JSM 2004 - Toronto

Abstract #301599

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Activity Number: 216
Type: Topic Contributed
Date/Time: Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
Sponsor: Social Statistics Section
Abstract - #301599
Title: Jointly Modeling Latent Trajectories and a Subsequent Outcome Variable: A Bayesian Approach
Author(s): Sujata Patil*+ and Trivellore E. Raghunathan
Companies: University of Michigan and University of Michigan
Address: 2901 Baxter Rd., Ann Arbor, MI, 48109-2150,
Keywords: Bayes ; longitudinal data ; trajectory
Abstract:

We propose a general class of models that treat latent trajectory variables summarized in stage 1 of the model as predictors of a subsequent outcome in a second stage. This general class of models allows for a variety of trajectory shapes and can incorporate outcomes that come from the exponential family of distributions. We propose a fully Bayesian approach to fit this model. The Bayesian approach and two competing approaches are used to study trajectories of adolescent alcohol use as predictors of motor vehicle offenses incurred during young adulthood. Additionally, we describe results from a simulation study that show that Bayes estimates have less bias and root mean square error and have better confidence than two competing approaches.

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