Ignoring competing events in the analysis of survival data may lead to biased results: a non-mathematical illustration of competing risk analysis

Objective: Competing events are often ignored in epidemiological studies. Conventional methods for the analysis of survival data assume independent or noninformative censoring, which is violated when subjects that experience a competing event are censored. Because many survival studies do not apply competing risk analysis, we explain and illustrate in a nonmathematical way how to analyze and interpret survival data in the presence of competing events. Study Design and Setting: Using data from the Longitudinal Aging Study Amsterdam, both marginal analyses (Kaplan–Meier method and Cox proportional-hazards regression) and competing risk analyses (cumulative incidence function [CIF], cause-specific and subdistribution hazard regression) were performed. We analyzed the association between sex and depressive symptoms, in which death before the onset of depression was a competing event. Results: The Kaplan–Meier method overestimated the cumulative incidence of depressive symptoms. Instead, the CIF should be used. As the subdistribution hazard model has a one-to-one relation with the CIF, it is recommended for prediction research, whereas the cause-specific hazard model is recommended for etiologic research. Conclusion: When competing risks are present, the type of research question guides the choice of the analytical model to be used. In any case, results should be presented for all event types