Aydin Burak, Leite Walter L, Algina James
RTE University, Rize, Turkey.
University of Florida, Gainesville, FL, USA.
Educ Psychol Meas. 2016 Oct;76(5):803-823. doi: 10.1177/0013164415618705. Epub 2015 Nov 26.
We investigated methods of including covariates in two-level models for cluster randomized trials to increase power to detect the treatment effect. We compared multilevel models that included either an observed cluster mean or a latent cluster mean as a covariate, as well as the effect of including Level 1 deviation scores in the model. A Monte Carlo simulation study was performed manipulating effect sizes, cluster sizes, number of clusters, intraclass correlation of the outcome, patterns of missing data, and the squared correlations between Level 1 and Level 2 covariates and the outcome. We found no substantial difference between models with observed means or latent means with respect to convergence, Type I error rates, coverage, and bias. However, coverage could fall outside of acceptable limits if a latent mean is included as a covariate when cluster sizes are small. In terms of statistical power, models with observed means performed similarly to models with latent means, but better when cluster sizes were small. A demonstration is provided using data from a study of the Tools for Getting Along intervention.
我们研究了在整群随机试验的两级模型中纳入协变量的方法，以提高检测治疗效果的效能。我们比较了将观察到的聚类均值或潜在聚类均值作为协变量的多级模型，以及在模型中纳入一级偏差分数的效果。进行了一项蒙特卡罗模拟研究，对效应大小、聚类大小、聚类数量、结果的组内相关性、缺失数据模式以及一级和二级协变量与结果之间的平方相关性进行了操纵。我们发现，在收敛性、I型错误率、覆盖率和偏差方面，使用观察均值的模型和使用潜在均值的模型之间没有实质性差异。然而，当聚类规模较小时，如果将潜在均值作为协变量纳入，覆盖率可能会超出可接受的范围。在统计效能方面，使用观察均值的模型与使用潜在均值的模型表现相似，但在聚类规模较小时表现更好。我们使用“相处工具”干预研究的数据进行了演示。
