In clinical trials especially those events-driven trials, it is often of great interest to predict the timing of pre-specified landmark event accurately in order to prepare for the interim or final analyses. Both parametric and non-parametric approaches have been proposed in the literature to estimate the underlying survival functions which is the key to the prediction of future event times. However, the existing approaches are neither not applicable to double-blind clinical trials or assuming smooth survival functions which might not hold in real clinical settings. In this talk, a hybrid parametric and non-parametric approach is proposed to predict event times in double-blind clinical trials with time-to-event outcomes. A greedy algorithm is first developed to detect change points in survival functions. The survival function before the last change point is estimated non-parametrically and the tail distribution beyond the last change point is estimated parametrically. Numerical results show that the proposed approach provides accurate predictions for future event times.