Understanding Competing Risks

Abstract

Highlights: • Clinicians and researchers need to be cognizant of competing risks (ie, the occurrence of an event that precludes future development of the outcome of interest). • Censoring is appropriate when a patient being censored at a particular time (eg, owing to loss to follow-up) is deemed to have approximately the same future risk of developing the outcome of interest as patients still being followed, and when the reason for censoring is not related to events of interest. • Censoring is not appropriate in many scenarios; for example, a dead patient can never develop a future cancer recurrence or late effect. • Kaplan-Meier (KM) analysis is commonly used for a time-to-event analysis when the primary outcome (eg, overall survival) has no meaningful competing risk and censoring is appropriate. Cox proportional hazards modeling can be used for multivariable analysis to examine the association between covariates and the primary outcome. • In the presence of competing events, incorrect use of the KM analysis will likely lead to an overestimation of the risk of the outcome of interest and biased estimates from both univariable and multivariable analyses. Therefore, KM usually should not be used to determine local recurrence risk because humanity is always at risk for dying, which is a competing risk. • A competing risk analysis is therefore most appropriate when there are competing events to the primary outcome of interest. The most commonly used multivariable model in these situations is the Fine-Gray model. • Many fundamental questions in radiation oncology—local and regional recurrence, treatment-related toxicity—are best addressed using competing risk methodologies.