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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
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    References listed on IDEAS

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    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.

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