文章基本信息

标题：Review of Recent Methodological Developments in Group-Randomized Trials: Part 2—Analysis
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作者：Elizabeth L. Turner ; Melanie Prague ; John A. Gallis 等
期刊名称：American journal of public health
印刷版ISSN：0090-0036
出版年度：2017
卷号：107
期号：7
页码：1078-1086
DOI：10.2105/AJPH.2017.303707
语种：English
出版社：American Public Health Association
摘要：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. In a group-randomized trial (GRT), the unit of randomization is a group, and outcome measurements are obtained for members of those groups. 1 Also called a cluster-randomized trial or community trial, 2–5 a GRT is the best comparative design available if the intervention operates at a group level, manipulates the physical or social environment, or cannot be delivered to individual members of the group without substantial risk of contamination; it is also the best available design in other circumstances such as a desire for herd immunity in studies of infectious disease. 1–5 In GRTs, outcomes for members of the same group are likely to be more similar to each other than to outcomes for members from other groups. 1 Such clustering must be accounted for in the design to avoid an underpowered study and in the analysis to avoid underestimated standard errors and inflated type I error for the intervention effect. 1–5 In analyses, regression modeling approaches are generally preferred and most commonly used because of their ease of implementation. 6 Several textbooks now address these and other issues. 1–5 In 2004, Murray et al. 7 published a review of methodological developments in both the design and analysis of GRTs. In the 13 years since, there have been many developments in each area. Here we focus on developments in analytic methods, including those relevant to our companion article that focuses on developments in GRT design. 8 (The glossary of terms is available as a supplement to the online version of this article at http://www.ajph.org .) As a pair, these articles update the 2004 review. In both, our goal is to provide a broad and comprehensive review to guide readers in seeking out appropriate materials for their own circumstances.