Bayesian inference for smoking cessation with a latent cure state.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Luo, S; Crainiceanu, CM; Louis, TA; Chatterjee, N

Published Date

  • September 2009

Published In

Volume / Issue

  • 65 / 3

Start / End Page

  • 970 - 978

PubMed ID

  • 19173701

Pubmed Central ID

  • 19173701

Electronic International Standard Serial Number (EISSN)

  • 1541-0420

Digital Object Identifier (DOI)

  • 10.1111/j.1541-0420.2008.01167.x

Language

  • eng

Conference Location

  • United States