期刊
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
卷 110, 期 510, 页码 669-680出版社
AMER STATISTICAL ASSOC
DOI: 10.1080/01621459.2014.922469
关键词
Conservative tests; Linear models; Pocock and Simon's marginal procedure; Power; Stratified permuted block design; Type I error
资金
- NSF [DMS-1442192, DMS-1209164, DMS-0907297]
- NSF of China [11225104]
- NSF of Zhejiang Province [R6100119]
- Fundament Research Funds for the Central University of China
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1442192] Funding Source: National Science Foundation
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据