First things first: risk model performance metrics should reflect the clinical application.

Developing new measures of risk model performance is an active line of research, often motivated by the conventional wisdom that area under the ROC curve is an 'insensitive' measure of the additional predictive capacity offered by new biomarkers. Without endorsing area under the ROC curve, we argue that this charge is not substantiated. Three articles in this issue discuss alternative metrics of risk model performance: NRI(p) (two-category net reclassification index at the event rate), integrated discrimination index, and R-squared statistics. Guided by the principle that performance metrics should match the intended use of a risk prediction model, we argue that routine use of these indices is not justified. Instead, we recommend decision-theoretic measures to evaluate risk prediction models for applications in which clinically relevant risk thresholds have been established for classifying individuals. In the absence of established risk thresholds, additional research is needed to develop suitable metrics.