%0 Journal Article %1 Wolbers:2009:Epidemiology:19367167 %A Wolbers, M %A Koller, M T %A Witteman, J C %A Steyerberg, E W %D 2009 %J Epidemiology %K CompetingRisks Multi-stateModels SurvivalAnalysis statistics %N 4 %P 555-561 %R 10.1097/EDE.0b013e3181a39056 %T Prognostic models with competing risks: methods and application to coronary risk prediction %U https://www.ncbi.nlm.nih.gov/pubmed/19367167 %V 20 %X 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.

@article{Wolbers:2009:Epidemiology:19367167, abstract = {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.}, added-at = {2018-10-03T04:08:41.000+0200}, author = {Wolbers, M and Koller, M T and Witteman, J C and Steyerberg, E W}, biburl = {https://www.bibsonomy.org/bibtex/2b0885227110d01286bc67e9cd37cd3a6/jkd}, description = {Prognostic models with competing risks: methods and application to coronary risk prediction. - PubMed - NCBI}, doi = {10.1097/EDE.0b013e3181a39056}, interhash = {486885794f262d24fa8824df3fe99bee}, intrahash = {b0885227110d01286bc67e9cd37cd3a6}, journal = {Epidemiology}, keywords = {CompetingRisks Multi-stateModels SurvivalAnalysis statistics}, month = jul, number = 4, pages = {555-561}, pmid = {19367167}, timestamp = {2018-10-03T04:08:41.000+0200}, title = {Prognostic models with competing risks: methods and application to coronary risk prediction}, url = {https://www.ncbi.nlm.nih.gov/pubmed/19367167}, volume = 20, year = 2009 }