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Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms

Author

Listed:
  • Judith J. Lok

    (Harvard School of Public Health)

  • Shu Yang

    (North Carolina State University)

  • Brian Sharkey

    (Incyte)

  • Michael D. Hughes

    (Harvard School of Public Health)

Abstract

Competing risks occur in a time-to-event analysis in which a patient can experience one of several types of events. Traditional methods for handling competing risks data presuppose one censoring process, which is assumed to be independent. In a controlled clinical trial, censoring can occur for several reasons: some independent, others dependent. We propose an estimator of the cumulative incidence function in the presence of both independent and dependent censoring mechanisms. We rely on semi-parametric theory to derive an augmented inverse probability of censoring weighted (AIPCW) estimator. We demonstrate the efficiency gained when using the AIPCW estimator compared to a non-augmented estimator via simulations. We then apply our method to evaluate the safety and efficacy of three anti-HIV regimens in a randomized trial conducted by the AIDS Clinical Trial Group, ACTG A5095.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:lifeda:v:24:y:2018:i:2:d:10.1007_s10985-017-9393-4
    DOI: 10.1007/s10985-017-9393-4
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    References listed on IDEAS

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    1. Jong‐Hyeon Jeong & Jason Fine, 2006. "Direct parametric inference for the cumulative incidence function," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 187-200, April.
    2. John Bryant & James J. Dignam, 2004. "Semiparametric Models for Cumulative Incidence Functions," Biometrics, The International Biometric Society, vol. 60(1), pages 182-190, March.
    3. 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.
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    Cited by:

    1. 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.
    2. Ran Dai & Cheng Zheng & Mei-Jie Zhang, 2023. "On High-Dimensional Covariate Adjustment for Estimating Causal Effects in Randomized Trials with Survival Outcomes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 242-260, April.

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