When do we need competing risks methods for survival analysis in nephrology?

TLDR

Survival analyses are routinely used to study death or other events, but when competing risks—events that either preclude or alter the likelihood of the event of interest—are present, conventional methods such as Kaplan–Meier and Cox regression become inappropriate. This article aims to highlight the importance of competing‑risk methods in nephrology and to explain how different analytical techniques can influence study outcomes. The authors compare standard Kaplan–Meier and Cox models with competing‑risk–specific approaches, arguing that the latter should be employed when competing events are present. They show that using competing‑risk methods can change the interpretation of survival data compared to conventional analyses.

Abstract

Survival analyses are commonly applied to study death or other events of interest. In such analyses, so-called competing risks may form an important problem. A competing risk is an event that either hinders the observation of the event of interest or modifies the chance that this event occurs. For example, when studying death on dialysis, receiving a kidney transplant is an event that competes with the event of interest. Conventional methods for survival analysis ignoring the competing event(s), such as the Kaplan–Meier method and standard Cox proportional hazards regression, may be inappropriate in the presence of competing risks, and alternative methods specifically designed for analysing competing risks data should then be applied. This problem deserves more attention in nephrology research and in the current article, we therefore explain the problem of competing risks in survival analysis and how using different techniques may affect study results.