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Stat Med. 2013 Dec 20;32(29):5133-44. doi: 10.1002/sim.5906. Epub 2013 Aug 2.

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

Author information

1
Division of Biostatistics, University of Texas School of Public Health, 1200 Pressler St, Houston, Texas 77030, U.S.A.

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.

KEYWORDS:

Bayes; MCMC; cure model; joint modeling; mixed-effects model; recurrent events

PMID:
23913574
PMCID:
PMC3856619
DOI:
10.1002/sim.5906
[Indexed for MEDLINE]
Free PMC Article
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