In cohort studies with competing risks, which method should be used to estimate the probability of the event of interest over time?

• A. Cumulative incidence function
• B. Kaplan-Meier estimator
• C. Log-rank test
• D. Cox proportional hazards model without competing risks adjustment

Answer: (A)

When other events can prevent the event of interest from happening, you need a method that directly gives the probability of that specific event occurring by each time point, accounting for those competing risks. The cumulative incidence function does exactly this: it provides the probability that the event of interest has occurred by time t, in the presence of competing events.

Using a Kaplan-Meier estimator treats competing events as if individuals were simply censored, which assumes they could still experience the event of interest if followed longer. This inflates the estimated probability and misrepresents the true risk under competing risks. The log-rank test is a tool for comparing survival curves, not for estimating the actual probability of a particular event when competing risks are present. A Cox model without competing risks adjustment estimates hazard differences for the cause of interest but does not yield the true cumulative probability by time, because it ignores how competing events alter the risk set.

So, the cumulative incidence function is the appropriate choice for estimating the probability of the event of interest over time in cohorts with competing risks. If you later want to assess covariate effects on that probability, you would move to models like the Fine-Gray subdistribution hazard model, which is built for CIF interpretation.