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群组随机试验近期方法学进展综述:第2部分——分析

Review of Recent Methodological Developments in Group-Randomized Trials: Part 2-Analysis.

作者信息

Turner Elizabeth L, Prague Melanie, Gallis John A, Li Fan, Murray David M

机构信息

Elizabeth L. Turner and John A. Gallis are with the Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, and the Duke Global Health Institute, Duke University. Melanie Prague is with the Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, and Inria, project team SISTM, Bordeaux, France. Fan Li is with the Department of Biostatistics and Bioinformatics, Duke University. David M. Murray is with the Office of Disease Prevention, Division of Program Coordination and Strategic Planning, and the Office of the Director, National Institutes of Health, Rockville, MD.

出版信息

Am J Public Health. 2017 Jul;107(7):1078-1086. doi: 10.2105/AJPH.2017.303707. Epub 2017 May 18.

Abstract

In 2004, Murray et al. reviewed methodological developments in the design and analysis of group-randomized trials (GRTs). We have updated that review with developments in analysis of the past 13 years, with a companion article to focus on developments in design. We discuss developments in the topics of the earlier review (e.g., methods for parallel-arm GRTs, individually randomized group-treatment trials, and missing data) and in new topics, including methods to account for multiple-level clustering and alternative estimation methods (e.g., augmented generalized estimating equations, targeted maximum likelihood, and quadratic inference functions). In addition, we describe developments in analysis of alternative group designs (including stepped-wedge GRTs, network-randomized trials, and pseudocluster randomized trials), which require clustering to be accounted for in their design and analysis.

摘要

2004年,默里等人回顾了群组随机试验(GRTs)设计与分析中的方法学进展。我们结合过去13年分析方法的发展对该综述进行了更新,并配有一篇姊妹文章聚焦设计方面的进展。我们讨论了早期综述主题(如平行组GRTs方法、个体随机分组治疗试验和缺失数据)的进展以及新主题,包括考虑多级聚类的方法和替代估计方法(如增强广义估计方程、靶向最大似然法和二次推断函数)。此外,我们描述了替代群组设计(包括阶梯楔形GRTs、网络随机试验和伪聚类随机试验)分析方面的进展,这些设计在其设计和分析中需要考虑聚类因素。

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