We present a Bayesian approach to modeling dynamic smoking addiction behavior processes when cure is not directly observed due to censoring. Subject-speci c probabilities model the stochastic transitions among three behavioral states: smoking, transient quitting, and permanent quitting (absorbent state). A multivariate normal distribution for random e ects is used to account for the potential correlation among the subject-speci c transition probabilities. Inference is conducted using a Bayesian framework via Markov Chain Monte Carlo simulation. This framework provides various measures of subject-speci c predictions, which are useful for policy making, intervention development, and evaluation. Simulations are used to validate our Bayesian methodology, and assess its frequentist properties. Our methods are motivated by, and applied to the Alpha-Tocopherol, Beta-Carotene (ATBC) Lung Cancer Prevention study, a large (29; 133 individuals) longitudinal cohortstudy of smokers from Finland.



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