Marginal hazard regression for correlated failure time data with auxiliary covariates
Abstract In many biomedical studies, it is common that due to budget constraints, the primary covariate is only collected in a randomly selected subset from the full study cohort. Often, there is an inexpensive auxiliary covariate for the primary exposure variable that is readily available for all t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Yanyan [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2011 |
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| Übergeordnetes Werk: |
Enthalten in: Lifetime data analysis - Springer US, 1995, 18(2011), 1 vom: 18. Nov., Seite 116-138 |
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| Übergeordnetes Werk: |
volume:18 ; year:2011 ; number:1 ; day:18 ; month:11 ; pages:116-138 |
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| DOI / URN: |
10.1007/s10985-011-9209-x |
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OLC2067136119 |
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| 520 | |a Abstract In many biomedical studies, it is common that due to budget constraints, the primary covariate is only collected in a randomly selected subset from the full study cohort. Often, there is an inexpensive auxiliary covariate for the primary exposure variable that is readily available for all the cohort subjects. Valid statistical methods that make use of the auxiliary information to improve study efficiency need to be developed. To this end, we develop an estimated partial likelihood approach for correlated failure time data with auxiliary information. We assume a marginal hazard model with common baseline hazard function. The asymptotic properties for the proposed estimators are developed. The proof of the asymptotic results for the proposed estimators is nontrivial since the moments used in estimating equation are not martingale-based and the classical martingale theory is not sufficient. Instead, our proofs rely on modern empirical process theory. The proposed estimator is evaluated through simulation studies and is shown to have increased efficiency compared to existing methods. The proposed method is illustrated with a data set from the Framingham study. | ||
| 650 | 4 | |a Marginal hazard model | |
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| 700 | 1 | |a Yuan, Zhongshang |4 aut | |
| 700 | 1 | |a Cai, Jianwen |4 aut | |
| 700 | 1 | |a Zhou, Haibo |4 aut | |
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10.1007/s10985-011-9209-x doi (DE-627)OLC2067136119 (DE-He213)s10985-011-9209-x-p DE-627 ger DE-627 rakwb eng 510 004 VZ Liu, Yanyan verfasserin aut Marginal hazard regression for correlated failure time data with auxiliary covariates 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract In many biomedical studies, it is common that due to budget constraints, the primary covariate is only collected in a randomly selected subset from the full study cohort. Often, there is an inexpensive auxiliary covariate for the primary exposure variable that is readily available for all the cohort subjects. Valid statistical methods that make use of the auxiliary information to improve study efficiency need to be developed. To this end, we develop an estimated partial likelihood approach for correlated failure time data with auxiliary information. We assume a marginal hazard model with common baseline hazard function. The asymptotic properties for the proposed estimators are developed. The proof of the asymptotic results for the proposed estimators is nontrivial since the moments used in estimating equation are not martingale-based and the classical martingale theory is not sufficient. Instead, our proofs rely on modern empirical process theory. The proposed estimator is evaluated through simulation studies and is shown to have increased efficiency compared to existing methods. The proposed method is illustrated with a data set from the Framingham study. Marginal hazard model Correlated failure time Validation set Auxiliary covariate Yuan, Zhongshang aut Cai, Jianwen aut Zhou, Haibo aut Enthalten in Lifetime data analysis Springer US, 1995 18(2011), 1 vom: 18. Nov., Seite 116-138 (DE-627)233193332 (DE-600)1393066-7 (DE-576)07005777X 1380-7870 nnns volume:18 year:2011 number:1 day:18 month:11 pages:116-138 https://doi.org/10.1007/s10985-011-9209-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_4305 AR 18 2011 1 18 11 116-138 |
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10.1007/s10985-011-9209-x doi (DE-627)OLC2067136119 (DE-He213)s10985-011-9209-x-p DE-627 ger DE-627 rakwb eng 510 004 VZ Liu, Yanyan verfasserin aut Marginal hazard regression for correlated failure time data with auxiliary covariates 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract In many biomedical studies, it is common that due to budget constraints, the primary covariate is only collected in a randomly selected subset from the full study cohort. Often, there is an inexpensive auxiliary covariate for the primary exposure variable that is readily available for all the cohort subjects. Valid statistical methods that make use of the auxiliary information to improve study efficiency need to be developed. To this end, we develop an estimated partial likelihood approach for correlated failure time data with auxiliary information. We assume a marginal hazard model with common baseline hazard function. The asymptotic properties for the proposed estimators are developed. The proof of the asymptotic results for the proposed