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Markov transition model to dementia with death as a competing event

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  • Wei, Shaoceng
  • Xu, Liou
  • Kryscio, Richard J.

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.

Suggested Citation

  • Wei, Shaoceng & Xu, Liou & Kryscio, Richard J., 2014. "Markov transition model to dementia with death as a competing event," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 78-88.
    • Handle: RePEc:eee:csdana:v:80:y:2014:i:c:p:78-88
    DOI: 10.1016/j.csda.2014.06.014
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    References listed on IDEAS

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    1. Paul S. Albert & Dean A. Follmann, 2003. "A Random Effects Transition Model For Longitudinal Binary Data With Informative Missingness," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(1), pages 100-111, February.
    2. Yu, Lei & Tyas, Suzanne L. & Snowdon, David A. & Kryscio, Richard J., 2009. "Effects of ignoring baseline on modeling transitions from intact cognition to dementia," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3334-3343, July.
    3. Jane Xu & Scott L. Zeger, 2001. "Joint analysis of longitudinal data comprising repeated measures and times to events," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 375-387.
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    1. Daewoo Pak & Jing Ning & Richard J. Kryscio & Yu Shen, 2023. "Evaluation of the natural history of disease by combining incident and prevalent cohorts: application to the Nun Study," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 752-768, October.

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