Cancer Biostatistics Monthly Seminar

Examining Smoking and Mood Regulation using Ecological Momentary Assessment (EMA) data and the Mixed-Effects Location Scale (MELS) Model

Donald Hedeker, PhD

Professor,

Department of Public Health Sciences, University of Chicago

Donald Hedeker’s chief expertise is in the development and use of advanced statistical methods for clustered and longitudinal data, with particular emphasis on mixed-effects models. He is the primary author of several freeware computer programs for mixed-effects analysis. With Robert Gibbons, Don is the author of the text “Longitudinal Data Analysis,” published by Wiley in 2006. More recently, he has developed methods and software for the analysis of intensive longitudinal data, which are data with many measurements over time, often collected using mobile devices and/or the internet. Such data are increasingly obtained by researchers in many research areas, for example in the areas of mobile health (mHealth) and ecological momentary assessment (EMA) studies. He is an associate editor for Statistics in Medicine.

Abstract

Intensive longitudinal data are increasingly encountered in many research areas. For example, ecological momentary assessment (EMA) is often used to study subjective experiences within changing environmental contexts. In these studies, up to 30 or 40 observations are usually obtained for each subject over a period of a week or so. Because there are so many measurements per subject, one can characterize a subject’s mean and variance and can specify models for both. In this presentation, we focus on an adolescent smoking study using EMA where interest is on characterizing how smoking level is related to mood variation. Specifically, is there increased mood regulation with increased levels of smoking, an idea that has often been postulated in the smoking literature.  We describe how covariates can influence the mood variances, which allows us to test this theory. The statistical model is further extended 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 scale, of their mood responses. 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.

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The meeting agenda includes a presentation, programmatic announcements, and updates from Cancer Center program leaders.