title: Joint analysis of stochastic processes with application to smoking patterns and insomnia.
abstract: 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.
authors.affiliation: Division of Biostatistics, University of Texas School of Public Health, 1200 Pressler St, Houston, Texas 77030, U.S.A.
authors.forename: Sheng
authors.initials: S
authors.lastname: Luo
mesh_terms: D001499:Bayes Theorem; D015331:Cohort Studies; D003198:Computer Simulation; D005387:Finland; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D007319:Sleep Initiation and Maintenance Disorders / Q000523:psychology*; D016540:Smoking Cessation / Q000523:psychology*; D013269:Stochastic Processes
keywords: Bayes; MCMC; cure model; joint modeling; mixed-effects model; recurrent events
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