For clinical trials with time to events as the primary endpoint, the study cutoff is often event-driven and the logrank test is the most commonly used statistical method for evaluating treatment effect. However, the method emphasizes covariate effects on failure times and relies on the proportional hazards (PH) assumption in that they have maximal power in this circumstance. In certain disease areas or populations, some patients can be curable and never experience the events despite a long follow-up. The event accumulation may dry out after a certain period of follow-up and the crossing hazard will occur after the plateau of event accrual in the control group. The treatment effect could be reflected as the combination of improvement of cure rate and the delay of events for those un-curable patients. Study power changes with different combination of cure rate improvement and risk reduction. In this talk, we will illustrate these practical issues using simulation studies and explore alternative ways for optimal study clinical cutoffs and efficient testing methods.