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首页> 美国卫生研究院文献>Applied Psychological Measurement >Detecting Intervention Effects in a Cluster-Randomized Design UsingMultilevel Structural Equation Modeling for Binary Responses
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Detecting Intervention Effects in a Cluster-Randomized Design UsingMultilevel Structural Equation Modeling for Binary Responses

机译：使用以下方法在集群随机化设计中检测干预效果二元响应的多级结构方程建模

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摘要

Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test–post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predict future attributes. The pre-test and post-test scores that are most often used in MLM are summed item responses (or total scores). In prior research, there have been concerns regarding measurement error in the use of total scores in using MLM. To correct for measurement error in the covariate and outcome, a theoretical justification for the use of multilevel structural equation modeling (MSEM) has been established. However, MSEM for binary responses has not been widely applied to detect intervention effects (group differences) in intervention studies. In this article, the use of MSEM for intervention studies is demonstrated and the performance of MSEM is evaluated via a simulation study. Furthermore, the consequences of using MLM instead of MSEM are shown in detecting group differences. Results of the simulation study showed that MSEM performed adequately as the number of clusters, cluster size, and intraclass correlation increased and outperformed MLM for the detection of group differences.
机译：多级建模（MLM）通常用于检测组差异，例如在测试前-测试后的集群随机设计中的干预效果。通过控制测试前分数作为预测未来属性的未观察因素的代理变量，可以检测出测试后分数的组差异。在传销中最常使用的测验前和测验分数是项目反应的总和（或总分数）。在先前的研究中，在使用传销中使用总分时存在关于测量误差的担忧。为了校正协变量和结果中的测量误差，已经建立了使用多层结构方程模型（MSEM）的理论依据。但是，用于二元反应的MSEM尚未广泛应用于检测干预研究中的干预效果（组差异）。在本文中，演示了MSEM在干预研究中的使用，并通过模拟研究评估了MSEM的性能。此外，在检测组差异中显示了使用MLM代替MSEM的后果。模拟研究的结果表明，随着聚类数量，聚类大小和类内相关性的增加，MSEM表现良好，并且在检测群体差异方面表现优于MLM。

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