abstract
- Competing events (or risks) preclude the observation of an event of interest or alter the probability of the event's occurrence and are commonly encountered in transplant outcomes research. Transplantation, for example, is a competing event for death on the waiting list because receiving a transplant may significantly decrease the risk of long-term mortality. In a typical analysis of time-to-event data, competing events may be censored or incorporated into composite end points; however, the presence of competing events violates the assumption of "independent censoring," which is the basis of standard survival analysis techniques. The use of composite end points disregards the possibility that competing events may be related to the exposure in a way that is different from the other components of the composite. Using data from the Scientific Registry of Transplant Recipients, this paper reviews the principles of competing risks analysis; outlines approaches for analyzing data with competing events (cause-specific and subdistribution hazards models); compares the estimates obtained from standard survival analysis, which handle competing events as censoring events; discusses the appropriate settings in which each of the two approaches could be used; and contrasts their interpretation.