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Competing risks in survival data analysis

  • Almut Dutz
    Affiliations
    OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany

    Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology – OncoRay, Dresden, Germany
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  • Steffen Löck
    Correspondence
    Corresponding author at: OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstr. 74, 01307 Dresden, Germany.
    Affiliations
    OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany

    German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany

    Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
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Published:October 09, 2018DOI:https://doi.org/10.1016/j.radonc.2018.09.007

      Abstract

      Clinical trials and retrospective studies in the field of radiation oncology often consider time-to-event data as their primary endpoint. Such studies are susceptible to competing risks, i.e. competing events may preclude the occurrence of the event of interest or modify the chance that the primary endpoint occurs. Competing risks are frequently neglected and the event of interest is analysed with standard statistical methods. Here, we would like to create awareness of the problem and demonstrate different methods for survival data analysis in the presence of competing risks.

      Keywords

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      References

        • Lee E.T.
        • Go O.T.
        Survival analysis in public health research.
        Annu Rev Public Health. 1997; 18: 105-134
        • Satagopan J.M.
        • Ben-Porat L.
        • Berwick M.
        • Robson M.
        • Kutler D.
        • Auerbach A.D.
        A note on competing risks in survival data analysis.
        Br J Cancer. 2004; 91: 1229-1235
        • Varadhan R.
        • Weiss C.O.
        • Segal J.B.
        • Wu A.W.
        • Scharfstein D.
        • Boyd C.
        Evaluating health outcomes in the presence of competing risks: a review of statistical methods and clinical applications.
        Med Care. 2010; 48: S96-S105
        • Noordzij M.
        • Leffondré K.
        • van Stralen K.J.
        • Zoccali C.
        • Dekker F.W.
        • Jager K.J.
        When do we need competing risks methods for survival analysis in nephrology?.
        Nephrol Dial Transplant. 2013; 28: 2670
        • van Walraven C.
        • McAlister F.A.
        Competing risk bias was common in Kaplan-Meier risk estimates published in prominent medical journals.
        J Clin Epidemiol. 2016; 69e8
        • Austin P.C.
        • Lee D.S.
        • Fine J.P.
        Introduction to the analysis of survival data in the presence of competing risks.
        Circulation. 2016; 133: 601-609
        • Wolbers M.
        • Koller M.T.
        • Stel V.S.
        • et al.
        Competing risks analyses: objectives and approaches.
        Eur Heart J. 2014; 35: 2936
        • Verduijn M.
        • Grootendorst D.C.
        • Dekker F.W.
        • Jager K.J.
        • le Cessie S.
        The analysis of competing events like cause-specific mortality – beware of the Kaplan-Meier method.
        Nephrol Dial Transplant. 2011; 26: 56
        • Caplan R.J.
        • Pajak T.F.
        • Cox J.D.
        Analysis of the probability and risk of cause-specific failure.
        Int J Radiat Oncol Biol Phys. 1994; 29: 1183-1186
        • Scrucca L.
        • Santucci A.
        • Aversa F.
        Competing risk analysis using R: an easy guide for clinicians.
        Bone Marrow Transplant. 2007; 40: 381-387
        • Pintilie M.
        An introduction to competing risks analysis.
        Rev Esp Cardiol (Engl Ed). 2011; 64: 599-605
        • Wongworawat M.D.
        • Dobbs M.B.
        • Gebhardt M.C.
        • et al.
        Editorial: estimating survivorship in the face of competing risks.
        Clin Orthop. 2015; 473: 1173-1176
        • Dok R.
        • Nuyts S.
        HPV positive head and neck cancers: molecular pathogenesis and evolving treatment strategies.
        Cancers. 2016; 8: 41
        • Koller M.T.
        • Raatz H.
        • Steyerberg E.W.
        • Wolbers M.
        Competing risks and the clinical community: irrelevance or ignorance?.
        Stat Med. 2012; 31: 1089-1097
        • Schumacher M.
        • Ohneberg K.
        • Beyersmann J.
        Competing risk bias was common in a prominent medical journal.
        J Clin Epidemiol. 2016; 80: 135-136
        • Gooley T.A.
        • Leisenring W.
        • Crowley J.
        • Storer B.E.
        Estimation of failure probabilities in the presence of competing risks: new representations of old estimators.
        Stat Med. 1999; 18: 695-706
        • Dignam J.J.
        • Zhang Q.
        • Kocherginsky M.
        The use and interpretation of competing risks regression models.
        Clin Cancer Res. 2012; 18: 2301-2308
        • Lohaus F.
        • Linge A.
        • Tinhofer I.
        • et al.
        HPV16 DNA status is a strong prognosticator of loco-regional control after postoperative radiochemotherapy of locally advanced oropharyngeal carcinoma: Results from a multicentre explorative study of the German Cancer Consortium Radiation Oncology Group (DKTK-ROG).
        Radiother Oncol. 2014; 113: 317-323
        • Linge A.
        • Lohaus F.
        • Löck S.
        • et al.
        HPV status, cancer stem cell marker expression, hypoxia gene signatures and tumour volume identify good prognosis subgroups in patients with HNSCC after primary radiochemotherapy: A multicentre retrospective study of the German Cancer Consortium Radiation Oncology Group (DKTK-ROG).
        Radiother Oncol. 2016; 121: 364-373
        • Latouche A.
        • Allignol A.
        • Beyersmann J.
        • Labopin M.
        • Fine J.P.
        A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions.
        J Clin Epidemiol. 2013; 66: 648-653
        • Kim H.T.
        Cumulative incidence in competing risks data and competing risks regression analysis.
        Clin Cancer Res. 2007; 13: 559-565
        • Wolbers M.
        • Koller M.T.
        • Witteman J.C.M.
        • Steyerberg E.W.
        Prognostic models with competing risks: methods and application to coronary risk prediction.
        Epidemiology. 2009; 20: 555-561
        • Wolkewitz M.
        • Cooper B.S.
        • Bonten M.J.M.
        • Barnett A.G.
        • Schumacher M.
        Interpreting and comparing risks in the presence of competing events.
        Br Med J. 2014; 349
        • Gray R.J.
        A class of K-sample tests for comparing the cumulative incidence of a competing risk.
        Ann Stat. 1988; 16: 1141-1154
        • Mantel N.
        Evaluation of survival data and two rank order statistics in its consideration.
        Cancer Chemother Rep. 1966; 50: 163-170
        • Fine J.P.
        • Gray R.J.
        A proportional hazards model for the subdistribution of a competing risk.
        J Am Stat Assoc. 1999; 94: 496-509
        • Chappell R.
        Competing risk analyses: how are they different and why should you care?.
        Clin Cancer Res. 2012; 18: 2127-2129
        • Scrucca L.
        • Santucci A.
        • Aversa F.
        Regression modeling of competing risk using R: an in depth guide for clinicians.
        Bone Marrow Transplant. 2010; 45: 1388
        • Dignam J.J.
        • Kocherginsky M.N.
        Choice and interpretation of statistical tests used when competing risks are present.
        J Clin Oncol. 2008; 26: 4027-4034