Joint Models for Bivariate Longitudinal Processes

Xuefeng Liu, PhD
* Candidate for Faculty Position in the Biostatistical Design and Analysis Center
Wayne State University

Thursday, January 17, 2008
12:00noon - 1:00pm
MoosT 2-580
Minneapolis Campus

Abstract:
Joint models for the association of a longitudinal binary and a longitudinal continuous process are proposed for situations where their association is of direct interest. The models are parameterized such that the dependence between the two processes is characterized by unconstrained regression coefficients. Bayesian variable selection techniques are used to parsimoniously model these coefficients. An MCMC sampling algorithm is developed for sampling from the posterior distribution, using data augmentation steps to handle missing data. Several technical issues are addressed to implement the MCMC algorithm efficiently. The models are motivated by, and are used for, the analysis of a smoking cessation clinical trial in which an important question of interest was the effect of the (exercise) treatment on the relationship between smoking cessation and weight gain.

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