Methods for evaluating the time-varying prognostic performance of survival models

Abstract

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.