Markov transition model to dementia with death as a competing event

• Published in: Computational statistics & data analysis Vol. 80; pp. 78 - 88
• Main Authors: Wei, Shaoceng, Xu, Liou, Kryscio, Richard J.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.12.2014
• Subjects: Age; alleles; Assessments; Cognition; cognitive disorders; Competing event; Cox proportional hazards model; Death; dementia; education; elderly; Impairment; longitudinal studies; Markov chain; Markov models; Multi-state Markov chain; Nun Study; Shared random effect; Survival; Weibull statistics; Weibull survival model; Weibull survival model; Multi-state Markov chain; Nun Study; Shared random effect; Cox proportional hazards model; Competing event; competing event; shared random effect; multi-state Markov chain
• ISSN: 0167-9473; 1872-7352
• DOI: 10.1016/j.csda.2014.06.014

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.