Using Latent Variable Modeling for Discrete Time Survival Analysis: Examining the Links of Depression to Mortality

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Using a latent variable modeling approach to discrete time survival analysis, the dynamics of the relationships of depression and body mass index to mortality are examined with data from the multiwave, nationally representative Health and Retirement Study. A set of medical and demographic variables are employed as time-invariant covariates along with lag-1 depression scores and body mass indexes as time-varying covariates for mortality within an up to 2-year follow-up interval. The results indicate marked links of immediately prior depression levels, as well as notable relations of the body mass indexes, to within-wave mortality in middle-aged and older adults. The approach highlights the benefits of using latent variable modeling for survival analysis, and its findings represent potentially important relationships of clinical and theoretical relevance.

Original languageEnglish (US)
Pages (from-to)287-293
Number of pages7
JournalStructural Equation Modeling
Volume25
Issue number2
DOIs
StatePublished - Mar 4 2018

Keywords

  • body mass index
  • depression
  • discrete time survival analysis
  • latent variable modeling
  • mortality
  • time-invariant covariate
  • time-varying covariate

ASJC Scopus subject areas

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

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