On the Potential of Discrete Time Survival Analysis Using Latent Variable Modeling: An Application to the Study of the Vascular Depression Hypothesis

Tenko Raykov*, Philip B. Gorelick, Anna Zajacova, George A. Marcoulides

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Analysis and modeling of time to event data have been traditionally associated with nonparametric, semiparametric, or parametric statistical frameworks. Recent advances in latent variable modeling have additionally provided unique analytic opportunities to methodologists and substantive researchers interested in survival time modeling. As a consequence, discrete time survival analyses can now be readily carried out using latent variable modeling, an approach that offers substantively important extensions to conventional survival models. Using data from the Health and Retirement Study, the discussed approach is applied to the study of the increasingly prominent vascular depression hypothesis in gerontology, geriatrics, and aging research, allowing examination of the unique predictive power of depression with respect to time to stroke in middle-aged and older adults.

Original languageEnglish (US)
Pages (from-to)926-935
Number of pages10
JournalStructural Equation Modeling
Volume24
Issue number6
DOIs
StatePublished - Nov 2 2017

Keywords

  • depression
  • discrete time survival analysis
  • latent variable modeling
  • multiple testing
  • unique predictive power
  • vascular depression hypothesis

ASJC Scopus subject areas

  • General Decision Sciences
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

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