estimators is nontrivial since the moments used in estimating equation are not martingale-based and the classical martingale theory is not sufficient. Instead, our proofs rely on modern empirical process theory. The proposed estimator is evaluated through simulation studies and is shown to have increased efficiency compared to existing methods. The proposed method is illustrated with a data set from the Framingham study. Marginal hazard model Correlated failure time Validation set Auxiliary covariate Yuan, Zhongshang aut Cai, Jianwen aut Zhou, Haibo aut Enthalten in Lifetime data analysis Springer US, 1995 18(2011), 1 vom: 18. Nov., Seite 116-138 (DE-627)233193332 (DE-600)1393066-7 (DE-576)07005777X 1380-7870 nnns volume:18 year:2011 number:1 day:18 month:11 pages:116-138 https://doi.org/10.1007/s10985-011-9209-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_4305 AR 18 2011 1 18 11 116-138 |
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10.1007/s10985-011-9209-x doi (DE-627)OLC2067136119 (DE-He213)s10985-011-9209-x-p DE-627 ger DE-627 rakwb eng 510 004 VZ Liu, Yanyan verfasserin aut Marginal hazard regression for correlated failure time data with auxiliary covariates 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract In many biomedical studies, it is common that due to budget constraints, the primary covariate is only collected in a randomly selected subset from the full study cohort. Often, there is an inexpensive auxiliary covariate for the primary exposure variable that is readily available for all the cohort subjects. Valid statistical methods that make use of the auxiliary information to improve study efficiency need to be developed. To this end, we develop an estimated partial likelihood approach for correlated failure time data with auxiliary information. We assume a marginal hazard model with common baseline hazard function. The asymptotic properties for the proposed estimators are developed. The proof of the asymptotic results for the proposed estimators is nontrivial since the moments used in estimating equation are not martingale-based and the classical martingale theory is not sufficient. Instead, our proofs rely on modern empirical process theory. The proposed estimator is evaluated through simulation studies and is shown to have increased efficiency compared to existing methods. The proposed method is illustrated with a data set from the Framingham study. Marginal hazard model Correlated failure time Validation set Auxiliary covariate Yuan, Zhongshang aut Cai, Jianwen aut Zhou, Haibo aut Enthalten in Lifetime data analysis Springer US, 1995 18(2011), 1 vom: 18. Nov., Seite 116-138 (DE-627)233193332 (DE-600)1393066-7 (DE-576)07005777X 1380-7870 nnns volume:18 year:2011 number:1 day:18 month:11 pages:116-138 https://doi.org/10.1007/s10985-011-9209-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_4305 AR 18 2011 1 18 11 116-138 |
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10.1007/s10985-011-9209-x doi (DE-627)OLC2067136119 (DE-He213)s10985-011-9209-x-p DE-627 ger DE-627 rakwb eng 510 004 VZ Liu, Yanyan verfasserin aut Marginal hazard regression for correlated failure time data with auxiliary covariates 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract In many biomedical studies, it is common that due to budget constraints, the primary covariate is only collected in a randomly selected subset from the full study cohort. Often, there is an inexpensive auxiliary covariate for the primary exposure variable that is readily available for all the cohort subjects. Valid statistical methods that make use of the auxiliary information to improve study efficiency need to be developed. To this end, we develop an estimated partial likelihood approach for correlated failure time data with auxiliary information. We assume a marginal hazard model with common baseline hazard function. The asymptotic properties for the proposed estimators are developed. The proof of the asymptotic results for the proposed estimators is nontrivial since the moments used in estimating equation are not martingale-based and the classical martingale theory is not sufficient. Instead, our proofs rely on modern empirical process theory. The proposed estimator is evaluated through simulation studies and is shown to have increased efficiency compared to existing methods. The proposed method is illustrated with a data set from the Framingham study. Marginal hazard model Correlated failure time Validation set Auxiliary covariate Yuan, Zhongshang aut Cai, Jianwen aut Zhou, Haibo aut Enthalten in Lifetime data analysis Springer US, 1995 18(2011), 1 vom: 18. Nov., Seite 116-138 (DE-627)233193332 (DE-600)1393066-7 (DE-576)07005777X 1380-7870 nnns volume:18 year:2011 number:1 day:18 month:11 pages:116-138 https://doi.org/10.1007/s10985-011-9209-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_4305 AR 18 2011 1 18 11 116-138 |
