Using Full Cohort Information to Improve the Estimation Efficiency of Marginal Hazard Model for Multivariate Failure Times in Case-Cohort Studies

Hongtao Zhang; Haibo Zhou; David Couper; Jianwen Cai

doi:doi:10.11648/j.ajam.20210906.11

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Using Full Cohort Information to Improve the Estimation Efficiency of Marginal Hazard Model for Multivariate Failure Times in Case-Cohort Studies

Hongtao Zhang, Haibo Zhou, David Couper, Jianwen Cai

Published in American Journal of Applied Mathematics (Volume 9, Issue 6)

Received: 8 June 2021 Accepted: 2 December 2021 Published: 24 December 2021

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Abstract

The case-cohort design is widely used in large cohort studies when it is prohibitively costly to measure some exposures for all subjects in the full cohort, especially in studies where the disease rate is low. To investigate the effect of a risk factor on different diseases, multiple case-cohort studies using the same subcohort are usually conducted. To compare the effect of a risk factor on different types of diseases, times to different disease events need to be modeled simultaneously. Existing case-cohort estimators for multiple disease outcomes utilize only the relevant covariate information in cases and subcohort controls, though many covariates are measured for everyone in the full cohort. Intuitively, making full use of the relevant covariate information can improve efficiency. To this end, we consider a class of doubly-weighted estimators for both regular and generalized case-cohort studies with multiple disease outcomes. The asymptotic properties of the proposed estimators are derived and our simulation studies show that a gain in efficiency can be achieved with a properly chosen weight function. We apply the proposed method to re-analyze a data set from Atherosclerosis Risk in Communities (ARIC) study to showcase the gain in efficiency. Concluding remarks and future researches are also discussed.

Published in	American Journal of Applied Mathematics (Volume 9, Issue 6)
DOI	10.11648/j.ajam.20210906.11
Page(s)	192-210
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Case-cohort Study, Multiple Disease Outcomes, Survival Analysis

References

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APA Style

Hongtao Zhang, Haibo Zhou, David Couper, Jianwen Cai. (2021). Using Full Cohort Information to Improve the Estimation Efficiency of Marginal Hazard Model for Multivariate Failure Times in Case-Cohort Studies. American Journal of Applied Mathematics, 9(6), 192-210. https://doi.org/10.11648/j.ajam.20210906.11

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ACS Style

Hongtao Zhang; Haibo Zhou; David Couper; Jianwen Cai. Using Full Cohort Information to Improve the Estimation Efficiency of Marginal Hazard Model for Multivariate Failure Times in Case-Cohort Studies. Am. J. Appl. Math. 2021, 9(6), 192-210. doi: 10.11648/j.ajam.20210906.11

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AMA Style

Hongtao Zhang, Haibo Zhou, David Couper, Jianwen Cai. Using Full Cohort Information to Improve the Estimation Efficiency of Marginal Hazard Model for Multivariate Failure Times in Case-Cohort Studies. Am J Appl Math. 2021;9(6):192-210. doi: 10.11648/j.ajam.20210906.11

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@article{10.11648/j.ajam.20210906.11,
  author = {Hongtao Zhang and Haibo Zhou and David Couper and Jianwen Cai},
  title = {Using Full Cohort Information to Improve the Estimation Efficiency of Marginal Hazard Model for Multivariate Failure Times in Case-Cohort Studies},
  journal = {American Journal of Applied Mathematics},
  volume = {9},
  number = {6},
  pages = {192-210},
  doi = {10.11648/j.ajam.20210906.11},
  url = {https://doi.org/10.11648/j.ajam.20210906.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20210906.11},
  abstract = {The case-cohort design is widely used in large cohort studies when it is prohibitively costly to measure some exposures for all subjects in the full cohort, especially in studies where the disease rate is low. To investigate the effect of a risk factor on different diseases, multiple case-cohort studies using the same subcohort are usually conducted. To compare the effect of a risk factor on different types of diseases, times to different disease events need to be modeled simultaneously. Existing case-cohort estimators for multiple disease outcomes utilize only the relevant covariate information in cases and subcohort controls, though many covariates are measured for everyone in the full cohort. Intuitively, making full use of the relevant covariate information can improve efficiency. To this end, we consider a class of doubly-weighted estimators for both regular and generalized case-cohort studies with multiple disease outcomes. The asymptotic properties of the proposed estimators are derived and our simulation studies show that a gain in efficiency can be achieved with a properly chosen weight function. We apply the proposed method to re-analyze a data set from Atherosclerosis Risk in Communities (ARIC) study to showcase the gain in efficiency. Concluding remarks and future researches are also discussed.},
 year = {2021}
}

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TY  - JOUR
T1  - Using Full Cohort Information to Improve the Estimation Efficiency of Marginal Hazard Model for Multivariate Failure Times in Case-Cohort Studies
AU  - Hongtao Zhang
AU  - Haibo Zhou
AU  - David Couper
AU  - Jianwen Cai
Y1  - 2021/12/24
PY  - 2021
N1  - https://doi.org/10.11648/j.ajam.20210906.11
DO  - 10.11648/j.ajam.20210906.11
T2  - American Journal of Applied Mathematics
JF  - American Journal of Applied Mathematics
JO  - American Journal of Applied Mathematics
SP  - 192
EP  - 210
PB  - Science Publishing Group
SN  - 2330-006X
UR  - https://doi.org/10.11648/j.ajam.20210906.11
AB  - The case-cohort design is widely used in large cohort studies when it is prohibitively costly to measure some exposures for all subjects in the full cohort, especially in studies where the disease rate is low. To investigate the effect of a risk factor on different diseases, multiple case-cohort studies using the same subcohort are usually conducted. To compare the effect of a risk factor on different types of diseases, times to different disease events need to be modeled simultaneously. Existing case-cohort estimators for multiple disease outcomes utilize only the relevant covariate information in cases and subcohort controls, though many covariates are measured for everyone in the full cohort. Intuitively, making full use of the relevant covariate information can improve efficiency. To this end, we consider a class of doubly-weighted estimators for both regular and generalized case-cohort studies with multiple disease outcomes. The asymptotic properties of the proposed estimators are derived and our simulation studies show that a gain in efficiency can be achieved with a properly chosen weight function. We apply the proposed method to re-analyze a data set from Atherosclerosis Risk in Communities (ARIC) study to showcase the gain in efficiency. Concluding remarks and future researches are also discussed.
VL  - 9
IS  - 6
ER  -

