Abstract

Competing risks arise when people are at risk of multiple mutually exclusive events, such that the occurrence of one event alters the probability of others. In research, ignoring competing risks can lead to biased estimates. We outline key approaches for analyzing competing risk data, focusing on their assumptions, interpretations, and epidemiological and clinical relevance. The Aalen-Johansen estimator, a non-parametric method for estimating the cumulative incidence function, provides an alternative to the naïve Kaplan-Meier estimator when competing events are present. The cause-specific hazard model estimates the instantaneous risk of a specific event type, treating competing events as censored, and is used for etiologic research. The Fine-Gray subdistribution hazard model directly models the cumulative incidence function, thus offering a clinically interpretable measure of absolute risk. We also discuss the use of composite endpoints, which combine several event types to increase statistical power, and highlight their limitations in clinical interpretation. By comparing these methods and illustrating their applications through analyses of the association between venous thromboembolism and arterial events, this review aims to guide researchers, particularly junior researchers, in selecting appropriate strategies for valid and meaningful analysis of competing risks in clinical and epidemiological studies.