Bridging studies are used to provide treatment effect estimates of a therapeutic product based on outcome data observed from a mixed population of high heterogeneity, as such data may be collected from multiple independent clinical trials. When dealing with time-to-event data, the non-linear structure of the (log) hazard ratio estimates based on a Cox proportional hazard model leads to challenges in the development of algorithms for combining multiple clinical trials. It is of much concern that only aggregate patient data may be available in the scenario of bridging studies. In particular, if the treatment effects of the same therapeutic observed from different clinical trials of concern are known to be essentially different, one do not expect a convex combination of the hazard ratio estimates can lead to a precise description of the treatment effect among the overall population. In this talk, we proposed a combined hazard ratio estimate using aggregate data. Its asymptotic efficiency and the power of a Wald test for the combined treatment effects are demonstrated using simulations.