IDEAS home Printed from https://ideas.repec.org/a/spr/lifeda/v28y2022i2d10.1007_s10985-022-09546-8.html
   My bibliography  Save this article

Competing risks regression models with covariates-adjusted censoring weight under the generalized case-cohort design

Author

Listed:
  • Yayun Xu

    (Medical College of Wisconsin)

  • Soyoung Kim

    (Medical College of Wisconsin)

  • Mei-Jie Zhang

    (Medical College of Wisconsin)

  • David Couper

    (University of North Carolina at Chapel Hill)

  • Kwang Woo Ahn

    (Medical College of Wisconsin)

Abstract

A generalized case-cohort design has been used when measuring exposures is expensive and events are not rare in the full cohort. This design collects expensive exposure information from a (stratified) randomly selected subset from the full cohort, called the subcohort, and a fraction of cases outside the subcohort. For the full cohort study with competing risks, He et al. (Scand J Stat 43:103-122, 2016) studied the non-stratified proportional subdistribution hazards model with covariate-dependent censoring to directly evaluate covariate effects on the cumulative incidence function. In this paper, we propose a stratified proportional subdistribution hazards model with covariate-adjusted censoring weights for competing risks data under the generalized case-cohort design. We consider a general class of weight functions to account for the generalized case-cohort design. Then, we derive the optimal weight function which minimizes the asymptotic variance of parameter estimates within the general class of weight functions. The proposed estimator is shown to be consistent and asymptotically normally distributed. The simulation studies show (i) the proposed estimator with covariate-adjusted weight is unbiased when the censoring distribution depends on covariates; and (ii) the proposed estimator with the optimal weight function gains parameter estimation efficiency. We apply the proposed method to stem cell transplantation and diabetes data sets.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:lifeda:v:28:y:2022:i:2:d:10.1007_s10985-022-09546-8
    DOI: 10.1007/s10985-022-09546-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10985-022-09546-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10985-022-09546-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    References listed on IDEAS

    as
    1. S. Kim & J. Cai & W. Lu, 2013. "More efficient estimators for case-cohort studies," Biometrika, Biometrika Trust, vol. 100(3), pages 695-708.
    2. Peng He & Frank Eriksson & Thomas H. Scheike & Mei-Jie Zhang, 2016. "A Proportional Hazards Regression Model for the Subdistribution with Covariates-adjusted Censoring Weight for Competing Risks Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 103-122, March.
    3. Lee, Unkyung & Sun, Yanqing & Scheike, Thomas H. & Gilbert, Peter B., 2018. "Analysis of generalized semiparametric regression models for cumulative incidence functions with missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 59-79.
    4. Donglin Zeng & D. Y. Lin, 2014. "Efficient Estimation of Semiparametric Transformation Models for Two-Phase Cohort Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 371-383, March.
    5. Jianwen Cai & Donglin Zeng, 2007. "Power Calculation for Case–Cohort Studies with Nonrare Events," Biometrics, The International Biometric Society, vol. 63(4), pages 1288-1295, December.
    6. Michal Kulich & D.Y. Lin, 2004. "Improving the Efficiency of Relative-Risk Estimation in Case-Cohort Studies," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 832-844, January.
    7. S. Kang & J. Cai, 2009. "Marginal hazards model for case-cohort studies with multiple disease outcomes," Biometrika, Biometrika Trust, vol. 96(4), pages 887-901.
    8. Erik T. Parner & Per K. Andersen & Morten Overgaard, 2020. "Cumulative risk regression in case–cohort studies using pseudo-observations," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 639-658, October.
    9. Per Kragh Andersen, 2003. "Generalised linear models for correlated pseudo-observations, with applications to multi-state models," Biometrika, Biometrika Trust, vol. 90(1), pages 15-27, March.
    10. Thomas H. Scheike & Mei-Jie Zhang & Thomas A. Gerds, 2008. "Predicting cumulative incidence probability by direct binomial regression," Biometrika, Biometrika Trust, vol. 95(1), pages 205-220.
    11. Lu Mao & D. Y. Lin, 2017. "Efficient estimation of semiparametric transformation models for the cumulative incidence of competing risks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 573-587, March.
    12. Yijian Huang, 2014. "Bootstrap for the case-cohort design," Biometrika, Biometrika Trust, vol. 101(2), pages 465-476.
    13. Bingqing Zhou & Aurelien Latouche & Vanderson Rocha & Jason Fine, 2011. "Competing Risks Regression for Stratified Data," Biometrics, The International Biometric Society, vol. 67(2), pages 661-670, June.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Lola Etievant & Mitchell H. Gail, 2024. "Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(3), pages 572-599, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Erik T. Parner & Per K. Andersen & Morten Overgaard, 2020. "Cumulative risk regression in case–cohort studies using pseudo-observations," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 639-658, October.
    2. Soyoung Kim & Yayun Xu & Mei‐Jie Zhang & Kwang‐Woo Ahn, 2020. "Stratified proportional subdistribution hazards model with covariate‐adjusted censoring weight for case‐cohort studies," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1222-1242, December.
    3. Adane F. Wogu & Haolin Li & Shanshan Zhao & Hazel B. Nichols & Jianwen Cai, 2023. "Additive subdistribution hazards regression for competing risks data in case‐cohort studies," Biometrics, The International Biometric Society, vol. 79(4), pages 3010-3022, December.
    4. 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.
    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. 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.
    7. 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.
    8. Qingning Zhou & Jianwen Cai & Haibo Zhou, 2018. "Outcome†dependent sampling with interval†censored failure time data," Biometrics, The International Biometric Society, vol. 74(1), pages 58-67, March.
    9. Jon Arni Steingrimsson & Robert L. Strawderman, 2017. "Estimation in the Semiparametric Accelerated Failure Time Model With Missing Covariates: Improving Efficiency Through Augmentation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1221-1235, July.
    10. 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.
    11. Deng, Lifeng & Ding, Jieli & Liu, Yanyan & Wei, Chengdong, 2018. "Regression analysis for the proportional hazards model with parameter constraints under case-cohort design," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 194-206.
    12. Jing Zhang & Haibo Zhou & Yanyan Liu & Jianwen Cai, 2021. "Feature screening for case‐cohort studies with failure time outcome," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 349-370, March.
    13. 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.
    14. Qingning Zhou & Jianwen Cai & Haibo Zhou, 2020. "Semiparametric inference for a two-stage outcome-dependent sampling design with interval-censored failure time data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(1), pages 85-108, January.
    15. 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.
    16. 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.
    17. He, Yizeng & Kim, Soyoung & Kim, Mi-Ok & Saber, Wael & Ahn, Kwang Woo, 2021. "Optimal treatment regimes for competing risk data using doubly robust outcome weighted learning with bi-level variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    18. Erik T. Parner & Per K. Andersen & Morten Overgaard, 2023. "Regression models for censored time-to-event data using infinitesimal jack-knife pseudo-observations, with applications to left-truncation," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(3), pages 654-671, July.
    19. Alina Schenk & Moritz Berger & Matthias Schmid, 2024. "Pseudo-value regression trees," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(2), pages 439-471, April.
    20. Rebecca Payne & Ming Yang & Yingye Zheng & Majken K. Jensen & Tianxi Cai, 2016. "Robust risk prediction with biomarkers under two‐phase stratified cohort design," Biometrics, The International Biometric Society, vol. 72(4), pages 1037-1045, December.

    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:spr:lifeda:v:28:y:2022:i:2:d:10.1007_s10985-022-09546-8. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.