We propose a method for estimating and comparing proportions of study participants who reached an event of interest during a randomized controlled trial. Standard methods for estimating this proportion include the intent-to-treat method, which counts the number who reached the event of interest divided by the total number of participants, and the completers-only method, which counts the number who reached the event only among those who completed the entire study. When participants drop out of the study early, however, these methods will either be biased or inefficient. We propose to use the Kaplan-Meier method from survival analysis to estimate the proportion of interest in this non-survival setting. We show through extensive simulation studies that the Kaplan-Meier method has less bias and is more efficient than the standard methods. We demonstrate the performance of all methods for estimating proportions in one sample and for comparing proportions across two samples. Finally, we apply the proposed method to a data set for estimating and comparing proportions of patients who achieved treatment response during a Parkinson's disease trial for the treatment of impulse control disorders.