IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v100y2013i3p695-708.html
   My bibliography  Save this article

More efficient estimators for case-cohort studies

Author

Listed:
  • S. Kim
  • J. Cai
  • W. Lu

Abstract

The case-cohort study design, used to reduce costs in large cohort studies, involves a random sample of the entire cohort, called the subcohort, augmented with subjects having the disease of interest but not in the subcohort sample. When several diseases are of interest, multiple case-cohort studies may be conducted using the same subcohort, with each disease analysed separately, ignoring the additional exposure measurements collected on subjects with the other diseases. This is not an efficient use of the data, and in this paper we propose more efficient estimators. We consider both joint and separate analyses for the multiple diseases. We propose an estimating equation approach with a new weight function, and we establish the consistency and asymptotic normality of the resulting estimator. Simulation studies show that the proposed methods using all available information lead to gains in efficiency. We apply our proposed method to data from the Busselton Health Study. Copyright 2013, Oxford University Press.

Suggested Citation

  • S. Kim & J. Cai & W. Lu, 2013. "More efficient estimators for case-cohort studies," Biometrika, Biometrika Trust, vol. 100(3), pages 695-708.
    • Handle: RePEc:oup:biomet:v:100:y:2013:i:3:p:695-708
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/biomet/ast018
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yayun Xu & Soyoung Kim & Mei-Jie Zhang & David Couper & Kwang Woo Ahn, 2022. "Competing risks regression models with covariates-adjusted censoring weight under the generalized case-cohort design," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(2), pages 241-262, April.
    2. Yichen Lou & Peijie Wang & Jianguo Sun, 2023. "A semi-parametric weighted likelihood approach for regression analysis of bivariate interval-censored outcomes from case-cohort studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(3), pages 628-653, July.
    3. Jieli Ding & Tsui-Shan Lu & Jianwen Cai & Haibo Zhou, 2017. "Recent progresses in outcome-dependent sampling with failure time data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(1), pages 57-82, January.
    4. Yinghao Pan & Jianwen Cai & Sangmi Kim & Haibo Zhou, 2018. "Regression analysis for secondary response variable in a case‐cohort study," Biometrics, The International Biometric Society, vol. 74(3), pages 1014-1022, September.
    5. Jing Zhang & Haibo Zhou & Yanyan Liu & Jianwen Cai, 2021. "Conditional screening for ultrahigh-dimensional survival data in case-cohort studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(4), pages 632-661, October.
    6. Mingzhe Wu & Ming Zheng & Wen Yu & Ruofan Wu, 2018. "Estimation and variable selection for semiparametric transformation models under a more efficient cohort sampling design," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 570-596, September.
    7. Jichang Yu & Haibo Zhou & Jianwen Cai, 2021. "Accelerated failure time model for data from outcome-dependent sampling," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(1), pages 15-37, January.
    8. Ying Yan & Haibo Zhou & Jianwen Cai, 2017. "Improving efficiency of parameter estimation in case-cohort studies with multivariate failure time data," Biometrics, The International Biometric Society, vol. 73(3), pages 1042-1052, September.
    9. Soyoung Kim & Donglin Zeng & Jianwen Cai, 2018. "Analysis of multiple survival events in generalized case‐cohort designs," Biometrics, The International Biometric Society, vol. 74(4), pages 1250-1260, December.
    10. Cutz, L. & Haro, P. & Santana, D. & Johnsson, F., 2016. "Assessment of biomass energy sources and technologies: The case of Central America," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1411-1431.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:biomet:v:100:y:2013:i:3:p:695-708. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/biomet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.