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Joint analysis of stochastic processes with application to smoking patterns and insomnia
Author(s) -
Luo Sheng
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5906
Subject(s) - smoking cessation , covariate , mixed model , markov chain monte carlo , random effects model , bayesian probability , econometrics , markov chain , computer science , mathematics , statistics , psychology , medicine , meta analysis , pathology
This article proposes a joint modeling framework for longitudinal insomnia measurements and a stochastic smoking cessation process in the presence of a latent permanent quitting state (i.e., ‘cure’). We use a generalized linear mixed‐effects model and a stochastic mixed‐effects model for the longitudinal measurements of insomnia symptom and for the smoking cessation process, respectively. We link these two models together via the latent random effects. We develop a Bayesian framework and Markov Chain Monte Carlo algorithm to obtain the parameter estimates. We formulate and compute the likelihood functions involving time‐dependent covariates. We explore the within‐subject correlation between insomnia and smoking processes. We apply the proposed methodology to simulation studies and the motivating dataset, that is, the Alpha‐Tocopherol, Beta‐Carotene Lung Cancer Prevention study, a large longitudinal cohort study of smokers from Finland. Copyright © 2013 John Wiley & Sons, Ltd.