Testing Hypotheses of Covariate-Adaptive Randomized Clinical Trials

Abstract

Covariate-adaptive designs are often implemented to balance important covariates in clinical trials. However, the theoretical properties of conventional testing hypotheses are usually unknown under covariate-adaptive randomized clinical trials. In the literature, most studies are based on simulations. In this article, we provide theoretical foundation of hypothesis testing under covariate-adaptive designs based on linear models. We derive the asymptotic distributions of the test statistics of testing both treatment effects and the significance of covariates under null and alternative hypotheses. Under a large class of covariate-adaptive designs, (i) the hypothesis testing to compare treatment effects is usually conservative in terms of small Type I error; (ii) the hypothesis testing to compare treatment effects is usually more powerful than complete randomization; and (iii) the hypothesis testing for significance of covariates is still valid. The class includes most of the covariate-adaptive designs in the literature; for example, Pocock and Simon’s marginal procedure, stratified permuted block design, etc. Numerical studies are also performed to assess their corresponding finite sample properties. Supplementary material for this article is available online.

• Conservative tests
• Linear models
• Pocock and Simon’s marginal procedure
• Power
• Stratified permuted block design
• Type I error

Additional information

Notes on contributors

Wei Ma

Wei Ma is PhD student, Department of Statistics, University of Virginia, Charlottesville, VA 22904-4135 (E-mail: [email protected]).

Feifang Hu

Feifang Hu is Professor, Department of Statistics, George Washington University, Washington, DC 20052 (E-mail: [email protected]).

Lixin Zhang

Lixin Zhang is Professor, Department of Mathematics, Zhejiang University, Hangzhou, P.R. China 310006 (E-mail: [email protected]).