Table 1.

Characteristics of some traditional and novel performance measures

Aspect	Measure	Visualization	Characteristics
Overall performance	R2 Brier	Validation graph	Better with lower distance between Y and Ŷ. Captures calibration and discrimination aspects.
Discrimination	C statistic	ROC curve	Rank order statistic; Interpretation for a pair of patients with and without the outcome
	Discrimination slope	Box plot	Difference in mean of predictions between outcomes; Easy visualization
Calibration	Calibration-in-the-large	Calibration or validation graph	Compare mean(y) versus mean(ŷ); essential aspect for external validation
	Calibration slope		Regression slope of linear predictor; essential aspect for internal and external validation related to ‘shrinkage’ of regression coefficients
	Hosmer-Lemeshow test		Compares observed to predicted by decile of predicted probability
Reclassification	Reclassification table	Cross-table or scatter plot	Compare classifications from 2 models (one with, one without a marker) for changes
	Reclassification calibration		Compare observed and predicted within cross-classified categories
	Net Reclassification Index (NRI)		Compare classifications from 2 models for changes by outcome for a net calculation of changes in the right correction
	Integrated Discrimination Index (IDI)	Box plots for 2 models (one with, one without a marker)	Integrates the NRI over all possible cut-offs; equivalent to difference in discrimination slopes
Clinical usefulness	Net Benefit (NB)	Cross-table	Net number of true positives gained by using a model compared to no model at a single threshold (NB) or over a range of thresholds (DCA)
	Decision curve analysis (DCA)	Decision curve