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

Producción científica: Article › revisión exhaustiva

Resumen

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.

Idioma original	English
Páginas (desde-hasta)	5733-5747
Número de páginas	15
Publicación	Communications in Statistics - Theory and Methods
Volumen	48
N.º	23
DOI	https://doi.org/10.1080/03610926.2018.1520884
Estado	Published - dic 2 2019

Nota bibliográfica

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

Financiación

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.

Financiadores	Número del financiador
University of Kentucky’s Sanders-Brown Center on Aging	P30 AG028383, R01 AG019241, K25 AG043546, R01 AG038651
National Institute on Aging
National Center for Advancing Translational Sciences (NCATS)	P01 AG003949
University of Kentucky, Center for Clinical and Translational Science	UL1TR000117

ASJC Scopus subject areas

Statistics and Probability

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10.1080/03610926.2018.1520884

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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 {\textquotedblleft}missing completely at random{\textquotedblright} and {\textquotedblleft}missing at random{\textquotedblright} mechanisms. We also applied our method to a real dataset, the Einstein Aging Study.",

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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.

PY - 2019/12/2

Y1 - 2019/12/2

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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JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

IS - 23

ER -

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

Resumen

Nota bibliográfica

Financiación

ASJC Scopus subject areas

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