Biomarker evaluation - c-statistic (AUC) and alternatives

The difference between c-index of 0.72/0.70 means that the first model has 2% higher chance to guess the event in an imaginary game that samples one event and one non-event from the validated data.

The real issue is not the interpretability, but the relevance for decision making.

Considering new biomarkers must come with cost, c-index can’t help you with these kind of decisions.

PS: That’s my attempt to explain c-index (and why I don’t like “area-under-curve” interpertation):

predictionperformancediscrimination.netlify.app

discrimination