Inference on the average treatment effect under minimization and other covariate-adaptive randomization methods

• 成果类型: Article
• 署名作者: Ye, Ting; Yi, Yanyao; Shao, Jun
• 署名单位: University of Pennsylvania; Eli Lilly; East China Normal University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asab015
• 发表日期: 2022
• 页码: 3347
• 关键词: sequential treatment allocation; biased coin randomization; clinical-trials; regression adjustments; designs; validity

摘要

Covariate-adaptive randomization schemes such as minimization and stratified permuted blocks are often applied in clinical trials to balance treatment assignments across prognostic factors. The existing theory for inference after covariate-adaptive randomization is mostly limited to situations where a correct model between the response and covariates can be specified or the randomization method has well-understood properties. Based on stratification with covariate levels utilized in randomization and a further adjustment for covariates not used in randomization, we propose several model-free estimators of the average treatment effect. We establish the asymptotic normality of the proposed estimators under all popular covariate-adaptive randomization schemes, including the minimization method, and we show that the asymptotic distributions are invariant with respect to covariate-adaptive randomization methods. Consistent variance estimators are constructed for asymptotic inference. Asymptotic relative efficiencies and finite-sample properties of estimators are also studied. We recommend using one of our proposed estimators for valid and model-free inference after covariate-adaptive randomization.