Box 1.

Competing risks: five key points

Item	Description
1	Competing risks occur if the time to a specific event is of interest but other types of events may preclude the occurrence of that event. More generally, competing risks methods can be used if different types of events are studied and the focus is on the time and type of the first event.
2	The basic descriptive statistic for competing risks data is the cumulative incidence function (CIF) which describes the absolute risk of an event of interest over time. The Kaplan–Meier method should not be used in the presence of competing events as it over-estimates the true absolute risk.
3	A complication of competing risks is that covariates can affect the absolute risk and the rate of an event of interest differently. Regression models based on the CIF (e.g. Fine-Gray models) explore the association between covariates and the absolute risk and are therefore essential for medical decision-making and prognostic research questions. Cause specific models for event rates (e.g. Cox proportional cause-specific hazards models) on the other hand are to be preferred for answering aetiological research questions.
4	A complete description of competing risks data should include the modelling of all event types and not only of the event of main interest.
5	Competing risks models can assess the effect of an intervention on individual components of a composite endpoint.