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Competing Risks Regression for Stratified Data

Authors

  • Bingqing Zhou,

    Corresponding author
    1. Division of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut 06520, U.S.A.
      email: bingqing.zhou@yale.edu
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  • Aurelien Latouche,

    Corresponding author
    1. Université Versailles St-Quentin, EA2506, Versailles, France
      email: aurelien.latouche@uvsq.fr
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  • Vanderson Rocha,

    Corresponding author
    1. Acute Leukemia Working Party and Eurocord, European Blood and Marrow Transplant Group, Hopital Saint Louis, Paris University, 75010 Paris, France
      email: vanderson.rocha@sls.aphp.fr
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  • Jason Fine

    Corresponding author
    1. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7420, U.S.A.
      email: bingqing.zhou@yale.edu
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email:bingqing.zhou@yale.edu

email:aurelien.latouche@uvsq.fr

email:vanderson.rocha@sls.aphp.fr

email:jfine@bios.unc.edu

Abstract

Summary For competing risks data, the Fine–Gray proportional hazards model for subdistribution has gained popularity for its convenience in directly assessing the effect of covariates on the cumulative incidence function. However, in many important applications, proportional hazards may not be satisfied, including multicenter clinical trials, where the baseline subdistribution hazards may not be common due to varying patient populations. In this article, we consider a stratified competing risks regression, to allow the baseline hazard to vary across levels of the stratification covariate. According to the relative size of the number of strata and strata sizes, two stratification regimes are considered. Using partial likelihood and weighting techniques, we obtain consistent estimators of regression parameters. The corresponding asymptotic properties and resulting inferences are provided for the two regimes separately. Data from a breast cancer clinical trial and from a bone marrow transplantation registry illustrate the potential utility of the stratified Fine–Gray model.

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