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

Optimum biased coin designs for sequential clinical trials with prognostic factors

Ting Ye, Yanyao Yi and Jun Shao

Biometrika, 2022, vol. 109, issue 1, 33-47

Abstract: SummaryCovariate-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.

Keywords: Balancing of treatment assignments; Covariate adjustment; Efficiency; Generalized regression; Model-free inference; Multiple treatment arms; Stratification; Variance estimation (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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