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10.1007/s10985-011-9209-x doi (DE-627)OLC2067136119 (DE-He213)s10985-011-9209-x-p DE-627 ger DE-627 rakwb eng 510 004 VZ Liu, Yanyan verfasserin aut Marginal hazard regression for correlated failure time data with auxiliary covariates 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract In many biomedical studies, it is common that due to budget constraints, the primary covariate is only collected in a randomly selected subset from the full study cohort. Often, there is an inexpensive auxiliary covariate for the primary exposure variable that is readily available for all the cohort subjects. Valid statistical methods that make use of the auxiliary information to improve study efficiency need to be developed. To this end, we develop an estimated partial likelihood approach for correlated failure time data with auxiliary information. We assume a marginal hazard model with common baseline hazard function. The asymptotic properties for the proposed estimators are developed. The proof of the asymptotic results for the proposed estimators is nontrivial since the moments used in estimating equation are not martingale-based and the classical martingale theory is not sufficient. Instead, our proofs rely on modern empirical process theory. The proposed estimator is evaluated through simulation studies and is shown to have increased efficiency compared to existing methods. The proposed method is illustrated with a data set from the Framingham study. Marginal hazard model Correlated failure time Validation set Auxiliary covariate Yuan, Zhongshang aut Cai, Jianwen aut Zhou, Haibo aut Enthalten in Lifetime data analysis Springer US, 1995 18(2011), 1 vom: 18. Nov., Seite 116-138 (DE-627)233193332 (DE-600)1393066-7 (DE-576)07005777X 1380-7870 nnns volume:18 year:2011 number:1 day:18 month:11 pages:116-138 https://doi.org/10.1007/s10985-011-9209-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_4305 AR 18 2011 1 18 11 116-138 |
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Abstract In many biomedical studies, it is common that due to budget constraints, the primary covariate is only collected in a randomly selected subset from the full study cohort. Often, there is an inexpensive auxiliary covariate for the primary exposure variable that is readily available for all the cohort subjects. Valid statistical methods that make use of the auxiliary information to improve study efficiency need to be developed. To this end, we develop an estimated partial likelihood approach for correlated failure time data with auxiliary information. We assume a marginal hazard model with common baseline hazard function. The asymptotic properties for the proposed estimators are developed. The proof of the asymptotic results for the proposed estimators is nontrivial since the moments used in estimating equation are not martingale-based and the classical martingale theory is not sufficient. Instead, our proofs rely on modern empirical process theory. The proposed estimator is evaluated through simulation studies and is shown to have increased efficiency compared to existing methods. The proposed method is illustrated with a data set from the Framingham study. © Springer Science+Business Media, LLC 2011 |
| abstractGer |
Abstract In many biomedical studies, it is common that due to budget constraints, the primary covariate is only collected in a randomly selected subset from the full study cohort. Often, there is an inexpensive auxiliary covariate for the primary exposure variable that is readily available for all the cohort subjects. Valid statistical methods that make use of the auxiliary information to improve study efficiency need to be developed. To this end, we develop an estimated partial likelihood approach for correlated failure time data with auxiliary information. We assume a marginal hazard model with common baseline hazard function. The asymptotic properties for the proposed estimators are developed. The proof of the asymptotic results for the proposed estimators is nontrivial since the moments used in estimating equation are not martingale-based and the classical martingale theory is not sufficient. Instead, our proofs rely on modern empirical process theory. The proposed estimator is evaluated through simulation studies and is shown to have increased efficiency compared to existing methods. The proposed method is illustrated with a data set from the Framingham study. © Springer Science+Business Media, LLC 2011 |
| abstract_unstemmed |
Abstract In many biomedical studies, it is common that due to budget constraints, the primary covariate is only collected in a randomly selected subset from the full study cohort. Often, there is an inexpensive auxiliary covariate for the primary exposure variable that is readily available for all the cohort subjects. Valid statistical methods that make use of the auxiliary information to improve study efficiency need to be developed. To this end, we develop an estimated partial likelihood approach for correlated failure time data with auxiliary information. We assume a marginal hazard model with common baseline hazard function. The asymptotic properties for the proposed estimators are developed. The proof of the asymptotic results for the proposed estimators is nontrivial since the moments used in estimating equation are not martingale-based and the classical martingale theory is not sufficient. Instead, our proofs rely on modern empirical process theory. The proposed estimator is evaluated through simulation studies and is shown to have increased efficiency compared to existing methods. The proposed method is illustrated with a data set from the Framingham study. © Springer Science+Business Media, LLC 2011 |
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Yuan, Zhongshang Cai, Jianwen Zhou, Haibo |
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Yuan, Zhongshang Cai, Jianwen Zhou, Haibo |
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| doi_str |
10.1007/s10985-011-9209-x |
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2024-07-03T13:53:41.676Z |
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