This module will consider biomarkers for binary outcomes. 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 course will also discuss methods to compare risk prediction models and, in particular, assessing the prediction increment of a new biomarker. Recent proposals for metrics that rely on the concept of reclassification to evaluate new biomarkers will be considered. The primary focus of the module is establishing principles and concepts for evaluating biomarkers and risk prediction models. There will be an opportunity for hands-on practice in R with graphical methods such as calibration plots and decision curves.