文章基本信息

标题：A mixed ordinal location scale model for analysis of ecological momentary assessment (EMA) data
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作者：Hakan Demirtas ; Donald Hedeker ; Robin J. Mermelstein 等
期刊名称：Statistics and Its Interface
印刷版ISSN：1938-7989
电子版ISSN：1938-7997
出版年度：2009
卷号：2
期号：4
页码：391-401
DOI：10.4310/SII.2009.v2.n4.a1
出版社：International Press
摘要：Mixed-effects logistic regression models are described for analysis of longitudinal ordinal outcomes, where observations are observed clustered within subjects. Random effects are included in the model to account for the correlation of the clustered observations. Typically, the error variance and the variance of the random effects are 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 this article, we describe how covariates can influence these variances, and also extend the standard logistic 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 responses. Additionally, we allow the random effects to be correlated. We illustrate application of these models for ordinal data using Ecological Momentary Assessment (EMA) data, or intensive longitudinal data, from an adolescent smoking study. These mixed-effects ordinal location scale models have useful applications in mental health research where outcomes are often ordinal and there is interest in subject heterogeneity, both between- and within-subjects.
关键词：complex variation; mood variation; heterogeneity; variance modeling