Bayesian Inference for Smoking Cessation with a Latent Cure State |
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Authors: | Sheng Luo Ciprian M. Crainiceanu Thomas A. Louis Nilanjan Chatterjee |
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Affiliation: | Division of Biostatistics, School of Public Health, University of Texas Health Science Center at Houston, 1200 Pressler Street, Houston, Texas 77459, U.S.A.;Department of Biostatistics, Johns Hopkins University, 615 North Wolfe Street, Baltimore, Maryland 21205, U.S.A.;Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland 20852, U.S.A. |
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Abstract: | Summary . 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. |
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Keywords: | Cure model MCMC Mixed-effects model Prediction Recurrent events Smoking cessation |
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