Joint modeling of longitudinal cholesterol measurements and time to onset of dementia in an elderly African American Cohort

Shanshan Li, Mengjie Zheng, Sujuan Gao

Research output: Contribution to journalArticle

Abstract

This paper presents a statistical method for analyzing the association between longitudinal cholesterol measurements and the timing of onset of dementia. The proposed approach jointly models the longitudinal and survival processes for each individual on the basis of a shared random effect, where a linear mixed effects model is assumed for the longitudinal component and an extended Cox regression model is employed for the survival component. A dynamic prediction model is built based on the joint model, which provides prediction of the conditional survival probabilities at different time points using available longitudinal measurements as well as baseline characteristics. We apply our method to the Indianapolis-Ibadan Dementia project, a 20-year study of dementia in elderly African Americans living in Indianapolis, Indiana. We find that with baseline covariates and comorbidities adjusted, the risk of dementia decreases by 1% per one mg/dl increase in total cholesterol. Therefore we conclude that, in a healthy cohort of African Americans aged 65 years or more, high late-life cholesterol level is associated with lower incidence of dementia.

Original languageEnglish (US)
Pages (from-to)148-160
Number of pages13
JournalBiostatistics and Epidemiology
Volume1
Issue number1
DOIs
StatePublished - Jan 1 2017

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Keywords

  • Cox regression
  • dynamic prediction
  • Joint model
  • linear mixed effects model
  • longitudinal studies
  • survival analysis

ASJC Scopus subject areas

  • Epidemiology
  • Health Informatics

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