On the Potential of Discrete Time Survival Analysis Using Latent Variable Modeling: An Application to the Study of the Vascular Depression Hypothesis. Issue 6 (2nd November 2017)

• Record Type: Journal Article
• Authors: Raykov, Tenko; Gorelick, Philip B.; Zajacova, Anna; Marcoulides, George A.
• Is Part Of: Structural equation modeling. Volume 24:Issue 6(2017)
• Page Start: 926
• Page End: 935
• Publication Date: 2017-11-02
• Subjects: depression -- discrete time survival analysis -- latent variable modeling -- multiple testing -- unique predictive power -- vascular depression hypothesis; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2017.1315305
• Languages: English
• ISSNs: 1070-5511

Abstract

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.