Background

Receiver Operating Characteristic (ROC) curve analysis is a popular method for evaluating the performance of (bio)markers. However, the standard ROC curve does not directly connect marker performance to patient-related outcomes. Methods

We propose a novel graphical tool, the Relative Impact Characteristic (RIC) curve, that depicts the trade-off between the population-level impact of treatment as a function of the size of the treated population for a given marker positivity rule (e.g., a threshold). We establish analogies between the ROC and the RIC curves around the interpretations of shape, slopes, and area under the curve, and discuss parametric inference on RIC. Results

As a case study, we used data from a clinical trial on preventive therapy for exacerbations of chronic obstructive pulmonary disease. We illustrate how the RIC curve can be constructed for a predication score and be interpreted in terms of a marker's ability towards concentrating treatment benefit in the population. Conclusions

The RIC curve enables evaluation of markers in terms of their treatment-related clinical utility. Its analogies with the standard ROC analysis can facilitate its interpretation, bringing a population-based perspective to the activities of diverse marker development and evaluation teams.