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

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.

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Acknowledgements

This work was supported in part by the Medical College of Wisconsin Cancer Center, the Advancing a Healthier Wisconsin Endowment (Project # 5520461), Institutional Research Grant IRG #16-183-31 from the American Cancer Society and the Medical College of Wisconsin Cancer Center, and the US National Cancer Institute (U24CA076518). The ARIC study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under Contract Nos. (HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700005I,HHSN268201700004I), with the diabetes ancillary study supported by National Institute of Diabetes, Digestive and Kidney Diseases Grant 5R01-DK56918-03. The authors thank the staff and participants of the ARIC study for their important contributions. Xu and Kim are equally contributed authors.

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Correspondence to Soyoung Kim.

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Xu, Y., Kim, S., Zhang, MJ. et al. Competing risks regression models with covariates-adjusted censoring weight under the generalized case-cohort design. Lifetime Data Anal 28, 241–262 (2022). https://doi.org/10.1007/s10985-022-09546-8

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Keywords

  • Competing risks data
  • Covariate-adjusted weight function
  • Optimal weight function
  • Stratified generalized case-cohort design