Children's Hospital Boston and Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA.
J Environ Public Health. 2011;2011:435078. doi: 10.1155/2011/435078. Epub 2011 May 14.
Linear mixed models (LMMs) are frequently used to analyze longitudinal data. Although these models can be used to evaluate mediation, they do not directly model causal pathways. Structural equation models (SEMs) are an alternative technique that allows explicit modeling of mediation. The goal of this paper is to evaluate the performance of LMMs relative to SEMs in the analysis of mediated longitudinal data with time-dependent predictors and mediators. We simulated mediated longitudinal data from an SEM and specified delayed effects of the predictor. A variety of model specifications were assessed, and the LMMs and SEMs were evaluated with respect to bias, coverage probability, power, and Type I error. Models evaluated in the simulation were also applied to data from an observational cohort of HIV-infected individuals. We found that when carefully constructed, the LMM adequately models mediated exposure effects that change over time in the presence of mediation, even when the data arise from an SEM.
线性混合模型（LMM）常用于分析纵向数据。虽然这些模型可用于评估中介效应，但它们不能直接模拟因果途径。结构方程模型（SEM）是一种替代技术，可允许对中介进行明确建模。本文的目的是评估 LMM 相对于 SEM 在分析具有时变预测因子和中介的介导纵向数据中的性能。我们从 SEM 模拟介导的纵向数据，并指定预测因子的延迟效应。评估了多种模型规格，并根据偏差、覆盖率概率、功效和 I 型错误评估了 LMM 和 SEM。还将模拟中评估的模型应用于 HIV 感染者观察队列的数据。我们发现，当精心构建时，即使数据来自 SEM，LMM 也能充分模拟随时间变化的中介暴露效应。
