Prognostic models with competing risks: methods and application to coronary risk prediction.
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.
Wolbers
Marcel
M
Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, Basel, Switzerland. mwolbers@oucru.org
Koller
Michael T
MT
Witteman
Jacqueline C M
JC
Steyerberg
Ewout W
EW
D000368:Aged; D000369:Aged, 80 and over; D003324:Coronary Artery Disease / Q000453:epidemiology*; D005260:Female; D006801:Humans; D008875:Middle Aged; D009426:Netherlands / Q000453:epidemiology; D011379:Prognosis; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000706:statistics & numerical data