Methods of competing risks analysis of end-stage renal disease and mortality among people with diabetes
1 Department of Community Health & Epidemiology College of Medicine, University of Saskatchewan 107 Wiggins Road Saskatoon, SK S7N 5E5, Canada
2 Department of Mathematics &Statistics Georgia State University 750 COE, 7th floor, 30 Pryor Street Atlanta, Georgia 30303, USA
3 Department of Medicine, University of Saskatchewan 103 Hospital Drive Saskatoon, SK S7J 5B6, Canada
4 Department of Computer Science University of Saskatchewan 110 Science Place Saskatoon, SK S7N 5C9, Canada
BMC Medical Research Methodology 2010, 10:97 doi:10.1186/1471-2288-10-97Published: 21 October 2010
When a patient experiences an event other than the one of interest in the study, usually the probability of experiencing the event of interest is altered. By contrast, disease-free survival time analysis by standard methods, such as the Kaplan-Meier method and the standard Cox model, does not distinguish different causes in the presence of competing risks. Alternative approaches use the cumulative incidence estimator by the Cox models on cause-specific and on subdistribution hazards models. We applied cause-specific and subdistribution hazards models to a diabetes dataset with two competing risks (end-stage renal disease (ESRD) or death without ESRD) to measure the relative effects of covariates and cumulative incidence functions.
In this study, the cumulative incidence curve of the risk of ESRD by the cause-specific hazards model was revealed to be higher than the curves generated by the subdistribution hazards model. However, the cumulative incidence curves of risk of death without ESRD based on those three models were very similar.
In analysis of competing risk data, it is important to present both the results of the event of interest and the results of competing risks. We recommend using either the cause-specific hazards model or the subdistribution hazards model for a dominant risk. However, for a minor risk, we do not recommend the subdistribution hazards model and a cause-specific hazards model is more appropriate. Focusing the interpretation on one or a few causes and ignoring the other causes is always associated with a risk of overlooking important features which may influence our interpretation.