Joint analysis of stochastic processes with application to smoking patterns and insomnia

• Published in: Statistics in medicine Vol. 32; no. 29; pp. 5133 - 5144
• Main Author: Luo, Sheng
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 20.12.2013; Wiley Subscription Services, Inc
• Subjects: Bayes; Bayes Theorem; Bayesian analysis; Cohort Studies; Computer Simulation; cure model; Finland; Humans; Insomnia; joint modeling; Longitudinal Studies; Markov analysis; MCMC; mixed-effects model; Models, Statistical; Monte Carlo simulation; Parameter estimation; recurrent events; Sleep Initiation and Maintenance Disorders - psychology; Smoking cessation; Smoking Cessation - psychology; Stochastic Processes; Finland; MCMC; cure model; joint modeling; recurrent events; mixed-effects model; Bayes
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.5906

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.