Statistical techniques such as Kaplan-Meier estimate is commonly used and interpreted as the probability of failure in time-to-event data. When used on biomedical survival data, patients who fail from unrelated or other causes (competing events) are often treated as censored observations. This paper reviews and compares two methods of estimating cumulative probability of cause-specific events in the present of other competing events: 1 minus Kaplan-Meier and cumulative incidence estimators. A subset of a breast cancer data with three competing events: recurrence, second primary cancers, and death, was used to illustrate the different estimates given by 1 minus Kaplan-Meier and cumulative incidence function. Recurrence of breast cancer was the event of interest and second primary cancers and deaths were competing risks.The results indicate that the cumulative incidences gives an appropriate estimates and 1 minus Kaplan-Meier overestimates the cumulative probability of cause-specific failure in the presence of competing events. In absence of competing events, the 1 minus Kaplan-Meier approach yields identical estimates as the cumulative incidence function.The public health relevance of this paper is to help researchers understand that competing events affect the cumulative probability of cause-specific events. Researchers should use methods such as the cumulative incidence function to correctly estimate and compare the cause-specific cumulative probabilities
