In this presentation we will consider a commensurate Bayesian model using a piecewise exponential likelihood for synthesizing evidence from two sources of non-exchangeable time-to-event data. After a brief introduction of possible alternative approaches and an overview of the proposed method, we will move on to applying the method in real data. The setting will be a pair of post-market surveillance datasets capturing adverse events from persons on dialysis that underwent cardiac revascularization with a bare metal stent. We will focus on the manner in which the methods allow us to update fairly our current understanding of safety for the medical device as we progressively accumulate new information.