Prognostic models with competing risks: methods and application to coronary risk prediction

Epidemiology. 2009 Jul;20(4):555-61. doi: 10.1097/EDE.0b013e3181a39056.


Clinical decision-making often relies on a subject's absolute risk of a disease event of interest. However, in a frail population, competing risk events may preclude the occurrence of the event of interest. We review competing-risk regression models with a view toward predictive modeling. We show how measures of prognostic performance (such as calibration and discrimination) can be adapted to the competing-risks setting. An example of coronary heart disease (CHD) prediction in women aged 55-90 years in the Rotterdam study is used to illustrate the proposed methods, and to compare the Fine and Gray regression model to 2 alternative approaches: (1) a standard Cox survival model, which ignores the competing risk of non-CHD death, and (2) a cause-specific hazards model, which combines proportional hazards models for the event of interest and the competing event. The Fine and Gray model and the cause-specific hazards model perform similarly. However, the standard Cox model substantially overestimates 10-year risk of CHD; it classifies 18% of the individuals as high risk (>20%), compared with only 8% according to the Fine and Gray model. We conclude that competing risks have to be considered explicitly in frail populations such as the elderly.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Coronary Artery Disease / epidemiology*
  • Female
  • Humans
  • Middle Aged
  • Netherlands / epidemiology
  • Prognosis
  • Proportional Hazards Models
  • Risk Assessment / statistics & numerical data