Prof. Mike Daniels, University of Florida
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 with Xuefeng Liu and Bess Marcus
