Competing Risks Analysis of Kidney Transplant Waitlist Outcomes: Two Important Statistical Perspectives

• Published in: Kidney international reports 2024-06, Vol.9 (6), p.1580-1589
• Main Authors: Gaynor, Jeffrey J., Guerra, Giselle, Vianna, Rodrigo, Tabbara, Marina M., Graell, Enric Lledo, Ciancio, Gaetano
• Format: Article
• Language: English
• Subjects: cause-specific hazard rates; cause-specific waiting time-to-event distributions following kidney transplant waitlisting; conditional cumulative incidence; cumulative incidence; modern competing risks analysis; Review
• ISSN: 2468-0249; 2468-0249
• DOI: 10.1016/j.ekir.2024.01.050

Modern competing risks analysis has 2 primary goals in clinical epidemiology as follows: (i) 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 (ii) to maximize the clinician’s knowledge of noteworthy differences existing in cause-specific patient risk via cause-specific subdistribution hazard models (cumulative incidence functions [CIFs]). A perfect application exists in analyzing the following 4 distinct outcomes after listing for a deceased donor kidney transplant (DDKT): (i) receiving a DDKT, (ii) receiving a living donor kidney transplant (LDKT), (iii) waitlist removal due to patient mortality or a deteriorating medical condition, and (iv) waitlist removal due to other reasons. It is important to realize that obtaining a complete understanding of subdistribution hazard ratios (HRs) 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), because the cause-specific hazards form the “building blocks” of CIFs. In addition, though 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 or 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).