The number needed to treat is a tool often used in clinical settings to illustrate the effect of a treatment. It has been widely adopted in the communication of risks to both clinicians and non-clinicians, such as patients, who are better able to understand this measure than absolute risk or rate reductions. The concept was introduced by Laupacis, Sackett, and Roberts in 1988 for binary data, and extended to time-to-event data by Altman and Andersen in 1999. However, up to the present, there is no definition of the number needed to treat for time-to-event data with competing risks. This paper introduces such a definition using the cumulative incidence function and suggests non-parametric and semi-parametric inferential methods for right-censored time-to-event data in the presence of competing risks. The procedures are illustrated using the data from a breast cancer clinical trial. Copyright © 2013 John Wiley & Sons, Ltd.