An application of a mixed-effects location scale model for analysis of ecological momentary assessment (EMA) data
An application of a mixed-effects location scale model for analysis of ecological momentary assessment (EMA) data
复制标题
DOI：
10.1111/j.1541-0420.2007.00924.x
复制
发表时间：
2008-06-01
期刊：
影响因子：
1.9
通讯作者：
Demirtas, Hakan
中科院分区：
文献类型：
--
作者：
Hedeker, Donald;Mermelstein, Robin J.;Demirtas, Hakan
对于纵向数据，混合模型包括随机受试者效应，以表明受试者如何影响其在重复评估中的反应。误差方差和随机效应的方差通常被认为是齐次的。这些方差项表征受试者内（即，误差方差）和受试者之间（即，随机效应方差）的变化。在使用生态瞬时评估（EMA）的研究中，每个受试者通常获得多达30或40个观察结果，并且兴趣经常集中在受试者内和受试者之间的方差变化上。在这篇文章中，我们专注于青少年吸烟研究使用EMA的兴趣是在情绪变化的特征变化。我们描述了协变量如何影响情绪方差，并通过向受试者内方差规范添加受试者水平的随机效应来扩展标准混合模型。这允许受试者对他们的情绪反应的平均值或位置和可变性或（平方）规模产生影响。此外，我们允许位置和尺度随机效应相关。这些混合效应的位置尺度模型在许多研究领域中有有用的应用，在这些研究领域中，人们的兴趣集中在均值和方差结构的联合建模上。
For longitudinal data, mixed models include random subject effects to indicate how subjects influence their responses over repeated assessments. The error variance and the variance of the random effects are usually considered to be homogeneous. These variance terms characterize the within-subjects (i.e., error variance) and between-subjects (i.e., random-effects variance) variation in the data. In studies using ecological momentary assessment (EMA), up to 30 or 40 observations are often obtained for each subject, and interest frequently centers around changes in the variances, both within and between subjects. In this article, we focus on an adolescent smoking study using EMA where interest is on characterizing changes in mood variation. We describe how covariates can influence the mood variances, and also extend the standard mixed model by adding a subject-level random effect to the within-subject variance specification. This permits subjects to have influence on the mean, or location, and variability, or (square of the) scale, of their mood responses. Additionally, we allow the location and scale random effects to be correlated. These mixed-effects location scale models have useful applications in many research areas where interest centers on the joint modeling of the mean and variance structure.