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Nonparametric estimation of cause-specific cross hazard ratio with bivariate competing risks data

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  • Yu Cheng
  • Jason P. Fine

Abstract

We propose an alternative representation of the cause-specific cross hazard ratio for bivariate competing risks data. The representation leads to a simple plug-in estimator, unlike an existing ad hoc procedure. The large sample properties of the resulting inferences are established. Simulations and a real data example demonstrate that the proposed methodology may substantially reduce the computational burden of the existing procedure, while maintaining similar efficiency properties. Copyright 2008, Oxford University Press.

Suggested Citation

  • Yu Cheng & Jason P. Fine, 2008. "Nonparametric estimation of cause-specific cross hazard ratio with bivariate competing risks data," Biometrika, Biometrika Trust, vol. 95(1), pages 233-240.
    • Handle: RePEc:oup:biomet:v:95:y:2008:i:1:p:233-240
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    File URL: http://hdl.handle.net/10.1093/biomet/asm089
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    Cited by:

    1. Wang Hao & Cheng Yu, 2014. "Piecewise Cause-Specific Association Analyses of Multivariate Untied or Tied Competing Risks Data," The International Journal of Biostatistics, De Gruyter, vol. 10(2), pages 197-220, November.
    2. Prakash Chandra & Yogesh Mani Tripathi & Liang Wang & Chandrakant Lodhi, 2023. "Estimation for Kies distribution with generalized progressive hybrid censoring under partially observed competing risks model," Journal of Risk and Reliability, , vol. 237(6), pages 1048-1072, December.
    3. Cheng Yu, 2009. "Modeling Cumulative Incidences of Dementia and Dementia-Free Death Using a Novel Three-Parameter Logistic Function," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-19, November.
    4. Jeongyong Kim & Karen Bandeen-Roche, 2019. "Parametric estimation of association in bivariate failure-time data subject to competing risks: sensitivity to underlying assumptions," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 259-279, April.

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