Markov transition model to dementia with death as a competing event

Shaoceng Wei; Liou Xu; Richard J. Kryscio

doi:10.1016/j.csda.2014.06.014

Markov transition model to dementia with death as a competing event

Shaoceng Wei, Liou Xu, Richard J. Kryscio

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8 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 language	English
Pages (from-to)	78-88
Number of pages	11
Journal	Computational Statistics and Data Analysis
Volume	80
DOIs	https://doi.org/10.1016/j.csda.2014.06.014
State	Published - 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 .

Funding

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 .

Funders	Funder number
University of Kentucky’s Center for Clinical and Translation Science	UL1TR000117
University of Kentucky’s Sanders-Brown Center on Aging	R01 AG038651-01A1
National Institute on Aging
National Center for Advancing Translational Sciences (NCATS)

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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10.1016/j.csda.2014.06.014

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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.",

keywords = "Competing event, Cox proportional hazards model, Multi-state Markov chain, Nun Study, Shared random effect, Weibull survival model",

author = "Shaoceng Wei and Liou Xu and Kryscio, {Richard J.}",

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

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doi = "10.1016/j.csda.2014.06.014",

language = "English",

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T1 - Markov transition model to dementia with death as a competing event

AU - Wei, Shaoceng

AU - Xu, Liou

AU - Kryscio, Richard J.

N1 - 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 .

PY - 2014/12

Y1 - 2014/12

N2 - 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.

AB - 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.

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KW - Cox proportional hazards model

KW - Multi-state Markov chain

KW - Nun Study

KW - Shared random effect

KW - Weibull survival model

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Markov transition model to dementia with death as a competing event

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

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