Power Analyses for Moderator Effects With (Non)Randomly Varying Slopes in Cluster Randomized Trials
Power Analyses for Moderator Effects With (Non)Randomly Varying Slopes in Cluster Randomized Trials
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DOI：
10.5964/meth.4003
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发表时间：
2021-06-01
影响因子：
3.1
通讯作者：
Bulus, Metin
中科院分区：
文献类型：
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作者：
Dong, Nianbo;Spybrook, Jessaca;Bulus, Metin
研究人员经常应用适度分析来检查干预的效果是否因个体或集群调节变量（如性别，预测试或学校规模）而有所不同。本研究开发了使用分层线性模型的两水平整群随机试验（CRT）中检验调节效应的功效分析公式。我们推导出估计统计功效、最小可检测效应大小差异和聚类和个体水平调节因子的95%置信区间的公式。我们的框架可以容纳二进制或连续的主持人，设计或不协变量，和个人水平的主持人，随机或非随机变化的集群的影响。一个小的Monte Carlo模拟证实了我们的公式的准确性。我们还比较了主效应分析和适度分析之间的功率，讨论了主持人斜率（随机与非随机变化）的错误指定的影响，并得出结论，为今后的研究方向。我们提供的软件进行功率分析在CRT的减速器效应。
Researchers often apply moderation analyses to examine whether the effects of an intervention differ conditional on individual or cluster moderator variables such as gender, pretest, or school size. This study develops formulas for power analyses to detect moderator effects in two-level cluster randomized trials (CRTs) using hierarchical linear models. We derive the formulas for estimating statistical power, minimum detectable effect size difference and 95% confidence intervals for cluster- and individual-level moderators. Our framework accommodates binary or continuous moderators, designs with or without covariates, and effects of individual-level moderators that vary randomly or nonrandomly across clusters. A small Monte Carlo simulation confirms the accuracy of our formulas. We also compare power between main effect analysis and moderation analysis, discuss the effects of mis-specification of the moderator slope (randomly vs. non-randomly varying), and conclude with directions for future research. We provide software for conducting a power analysis of moderator effects in CRTs.