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1.  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 in which 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. A Markov chain Monte Carlo (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.
doi:10.1198/016214508000000904
PMCID: PMC2746699  PMID: 20161053
Calibrated posterior predictive p-value; Data augmentation; Dependence; Joint models; Markov chain Monte Carlo; Parameter expansion; Stochastic search variable selection

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