title: Understanding competing risks: a simulation point of view.
abstract: BACKGROUND
  Competing risks methodology allows for an event-specific analysis of the single components of composite time-to-event endpoints. A key feature of competing risks is that there are as many hazards as there are competing risks. This is not always well accounted for in the applied literature.
  
  
  METHODS
  We advocate a simulation point of view for understanding competing risks. The hazards are envisaged as momentary event forces. They jointly determine the event time. Their relative magnitude determines the event type. 'Empirical simulations' using data from a recent study on cardiovascular events in diabetes patients illustrate subsequent interpretation. The method avoids concerns on identifiability and plausibility known from the latent failure time approach.
  
  
  RESULTS
  The 'empirical simulations' served as a proof of concept. Additionally manipulating baseline hazards and treatment effects illustrated both scenarios that require greater care for interpretation and how the simulation point of view aids the interpretation. The simulation algorithm applied to real data also provides for a general tool for study planning.
  
  
  CONCLUSIONS
  There are as many hazards as there are competing risks. All of them should be analysed. This includes estimation of baseline hazards. Study planning must equally account for these aspects.
authors.affiliation: Freiburg Center for Data Analysis and Modeling, University of Freiburg, Germany. arthur.allignol@fdm.uni-freiburg.de
authors.forename: Arthur
authors.initials: A
authors.lastname: Allignol
authors.forename: Martin
authors.initials: M
authors.lastname: Schumacher
authors.forename: Christoph
authors.initials: C
authors.lastname: Wanner
authors.forename: Christiane
authors.initials: C
authors.lastname: Drechsler
authors.forename: Jan
authors.initials: J
authors.lastname: Beyersmann
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016001:Confidence Intervals; D006801:Humans; D016016:Proportional Hazards Models; D012306:Risk; D016019:Survival Analysis