariate-adaptive Randomization in a Two-stage Design

Thomas M. Menino Convention & Exhibition Center 

We investigate statistical inference for treatment effects in two-stage designs under covariate-adaptive randomization. We select the treatment with the most promising estimated effect from multiple experimental arms in the initial stage and use a controlled study to confirm the efficacy of the selected treatment in the second stage. However, this setting faces two key challenges: (i) obtaining valid and efficient inference results under covariate-adaptive randomization, and (ii) correcting for the selection bias introduced during the selection in the first stage. To address these, we develop an inference procedure under covariate-adaptive randomization without discretizing continuous covariates, enabling more efficient inference results for two-stage designs. Additionally, we employ a resampling method to mitigate the selection bias. Numerical studies show that the proposed approach often improves performance over the existing approaches for inference under a two-stage design employing covariate-adaptive randomization.

Clinical trial

Covariate adaptive-randomization

Two-stage design 

Biopharmaceutical Section