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Joint inference for competing risks data using multiple endpoints

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  • Jiyang Wen
  • Chen Hu
  • Mei‐Cheng Wang

Abstract

Competing risks data are commonly encountered in randomized clinical trials and observational studies. This paper considers the situation where the ending statuses of competing events have different clinical interpretations and/or are of simultaneous interest. In clinical trials, often more than one competing event has meaningful clinical interpretations even though the trial effects of different events could be different or even opposite to each other. In this paper, we develop estimation procedures and inferential properties for the joint use of multiple cumulative incidence functions (CIFs). Additionally, by incorporating longitudinal marker information, we develop estimation and inference procedures for weighted CIFs and related metrics. The proposed methods are applied to a COVID‐19 in‐patient treatment clinical trial, where the outcomes of COVID‐19 hospitalization are either death or discharge from the hospital, two competing events with completely different clinical implications.

Suggested Citation

  • Jiyang Wen & Chen Hu & Mei‐Cheng Wang, 2023. "Joint inference for competing risks data using multiple endpoints," Biometrics, The International Biometric Society, vol. 79(3), pages 1635-1645, September.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:1635-1645
    DOI: 10.1111/biom.13752
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    References listed on IDEAS

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    1. John Bryant & James J. Dignam, 2004. "Semiparametric Models for Cumulative Incidence Functions," Biometrics, The International Biometric Society, vol. 60(1), pages 182-190, March.
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    4. Min Zhang & Douglas E. Schaubel, 2011. "Estimating Differences in Restricted Mean Lifetime Using Observational Data Subject to Dependent Censoring," Biometrics, The International Biometric Society, vol. 67(3), pages 740-749, September.
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