Markov transition model to dementia with death as a competing event

Shaoceng Wei, Liou Xu, Richard J. Kryscio

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

This study evaluates the effect of death as a competing event to the development of dementia in a longitudinal study of the cognitive status of elderly subjects. A multi-state Markov model with three transient states: intact cognition, mild cognitive impairment (M.C.I.) and global impairment (G.I.) and one absorbing state: dementia is used to model the cognitive panel data; transitions among states depend on four covariates age, education, prior state (intact cognition, or M.C.I., or G.I.) and the presence/absence of an apolipoprotein E-4 allele (APOE4). A Weibull model and a Cox proportional hazards (Cox PH) model are used to fit the survival from death based on age at entry and the APOE4 status. A shared random effect correlates this survival time with the transition model. Simulation studies determine the sensitivity of the maximum likelihood estimates to the violations of the Weibull and Cox PH model assumptions. Results are illustrated with an application to the Nun Study, a longitudinal cohort of 672 participants 75+ years of age at baseline and followed longitudinally with up to ten cognitive assessments per nun.

Original languageEnglish
Pages (from-to)78-88
Number of pages11
JournalComputational Statistics and Data Analysis
Volume80
DOIs
StatePublished - Dec 2014

Bibliographical note

Funding Information:
This research was partially funded with support from the following grant to the University of Kentucky’s Center of Aging: R01 AG038651-01A1 from National Institute on Aging , as well as a grant to the University of Kentucky’s Center for Clinical and Translation Science: UL1TR000117 from the National Center for Advancing Translational Sciences .

Keywords

  • Competing event
  • Cox proportional hazards model
  • Multi-state Markov chain
  • Nun Study
  • Shared random effect
  • Weibull survival model

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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