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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Luo, S

Published Date

  • December 20, 2013

Published In

Volume / Issue

  • 32 / 29

Start / End Page

  • 5133 - 5144

PubMed ID

  • 23913574

Pubmed Central ID

  • 23913574

Electronic International Standard Serial Number (EISSN)

  • 1097-0258

Digital Object Identifier (DOI)

  • 10.1002/sim.5906

Language

  • eng

Conference Location

  • England