Power Analyses for Moderator Effects in Three-Level Cluster Randomized Trials
Power Analyses for Moderator Effects in Three-Level Cluster Randomized Trials
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DOI：
10.1080/00220973.2017.1315714
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发表时间：
2018-01-01
影响因子：
2.2
通讯作者：
Spybrook, Jessaca
中科院分区：
文献类型：
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作者：
Dong, Nianbo;Kelcey, Benjamin;Spybrook, Jessaca
研究人员通常感兴趣的是，干预的效果是否会因个体或群体调节变量而有所不同，如儿童的特征（如性别）、教师的背景（如教学年限）和学校的特征（如城市性）；也就是说，研究人员试图研究干预对谁以及在什么情况下起作用。此外，研究人员对通过适度分析来理解和解释治疗效果的可变性感兴趣，以此作为探索治疗效果可变性来源的一种方法。本研究在设计三水平聚类随机试验（crt）时，发展了检验调节效应的功效分析公式。我们开发了统计公式，用于计算统计能力、最小可检测效应大小差异、聚类或交叉水平调节、非随机变化或随机斜率、二元或连续调节以及有或没有协变量的设计的95%置信区间。我们演示了如何使用软件PowerUp -Moderator在三电平crt的规划阶段使用这些计算。
Researchers are often interested in whether the effects of an intervention differ conditional on individual- or group-moderator variables such as children's characteristics (e.g., gender), teacher's background (e.g., years of teaching), and school's characteristics (e.g., urbanity); that is, the researchers seek to examine for whom and under what circumstances an intervention works. Furthermore, the researchers are interested in understanding and interpreting variability in treatment effects through moderation analysis as an approach to exploring the sources of the treatment effect variability. This study develops formulas for power analyses to detect the moderator effects in designing three-level cluster randomized trials (CRTs). We develop the statistical formulas for calculating statistical power, minimum detectable effect size difference, and 95% confidence intervals for cluster or cross-level moderation, nonrandomly varying or random slopes, binary or continuous moderators, and designs with or without covariates. We demonstrate how the calculations can be used in the planning phase of three-level CRTs using the software PowerUp!-Moderator.