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Competing Risk Models

  • Melania PintilieEmail author
Reference work entry
Part of the Health Services Research book series (HEALTHSR)

Abstract

In the time-to-event analysis when more than one type of event can occur and not all are of interest, the situation of competing risks appears. In this chapter the competing risks will be defined, and the need for special statistical analysis techniques will be justified. The methodology for estimation and modeling in the presence of competing risks will be presented. The cumulative incidence function and the Fine and Gray model will be introduced as the main methods to analyze competing risks data. The cumulative incidence function will be contrasted to Kaplan-Meier method. For a deeper understanding of the modelling, the subdistribution hazard will be defined.

The importance of considering the competing risks in the process of designing a study will be emphasized, and the steps needed to be taken in the calculation will be presented. For a better understanding of the material and of the interpretation, examples will be given at each step.

References

  1. Barlow EW, Ichikawa L, Rosner D, Izumi S. Analysis of case-cohort design. J Clin Epidemiol. 1999;52(12):1165–72.CrossRefGoogle Scholar
  2. David HA, Moeschberger ML. The theory of competing risks. London: Griffin; 1978.Google Scholar
  3. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999;94:496–509.CrossRefGoogle Scholar
  4. Gooley TA, Leisenring W, Crowley J, Storer BE. Estimation of failure probabilities in the presence of competing risks: new representations of old estimators. Stat Med. 1999;18:695–706.CrossRefGoogle Scholar
  5. Kalbfleisch JD, Prentice RL. The statistical analysis of failure time data. New York: John Wiley & Sons, Inc.; 1980.Google Scholar
  6. Lee EW, Wei LJ, Amato D, Leurgans S. Cox-type regression analysis for large numbers of small groups of correlated failure time observations. In: Klein JP, Goel PK, editors. Survival analysis: state of the art. Dordrecht: Kluwer; 1992.Google Scholar
  7. Maida V, Corbo M, Dolzhykov M, Ennis M, Irani S, Trozzolo L. Wounds in advanced illness: a prevalence and incidence study based on a prospective case series. Int Wound J. 2008;5(2):305–14.CrossRefGoogle Scholar
  8. Maida V, Ennis M, Corban J. Wound outcomes in patients with advanced illness. Int Wound J. 2012;9(6):683–92.CrossRefGoogle Scholar
  9. Pintilie M. Competing risks a practical perspective. Chichester: Wiley & Sons Ltd; 2006.CrossRefGoogle Scholar
  10. Pintilie M, Bai Y, Yun LS, Hodgson DC. The analysis of case cohort design in the presence of competing risks with application to estimate the risk of delayed cardiac toxicity among Hodgkin Lymphoma survivors. Stat Med. 2010;29(27):2802–10.CrossRefGoogle Scholar
  11. Tsiatis A. Nonidentifiability aspect of problem of competing risks. Proc Natl Acad Sci U S A. 1975;72:20–2.CrossRefGoogle Scholar
  12. Zhou BQ, Latouche A, Rocha V, Fine J. Competing risks regression for stratified data. Biostatistics. 2011;67(2):661–70.Google Scholar
  13. Zhou BQ, Fine J, Latouche A, Labopin M. Competing risks regression for clustered data. Biostatistics. 2012;13(3):371–83.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University Health NetworkTorontoCanada

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