Applying competing risks regression models: An overview

Bernhard Haller, Georg Schmidt, Kurt Ulm

Research output: Contribution to journalArticlepeer-review

118 Scopus citations

Abstract

In many clinical research applications the time to occurrence of one event of interest, that may be obscured by another-so called competing-event, is investigated. Specific interventions can only have an effect on the endpoint they address or research questions might focus on risk factors for a certain outcome. Different approaches for the analysis of time-to-event data in the presence of competing risks were introduced in the last decades including some new methodologies, which are not yet frequently used in the analysis of competing risks data. Cause-specific hazard regression, subdistribution hazard regression, mixture models, vertical modelling and the analysis of time-to-event data based on pseudo-observations are described in this article and are applied to a dataset of a cohort study intended to establish risk stratification for cardiac death after myocardial infarction. Data analysts are encouraged to use the appropriate methods for their specific research questions by comparing different regression approaches in the competing risks setting regarding assumptions, methodology and interpretation of the results. Notes on application of the mentioned methods using the statistical software R are presented and extensions to the presented standard methods proposed in statistical literature are mentioned.

Original languageEnglish
Pages (from-to)33-58
Number of pages26
JournalLifetime Data Analysis
Volume19
Issue number1
DOIs
StatePublished - Dec 2012

Keywords

  • Cause-specific hazard
  • Competing risks
  • Mixture model
  • Pseudo-observation approach
  • Subdistribution hazard
  • Vertical modelling

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