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

Authors

  • Sheng Luo

    Corresponding author
    1. Division of Biostatistics, University of Texas School of Public Health, Houston, Texas 77030, U.S.A.
    • Correspondence to: Sheng Luo, Division of Biostatistics University of Texas School of Public Health 1200 Pressler St Houston Texas 77030 U.S.A.

      E-mail: sheng.t.luo@uth.tmc.edu

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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. Copyright © 2013 John Wiley & Sons, Ltd.

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