Bayesian inference for smoking cessation with a latent cure state.
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.
Luo
Sheng
S
Division of Biostatistics, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas 77459, USA. sheng.t.luo@uth.tmc.edu
Crainiceanu
Ciprian M
CM
Louis
Thomas A
TA
Chatterjee
Nilanjan
N
D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic / Q000379:methods; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D018579:Patient Selection; D011930:Reaction Time; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012907:Smoking / Q000453:epidemiology*; D016540:Smoking Cessation / Q000706:statistics & numerical data*; D000074606:Smoking Prevention