JSM 2004 - Toronto

Abstract #301146

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Activity Number: 15
Type: Topic Contributed
Date/Time: Sunday, August 8, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #301146
Title: Modeling Semicontinuous Longitudinal Data
Author(s): Warren S. Comulada*+ and Robert E. Weiss
Companies: University of California, Los Angeles and University of California, Los Angeles
Address: School of Public Health, Los Angeles, CA, 90095-1772,
Keywords: semicontinuous ; longitudinal
Abstract:

Semicontinuous variables are composed of zero and positive responses. One method to model longitudinal semicontinuous outcome variables has a random effects logistic model for whether a response is positive or zero and a linear random effects model for the positive responses; two correlated random effects are included. We propose a set of models fit using Bayesian methods. One model shares but a single random effect between the models. The other model shares a linear predictor between models and includes two correlated random effects. We discuss computational issues in fitting these models and propose a method to compare models. To illustrate, we apply these models to data from the CLEAR study, a behavorial intervention study of 172 HIV-postive young people. We investigate predictors of recent substance use over time.

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