JSM 2005 Online Program

Abstract #302988

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Activity Number:	56
Type:	Topic Contributed
Date/Time:	Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #302988
Title:	Joint Models for Longitudinal Binary and Continuous Processes
Author(s):	Xuefeng Liu*+ and Michael Daniels
Companies:	University of Florida and University of Florida
Address:	305 Diamond Village, Gainesville, FL, 32603, United States
Keywords:	Joint Models ; Gibbs Sampler ; Hierarchical Prior ; Variable Selection ; MCMC Algorithm ; Association
Abstract:	A joint model for the association of longitudinal binary and continuous processes is proposed. The model is used for the analysis of the experiment in which moderate-intensity exercise was used as an adjunct to smoking cessation and weight gain and smoking quit status were measured repeatedly on subjects through eight weeks to assess the interrelation of them across treatments. The main question of interest is the effect of the treatment on the relationship between smoking cessation and weight gain. The model is reparameterized such that the dependence can be characterized by the unconstrained regression coefficients. Bayesian variable selection techniques are used to parsimoniously model these coefficients for each treatment. An MCMC algorithm is developed for estimating the parameters by implementing the data augmentation step and the posterior sampling step.

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