BAYESIAN INFERENCE FOR SMOKING CESSATION WITH A LATENT CURE STATE
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Abstract:
We present a fully Bayesian approach to modeling dynamic 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 transition probabilities. Inference is conducted using a Bayesian framework via Markov Chain Monte Carlo simulation. This framework provides various measures of subject-specific predictions and estimations, 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 were motivated by and applied to the Alpha-Tocopherol, Beta-Carotene (ATBC) Lung Cancer Prevention study, a large (29,133 participants)longitudinal cohort study of smokers from Finland.
Subject Area:
General Biostatistics
Suggested Citation:
Sheng Luo, Ciprian M. Crainiceanu, Thomas A. Louis, and Nilanjan Chatterjee, "BAYESIAN INFERENCE FOR SMOKING CESSATION WITH A LATENT CURE STATE" (February 2008). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 153.
http://www.bepress.com/jhubiostat/paper153