Abstract

The concept of competing risks is particularly relevant to survival analyses of diabetic ESRD given the high likelihood of death prior to ESRD. Approaches such as Kaplan-Meier curves and Cox regression models operate on the assumption that there are no competing risks for the event of interest, yielding uninterpretable and generally biased estimates in the presence of competing risks. The cumulative incidence function and Fine-Gray regression are more appropriate methodologies for survival analysis when competing risks are present. We present an example taken from the Action to Control Cardiovascular Risk in Diabetes, a randomized trial of people with type 2 diabetes at high risk for cardiovascular disease. Participants were stratified according to baseline markers of kidney disease: (1) no kidney disease; (2) low estimated glomerular filtration rate; (3) microalbuminuria alone; and (4) macroalbuminuria. The macroalbuminuria group had the highest risk for ESRD and demonstrated the most marked difference between the Kaplan-Meier and cumulative incidence estimator. Cox and Fine-Gray regression models yielded similar risk estimates for baseline characteristics, with the exception of diabetes duration, which was significant in the Cox but not Fine-Gray model. We underscore the importance of competing risk methods, particularly when the competing risk is common, as is the case in diabetic kidney disease.