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A new approach to regression analysis of censored competing-risks data

Author

Listed:
  • Yuxue Jin

    (Quantitative Marketing, Google)

  • Tze Leung Lai

    (Stanford University)

Abstract

An approximate likelihood approach is developed for regression analysis of censored competing-risks data. This approach models directly the cumulative incidence function, instead of the cause-specific hazard function, in terms of explanatory covariates under a proportional subdistribution hazards assumption. It uses a self-consistent iterative procedure to maximize an approximate semiparametric likelihood function, leading to an asymptotically normal and efficient estimator of the vector of regression parameters. Simulation studies demonstrate its advantages over previous methods.

Suggested Citation

  • Yuxue Jin & Tze Leung Lai, 2017. "A new approach to regression analysis of censored competing-risks data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 605-625, October.
    • Handle: RePEc:spr:lifeda:v:23:y:2017:i:4:d:10.1007_s10985-016-9378-8
    DOI: 10.1007/s10985-016-9378-8
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    References listed on IDEAS

    as
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    5. John P. Klein & Per Kragh Andersen, 2005. "Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function," Biometrics, The International Biometric Society, vol. 61(1), pages 223-229, March.
    6. Lai, Tze Leung & Ying, Zhiliang, 1988. "Stochastic integrals of empirical-type processes with applications to censored regression," Journal of Multivariate Analysis, Elsevier, vol. 27(2), pages 334-358, November.
    7. Ronald B. Geskus, 2011. "Cause-Specific Cumulative Incidence Estimation and the Fine and Gray Model Under Both Left Truncation and Right Censoring," Biometrics, The International Biometric Society, vol. 67(1), pages 39-49, March.
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