A Mixed Effects Location Scale Model for Ordinal Data

• Activity Number: 635
• Type: Invited
• Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
• Sponsor: WNAR
• Author(s): Donald Hedeker*+ and Hakan Demirtas and Robin J. Mermelstein
• Companies: University of Illinois at Chicago and University of Illinois at Chicago and University of Illinois at Chicago
• Keywords: complex variation ; heterogeneity ; variance modeling ; random scale

Abstract

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 (ie error variance) and between-subjects (ie random-effects variance) variation in the data. In this presentation, 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.