This module covers principles, metrics, concepts, and graphical devices for evaluating biomarkers and risk prediction models.  The focus is prediction of binary outcomes such as 5-year risk of cardiovascular disease; acute kidney injury following cardiac surgery; and diagnostic biomarkers of cancer.

We will discuss motivations for risk prediction in clinical medicine and public health, and clarify the concept of “personal” risk.  Metrics and graphical tools will include ROC curves and AUC; calibration plots for risk prediction models; and net benefit and decision curves. The module will also discuss methods for comparing risk prediction models and, in particular, assessing the prediction increment of a new biomarker.  Recent proposals that rely on the concept of reclassification to evaluate new biomarkers will be evaluated.

There will be an opportunity for hands-on practice in R with graphical methods such as calibration plots and decision curves, but knowledge of R is not required for this module.