Many prognostic models are created using survival data. Despite this, the practice of developing such models remains fairly ad hoc, and the temporal aspect of survival data is often underused. I will outline a number of existing methods for evaluating prognostic survival models. In particular, the emphasis will be on tools that can quantify how prognostic performance varies with time. I will also present a complementary new tool we have developed, the hazard discrimination summary (HDS). HDS is an interpretable, risk-based measure of how a model’s discrimination varies with time. I will also describe an interesting connection between HDS and the Cox model partial likelihood.