  Many medical decisions involve the use of dynamic information collected on
individual patients toward predicting likely transitions in their future health
status. If accurate predictions are developed, then a prognostic mode can
identify patients at greatest risk for future adverse events, and may be used
clinically to define populations appropriate for targeted intervention. In
practice, a prognostic model is often used to guide decisions at multiple time
points over the course of disease, and classification performance, i.e.
sensitivity and specificity, for distinguishing high-risk versus low-risk
individuals may vary over time as an individual's disease status and prognostic
information change. In this tutorial, we detail contemporary statistical
methods that can characterize the time-varying accuracy of prognostic survival
models when used for dynamic decision-making. Although statistical methods for
evaluating prognostic models with simple binary outcomes are well established,
methods appropriate for survival outcomes are less well known and require
time-dependent extensions of sensitivity and specificity to fully characterize
longitudinal biomarkers or models. The methods we review are particularly
important in that they allow for appropriate handling of censored outcomes
commonly encountered with event-time data. We highlight the importance of
determining whether clinical interest is in predicting cumulative (or
prevalent) cases over a fixed future time interval versus predicting incident
cases over a range of follow-up time, and whether patient information is static
or updated over time. We discuss implementation of time-dependent ROC
approaches using relevant R statistical software packages. The statistical
summaries are illustrated using a liver prognostic model to guide
transplantation in primary biliary cirrhosis.
