Abstract
Modern competing risks analysis has 2 primary goals in clinical epidemiology: to maximize the clinician’s knowledge of etiologic associations existing between potential predictor variables and various cause-specific outcomes via cause-specific hazard models, and to maximize the clinician’s knowledge of noteworthy differences existing in cause-specific patient risk via cause-specific subdistribution hazard models (cumulative incidence functions). A perfect application exists in analyzing 4 distinct outcomes following listing for a deceased donor kidney transplant(DDKT): receiving a DDKT, receiving a living donor kidney transplant, waitlist removal due to patient mortality or a deteriorating medical condition, and waitlist removal due to other reasons. It is important to realize that obtaining a complete understanding of subdistribution hazard ratios is simply not possible without first having knowledge of the multivariable relationships existing between the potential predictor variables and the cause-specific hazards (Perspective #1), as the cause-specific hazards form the “building blocks” of cumulative incidence functions. Additionally, while we believe that a worthy and practical alternative to estimating the median waiting-time-to DDKT is to ask, “what is the conditional probability of the patient receiving a DDKT, given that he/she would not previously experience one of the competing events (known as the cause-specific conditional failure probability),” only an appropriate estimator of this conditional type of cumulative incidence should be used (Perspective #2). One suggested estimator, the well-known “one minus Kaplan-Meier” approach (censoring competing events), simply does not represent any probability in the presence of competing risks and will almost always produce biased estimates (thus, it should never be used).