This module covers methodology for evaluating biomarkers and risk prediction models, covering principles, concepts, metrics, and graphical tools.
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. We will also consider evaluating the utility of a single or composite biomarker for prognostic enrichment of a clinical trial.
There will be an opportunity for hands-on practice in R using relevant packages such as rms, rmda, and BioPET. However, the software component of the module is small and knowledge of R is not required for this module.