title: Markov Transition Model to Dementia with Death as a Competing Event.
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.
authors.affiliation: Department of Statistics, University of Kentucky.
authors.forename: Shaoceng
authors.initials: S
authors.lastname: Wei
authors.affiliation: Department of Statistics, University of Kentucky.
authors.forename: Liou
authors.initials: L
authors.lastname: Xu
authors.affiliation: Department of Statistics, University of Kentucky ; Department of Biostatistics, University of Kentucky.
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Kryscio
keywords: Cox proportional hazards model; Nun Study; Weibull survival model; competing event; multi-state Markov chain; shared random effect
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