TY - JOUR
T1 - Using Latent Variable Modeling for Discrete Time Survival Analysis
T2 - Examining the Links of Depression to Mortality
AU - Raykov, Tenko
AU - Zajacova, Anna
AU - Gorelick, Philip B.
AU - Marcoulides, George A.
N1 - Funding Information:
To attain the goals of this article, we make use as mentioned of data from the HRS. Information about this study, which is of relevance for this discussion, can also be found in Raykov, Gorelick, et al. (2017b). The HRS is a nationally representative complex sampling design study (e.g., Heeringa, West, & Berglund, 2017), sponsored by the National Institute on Aging (Grant Number NIA U01AG009740) and conducted by the University of Michigan. The HRS data can be found at http://hrsonline. isr.umich.edu. As mentioned in Raykov, Gorelick, et al. (2017b), the baseline interviews were carried out in 1992 with nearly 10,000 individuals born between 1931 and 1941, and the respondents were reinterviewed every 2 years thereafter. In this analysis, we use data from 12 waves conducted between 1992 and 2014, as available in a version harmonized by the RAND Corporation and published as Version P (RAND Corporation, 2016).
Publisher Copyright:
Copyright © Taylor & Francis Group, LLC.
PY - 2018/3/4
Y1 - 2018/3/4
N2 - 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.
AB - 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.
KW - body mass index
KW - depression
KW - discrete time survival analysis
KW - latent variable modeling
KW - mortality
KW - time-invariant covariate
KW - time-varying covariate
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U2 - 10.1080/10705511.2017.1364969
DO - 10.1080/10705511.2017.1364969
M3 - Article
AN - SCOPUS:85029674578
SN - 1070-5511
VL - 25
SP - 287
EP - 293
JO - Structural Equation Modeling
JF - Structural Equation Modeling
IS - 2
ER -