Biostatistics short course: Variance Modeling of Ecological Momentary Assessment (EMA) and Mobile Health Data

This workshop, led by Donald Hedeker, PhD, professor of biostatistics at the University of Chicago, will focus on an adolescent smoking study using EMA at both one and several measurement waves, where interest is on characterizing changes in mood variation associated with smoking. Computer application using SAS NLMIXED and the freeware MIXREGLS program will be described and illustrated. This is an intermediate/advanced level course. Attendees should have a basic understanding of multilevel models. Use of MIXwild software program will be demonstrated.

Registration required.

Abstract
For longitudinal data, mixed models include random subject effects to indicate how subjects influence their responses over the 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 (error variance) and between-subjects (random-effects variance) variation in the data. In studies using Mobile Health measurement modalities like Ecological Momentary Assessment (EMA), up to thirty or forty observations are often obtained for each subject, and interest frequently centers around changes in the variances, both within- and between-subjects. Also, such EMA studies often include several waves of data collection. In this workshop, we focus on an adolescent smoking study using EMA at both one and several measurement waves, where interest is on characterizing changes in mood variation associated with smoking. We describe how covariates can influence the mood variances, and also describe an extension of 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 (MELS) models have useful applications in many research areas where interest centers on the joint modeling of the mean and variance structure. Computer application using SAS NLMIXED and the freeware MIXREGLS program will be described and illustrated.

Prerequisites
This is an intermediate/advanced level course, so attendees should have a basic understanding of multilevel models. Use of the MIXwild software program will be demonstrated. The software can be freely downloaded. https://voices.uchicago.edu/hedeker/mixwild_mixregls/

Donald Hedeker, PhD
Donald Hedeker is professor of biostatistics in the department of public health sciences of the University of Chicago. His research has focused on statistical methods for intensive longitudinal data, which come from ecological momentary assessment (EMA), accelerometers, and mobile health recording. In particular, he has developed the mixed-effects location scale (MELS) model which allows modeling of the mean, as well as the between- and within-subject variances of intensive longitudinal data.