Mike Daniels
Department of Epidemiology and Biostatistics and Department of Statistics
University of Florida
Joint Models for the Association of Longitudinal Binary and Continuous Processes with Application to a Smoking Cessation Trial
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.
Joint work with Xuefeng Liu (Wayne State Univ.) and Bess Marcus (Brown Univ.)
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