Abstract #300194

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JSM 2003 Abstract #300194
Activity Number: 94
Type: Contributed
Date/Time: Monday, August 4, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #300194
Title: Marginalized Transition Random Effects Models for Multivariate Longitudinal Binary Data
Author(s): Ozlem Ilk*+ and Michael J. Daniels
Companies: Iowa State University and University of Florida
Address: 2304 Knapp St. No:4, Ames, IA, 50014-7590,
Keywords: Bayesian hierarchical model ; hybrid MC
Abstract:

Generalized linear models with random effects and/or serial dependence are commonly used to analyze longitudinal data. However, determination and computation of marginal covariate effects can be difficult. Heagerty has proposed marginally specified logistic-normal models (1999) and marginalized transition models (2002) for longitudinal binary and categorical data in which the marginal mean is modeled explicitly in the presence of random effects and serial dependence, respectively. We extend his work to handle multivariate longitudinal binary response data by proposing a framework consisting of a triple of regression models, which permits subject-specific inferences. While modeling the marginal mean response taking into account dependence across time via a transition structure and across responses within a subject for a given time via random effects. Markov chain Monte Carlo methods, specifically Gibbs sampling with Hybrid and Metropolis Hastings steps, are used to sample from the posterior distribution of parameters. Complete data methods are illustrated on a real life dataset.

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