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Analysis of failure time data under competing censoring mechanisms

Author

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  • Andrea Rotnitzky
  • Andres Farall
  • Andrea Bergesio
  • Daniel Scharfstein

Abstract

Summary. We derive estimators of the survival curve of a failure time in the presence of competing right censoring mechanisms. Our approach allows for the possibility that some or all of the competing censoring mechanisms are associated with the end point, even after adjustment for recorded prognostic factors. It also allows the degree of residual association to be possibly different for distinct censoring processes. Our methods generalize from one to several competing censoring mechanisms the methods of Scharfstein and Robins.

Suggested Citation

  • Andrea Rotnitzky & Andres Farall & Andrea Bergesio & Daniel Scharfstein, 2007. "Analysis of failure time data under competing censoring mechanisms," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(3), pages 307-327, June.
    • Handle: RePEc:bla:jorssb:v:69:y:2007:i:3:p:307-327
    DOI: 10.1111/j.1467-9868.2007.00590.x
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    Cited by:

    1. Shu Yang & Yilong Zhang & Guanghan Frank Liu & Qian Guan, 2023. "SMIM: A unified framework of survival sensitivity analysis using multiple imputation and martingale," Biometrics, The International Biometric Society, vol. 79(1), pages 230-240, March.
    2. Judith J. Lok & Shu Yang & Brian Sharkey & Michael D. Hughes, 2018. "Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(2), pages 201-223, April.
    3. Shu Yang, 2022. "Semiparametric estimation of structural nested mean models with irregularly spaced longitudinal observations," Biometrics, The International Biometric Society, vol. 78(3), pages 937-949, September.
    4. Paul Frédéric Blanche & Anders Holt & Thomas Scheike, 2023. "On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 441-482, April.
    5. Ungolo, Francesco & van den Heuvel, Edwin R., 2024. "A Dirichlet process mixture regression model for the analysis of competing risk events," Insurance: Mathematics and Economics, Elsevier, vol. 116(C), pages 95-113.
    6. Sujatro Chakladar & Samuel Rosin & Michael G. Hudgens & M. Elizabeth Halloran & John D. Clemens & Mohammad Ali & Michael E. Emch, 2022. "Inverse probability weighted estimators of vaccine effects accommodating partial interference and censoring," Biometrics, The International Biometric Society, vol. 78(2), pages 777-788, June.

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