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An additive subdistribution hazard model for competing risks data

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  • Wanxing Li
  • Xiaoming Xue
  • Yonghong Long

Abstract

The cumulative incidence function plays an important role in assessing its treatment and covariate effects with competing risks data. In this article, we consider an additive hazard model allowing the time-varying covariate effects for the subdistribution and propose the weighted estimating equation under the covariate-dependent censoring by fitting the Cox-type hazard model for the censoring distribution. When there exists some association between the censoring time and the covariates, the proposed coefficients’ estimations are unbiased and the large-sample properties are established. The finite-sample properties of the proposed estimators are examined in the simulation study. The proposed Cox-weighted method is applied to a competing risks dataset from a Hodgkin's disease study.

Suggested Citation

  • Wanxing Li & Xiaoming Xue & Yonghong Long, 2017. "An additive subdistribution hazard model for competing risks data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(23), pages 11667-11687, December.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:23:p:11667-11687
    DOI: 10.1080/03610926.2016.1277759
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    Cited by:

    1. 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.

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