Estimation of multi-state models with missing covariate values based on observed data likelihood

Wenjie Lou; Erin L. Abner; Lijie Wan; David W. Fardo; Richard Lipton; Mindy Katz; Richard J. Kryscio

doi:10.1080/03610926.2018.1520884

Estimation of multi-state models with missing covariate values based on observed data likelihood

Wenjie Lou, Erin L. Abner, Lijie Wan, David W. Fardo, Richard Lipton, Mindy Katz, Richard J. Kryscio

Research output: Contribution to journal › Article › peer-review

Abstract

Continuous-time multi-state models are commonly used to study diseases with multiple stages. Potential risk factors associated with the disease are added to the transition intensities of the model as covariates, but missing covariate measurements arise frequently in practice. We propose a likelihood-based method that deals efficiently with a missing covariate in these models. Our simulation study showed that the method performs well for both “missing completely at random” and “missing at random” mechanisms. We also applied our method to a real dataset, the Einstein Aging Study.

Original language	English
Pages (from-to)	5733-5747
Number of pages	15
Journal	Communications in Statistics - Theory and Methods
Volume	48
Issue number	23
DOIs	https://doi.org/10.1080/03610926.2018.1520884
State	Published - Dec 2 2019

Bibliographical note

Funding Information:
This research was partially funded with support from the following grants to the University of Kentucky’s Center on Aging: R01 AG038651, P30 AG028383, K25 AG043546, and R01 AG019241 from the National Institute on Aging, as well as a grant to the University of Kentucky’s Center for Clinical and Translational Science, UL1TR000117, from the National Center for Advancing Translational Sciences. The Einstein Aging Study is supported by P01 AG003949.

Publisher Copyright:
© 2018, © 2018 Taylor & Francis Group, LLC.

Keywords

Longitudinal data
MAR
MCAR
missing covariate
multi-state model

ASJC Scopus subject areas

Statistics and Probability

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10.1080/03610926.2018.1520884

Cite this

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title = "Estimation of multi-state models with missing covariate values based on observed data likelihood",

abstract = "Continuous-time multi-state models are commonly used to study diseases with multiple stages. Potential risk factors associated with the disease are added to the transition intensities of the model as covariates, but missing covariate measurements arise frequently in practice. We propose a likelihood-based method that deals efficiently with a missing covariate in these models. Our simulation study showed that the method performs well for both “missing completely at random” and “missing at random” mechanisms. We also applied our method to a real dataset, the Einstein Aging Study.",

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author = "Wenjie Lou and Abner, {Erin L.} and Lijie Wan and Fardo, {David W.} and Richard Lipton and Mindy Katz and Kryscio, {Richard J.}",

note = "Funding Information: This research was partially funded with support from the following grants to the University of Kentucky{\textquoteright}s Center on Aging: R01 AG038651, P30 AG028383, K25 AG043546, and R01 AG019241 from the National Institute on Aging, as well as a grant to the University of Kentucky{\textquoteright}s Center for Clinical and Translational Science, UL1TR000117, from the National Center for Advancing Translational Sciences. The Einstein Aging Study is supported by P01 AG003949. Publisher Copyright: {\textcopyright} 2018, {\textcopyright} 2018 Taylor & Francis Group, LLC.",

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AU - Lou, Wenjie

AU - Abner, Erin L.

AU - Wan, Lijie

AU - Fardo, David W.

AU - Lipton, Richard

AU - Katz, Mindy

AU - Kryscio, Richard J.

N1 - Funding Information: This research was partially funded with support from the following grants to the University of Kentucky’s Center on Aging: R01 AG038651, P30 AG028383, K25 AG043546, and R01 AG019241 from the National Institute on Aging, as well as a grant to the University of Kentucky’s Center for Clinical and Translational Science, UL1TR000117, from the National Center for Advancing Translational Sciences. The Einstein Aging Study is supported by P01 AG003949. Publisher Copyright: © 2018, © 2018 Taylor & Francis Group, LLC.

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N2 - Continuous-time multi-state models are commonly used to study diseases with multiple stages. Potential risk factors associated with the disease are added to the transition intensities of the model as covariates, but missing covariate measurements arise frequently in practice. We propose a likelihood-based method that deals efficiently with a missing covariate in these models. Our simulation study showed that the method performs well for both “missing completely at random” and “missing at random” mechanisms. We also applied our method to a real dataset, the Einstein Aging Study.

AB - Continuous-time multi-state models are commonly used to study diseases with multiple stages. Potential risk factors associated with the disease are added to the transition intensities of the model as covariates, but missing covariate measurements arise frequently in practice. We propose a likelihood-based method that deals efficiently with a missing covariate in these models. Our simulation study showed that the method performs well for both “missing completely at random” and “missing at random” mechanisms. We also applied our method to a real dataset, the Einstein Aging Study.

KW - Longitudinal data

KW - MAR

KW - MCAR

KW - missing covariate

KW - multi-state model

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Estimation of multi-state models with missing covariate values based on observed data likelihood

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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