Multivariate joint modeling of mean and variation and time-lagged intensive longitudinal methods to assess associations between marijuana use and craving variation. (307906)*Maryam Skafyan, University of Northern Colorado
Keywords: EMA, Bivariate Mixed Model, Intensive Longitudinal Data, Joint Modeling of Mean and Variance.
Marijuana is the most commonly used illicit substance in the U.S. and Ecological momentary assessments (EMAs) methods are useful for understanding both between- and within-subject dynamic changes in marijuana use. However, many traditional longitudinal models treat within-subject variability as nuisance factor. Here, we present a combination of multivariate longitudinal modeling, joint modeling of mean and variation, and time-lagged intensive longitudinal methods to assess associations between marijuana use and craving variation. A novel joint mixed bivariate model will be presented, using both the outcome of interest and a predictor as dependent variables, and including as independent variables measures of variation from the dispersion model. Analysis will be applied for an analysis of feedback-loop associations between marijuana use and craving from an Ecological Momentary Assessment study. We will show that both marijuana use, and craving are associated with each other at subsequent times of observation.