**Abstract:** | When comparing a new treatment to a control with a time-to-event endpoint in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. Furthermore, standard methods of summarizing the treatment difference are based on Kaplan-Meier curves, the logrank test and the point and interval estimates via Cox's proportional hazards model. However, when the proportional hazards assumption is violated, the logrank test may not have sufficient power to detect the difference between two event time distributions, and the resulting hazard ratio estimate is difficult, if not impossible, to interpret as a treatment contrast. In this research, we propose a systematic, effective way to identify a promising subpopulation, for which the new treatment is expected to have a desired survival benefit, using the data from a current study involving similar comparator treatments. We illustrate the methods with the data from a randomized clinical trial. |