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用于平行和阶梯楔形整群随机试验分析的置换检验

The use of permutation tests for the analysis of parallel and stepped-wedge cluster-randomized trials.

作者信息

Wang Rui, De Gruttola Victor

机构信息

Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, 02215, MA, U.S.A.

Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, 02115, MA, U.S.A.

出版信息

Stat Med. 2017 Aug 15;36(18):2831-2843. doi: 10.1002/sim.7329. Epub 2017 May 2.

DOI:10.1002/sim.7329
PMID:28464567
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5507602/
Abstract

We investigate the use of permutation tests for the analysis of parallel and stepped-wedge cluster-randomized trials. Permutation tests for parallel designs with exponential family endpoints have been extensively studied. The optimal permutation tests developed for exponential family alternatives require information on intraclass correlation, a quantity not yet defined for time-to-event endpoints. Therefore, it is unclear how efficient permutation tests can be constructed for cluster-randomized trials with such endpoints. We consider a class of test statistics formed by a weighted average of pair-specific treatment effect estimates and offer practical guidance on the choice of weights to improve efficiency. We apply the permutation tests to a cluster-randomized trial evaluating the effect of an intervention to reduce the incidence of hospital-acquired infection. In some settings, outcomes from different clusters may be correlated, and we evaluate the validity and efficiency of permutation test in such settings. Lastly, we propose a permutation test for stepped-wedge designs and compare its performance with mixed-effect modeling and illustrate its superiority when sample sizes are small, the underlying distribution is skewed, or there is correlation across clusters. Copyright © 2017 John Wiley & Sons, Ltd.

摘要

我们研究了排列检验在平行和阶梯楔形整群随机试验分析中的应用。针对具有指数族端点的平行设计的排列检验已得到广泛研究。为指数族备择假设开发的最优排列检验需要组内相关信息,而对于事件发生时间端点,该量尚未定义。因此,尚不清楚如何为具有此类端点的整群随机试验构建高效的排列检验。我们考虑一类由特定对治疗效果估计值的加权平均值构成的检验统计量,并就权重选择提供实用指导以提高效率。我们将排列检验应用于一项评估降低医院获得性感染发生率干预效果的整群随机试验。在某些情况下,不同整群的结果可能相关,我们评估排列检验在此类情况下的有效性和效率。最后,我们提出一种针对阶梯楔形设计的排列检验,并将其性能与混合效应建模进行比较,说明在样本量小、基础分布偏态或整群间存在相关性时它的优越性。版权所有© 2017约翰威立父子有限公司。

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