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Author Information

Hongtao Zhang

Bristol Myers Squibb, Berkeley Heights, New Jersey, USA
Haibo Zhou

Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA
David Couper

Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA
Jianwen Cai

Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA

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APA Style

Hongtao Zhang, Haibo Zhou, David Couper, Jianwen Cai. (2021). Using Full Cohort Information to Improve the Estimation Efficiency of Marginal Hazard Model for Multivariate Failure Times in Case-Cohort Studies. American Journal of Applied Mathematics, 9(6), 192-210. https://doi.org/10.11648/j.ajam.20210906.11

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ACS Style

Hongtao Zhang; Haibo Zhou; David Couper; Jianwen Cai. Using Full Cohort Information to Improve the Estimation Efficiency of Marginal Hazard Model for Multivariate Failure Times in Case-Cohort Studies. Am. J. Appl. Math. 2021, 9(6), 192-210. doi: 10.11648/j.ajam.20210906.11

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AMA Style

Hongtao Zhang, Haibo Zhou, David Couper, Jianwen Cai. Using Full Cohort Information to Improve the Estimation Efficiency of Marginal Hazard Model for Multivariate Failure Times in Case-Cohort Studies. Am J Appl Math. 2021;9(6):192-210. doi: 10.11648/j.ajam.20210906.11

Copy | Download

@article{10.11648/j.ajam.20210906.11,
  author = {Hongtao Zhang and Haibo Zhou and David Couper and Jianwen Cai},
  title = {Using Full Cohort Information to Improve the Estimation Efficiency of Marginal Hazard Model for Multivariate Failure Times in Case-Cohort Studies},
  journal = {American Journal of Applied Mathematics},
  volume = {9},
  number = {6},
  pages = {192-210},
  doi = {10.11648/j.ajam.20210906.11},
  url = {https://doi.org/10.11648/j.ajam.20210906.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20210906.11},
  abstract = {The case-cohort design is widely used in large cohort studies when it is prohibitively costly to measure some exposures for all subjects in the full cohort, especially in studies where the disease rate is low. To investigate the effect of a risk factor on different diseases, multiple case-cohort studies using the same subcohort are usually conducted. To compare the effect of a risk factor on different types of diseases, times to different disease events need to be modeled simultaneously. Existing case-cohort estimators for multiple disease outcomes utilize only the relevant covariate information in cases and subcohort controls, though many covariates are measured for everyone in the full cohort. Intuitively, making full use of the relevant covariate information can improve efficiency. To this end, we consider a class of doubly-weighted estimators for both regular and generalized case-cohort studies with multiple disease outcomes. The asymptotic properties of the proposed estimators are derived and our simulation studies show that a gain in efficiency can be achieved with a properly chosen weight function. We apply the proposed method to re-analyze a data set from Atherosclerosis Risk in Communities (ARIC) study to showcase the gain in efficiency. Concluding remarks and future researches are also discussed.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Using Full Cohort Information to Improve the Estimation Efficiency of Marginal Hazard Model for Multivariate Failure Times in Case-Cohort Studies
AU  - Hongtao Zhang
AU  - Haibo Zhou
AU  - David Couper
AU  - Jianwen Cai
Y1  - 2021/12/24
PY  - 2021
N1  - https://doi.org/10.11648/j.ajam.20210906.11
DO  - 10.11648/j.ajam.20210906.11
T2  - American Journal of Applied Mathematics
JF  - American Journal of Applied Mathematics
JO  - American Journal of Applied Mathematics
SP  - 192
EP  - 210
PB  - Science Publishing Group
SN  - 2330-006X
UR  - https://doi.org/10.11648/j.ajam.20210906.11
AB  - The case-cohort design is widely used in large cohort studies when it is prohibitively costly to measure some exposures for all subjects in the full cohort, especially in studies where the disease rate is low. To investigate the effect of a risk factor on different diseases, multiple case-cohort studies using the same subcohort are usually conducted. To compare the effect of a risk factor on different types of diseases, times to different disease events need to be modeled simultaneously. Existing case-cohort estimators for multiple disease outcomes utilize only the relevant covariate information in cases and subcohort controls, though many covariates are measured for everyone in the full cohort. Intuitively, making full use of the relevant covariate information can improve efficiency. To this end, we consider a class of doubly-weighted estimators for both regular and generalized case-cohort studies with multiple disease outcomes. The asymptotic properties of the proposed estimators are derived and our simulation studies show that a gain in efficiency can be achieved with a properly chosen weight function. We apply the proposed method to re-analyze a data set from Atherosclerosis Risk in Communities (ARIC) study to showcase the gain in efficiency. Concluding remarks and future researches are also discussed.
VL  - 9
IS  - 6
ER  -

Copy | Download