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Bayesian Inference for Smoking Cessation with a Latent Cure State

Luo, Sheng, Crainiceanu, Ciprian M., Louis, Thomas A., Chatterjee, Nilanjan
Biometrics 2009 v.65 no.3 pp. 970-978
Bayesian theory, Markov chain, Monte Carlo method, alpha-tocopherol, beta-carotene, biometry, cohort studies, lung neoplasms, models, prediction, Finland
We present a Bayesian approach to modeling dynamic smoking addiction behavior processes when cure is not directly observed due to censoring. Subject‐specific probabilities model the stochastic transitions among three behavioral states: smoking, transient quitting, and permanent quitting (absorbent state). A multivariate normal distribution for random effects is used to account for the potential correlation among the subject‐specific transition probabilities. Inference is conducted using a Bayesian framework via Markov chain Monte Carlo simulation. This framework provides various measures of subject‐specific 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 Lung Cancer Prevention study, a large (29,133 individuals) longitudinal cohort study of smokers from Finland.