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.
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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