Analysis of multiple-period group randomized trials: random coefficients model or repeated measures ANOVA?
Analysis of multiple-period group randomized trials: random coefficients model or repeated measures ANOVA?
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DOI：
10.1186/s13063-022-06917-2
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发表时间：
2022-12-07
期刊：
影响因子：
2.5
通讯作者：
Murray, David M. M.
中科院分区：
文献类型：
--
作者：
Moyer, Jonathan C. C.;Heagerty, Patrick J. J.;Murray, David M. M.
关键词：
用线性混合模型分析的多阶段平行组随机试验（GRT）可以将平均模型中的时间表示为连续或分类。如果时间是连续的，则随机效应通常是与条件特定斜率和截距的组和成员水平偏差，称为随机系数（RC）分析模型。如果时间是分类的，则随机效应传统上是与特定时间条件均值的组和成员水平偏差，称为重复测量ANOVA（RM-ANOVA）分析模型。长期指导建议使用RC优于RM-ANOVA用于两个以上周期的平行GRT，因为RC在两个时间参数化中均表现出标称I型错误率，而RM-ANOVA在应用于使用RC模型生成的数据时表现出膨胀的I型错误率。然而，该建议的制定假设了RM-ANOVA的方差分量协方差矩阵，仅使用横截面数据，并明确建模时间×组变异。如果在队列数据中观察到类似模式，则未回答的是具有非结构化协方差的RM-ANOVA对根据RC机制生成的数据的表现如何，以及如果数据生成模型中存在此类变异，则未建模时间×组变异的影响。根据RM-ANOVA和RC机制，在总共五个时间段模拟队列和横断面平行GRT数据的连续结局。所有模拟均假设时间×组变化。我们改变了组的数量，组的大小和集群内的相关性。将使用RC、RM-ANOVA、具有非结构化协方差的RM-ANOVA和饱和随机效应结构的分析模型应用于数据。所有分析模型均指定时间×组随机效应。然后重新应用分析模型，未指定时间×组的随机效应。结果表明，RC和饱和分析模型在所有数据集中保持了名义I型错误率，当应用于队列RC数据时，具有非结构化协方差的RM-ANOVA并没有避免I型错误率膨胀，和忽略时间的分析模型当数据中存在这种变化时，变化的组随机效应倾向于大量的I型误差膨胀，除非残差方差相对于时间×组方差对于多周期并联GRT，推荐采用时间×群RC和饱和解析模型。在线版本包含补充材料，可通过10.1186/s13063-022-06917-2获得。
Multiple-period parallel group randomized trials (GRTs) analyzed with linear mixed models can represent time in mean models as continuous or categorical. If time is continuous, random effects are traditionally group- and member-level deviations from condition-specific slopes and intercepts and are referred to as random coefficients (RC) analytic models. If time is categorical, random effects are traditionally group- and member-level deviations from time-specific condition means and are referred to as repeated measures ANOVA (RM-ANOVA) analytic models. Longstanding guidance recommends the use of RC over RM-ANOVA for parallel GRTs with more than two periods because RC exhibited nominal type I error rates for both time parameterizations while RM-ANOVA exhibited inflated type I error rates when applied to data generated using the RC model. However, this recommendation was developed assuming a variance components covariance matrix for the RM-ANOVA, using only cross-sectional data, and explicitly modeling time × group variation. Left unanswered were how well RM-ANOVA with an unstructured covariance would perform on data generated according to the RC mechanism, if similar patterns would be observed in cohort data, and the impact of not modeling time × group variation if such variation was present in the data-generating model. Continuous outcomes for cohort and cross-sectional parallel GRT data were simulated according to RM-ANOVA and RC mechanisms at five total time periods. All simulations assumed time × group variation. We varied the number of groups, group size, and intra-cluster correlation. Analytic models using RC, RM-ANOVA, RM-ANOVA with unstructured covariance, and a Saturated random effects structure were applied to the data. All analytic models specified time × group random effects. The analytic models were then reapplied without specifying random effects for time × group. Results indicated the RC and saturated analytic models maintained the nominal type I error rate in all data sets, RM-ANOVA with an unstructured covariance did not avoid type I error rate inflation when applied to cohort RC data, and analytic models omitting time-varying group random effects when such variation exists in the data were prone to substantial type I error inflation unless the residual error variance is high relative to the time × group variance. The time × group RC and saturated analytic models are recommended as the default for multiple period parallel GRTs. The online version contains supplementary material available at 10.1186/s13063-022-06917-2.
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