Metrological characterization of a diagnostic test extending the Receiving Operating Curve analysis using Supplement 2 recommendations

Receiving Operating Curve (ROC) analysis is a powerful and statistical accepted method to assess the performance of a diagnostic test. ROC curve plots true positive rate against false positive rate, evaluated on a certain population. Instrumental and model uncertainty contributions can strongly affect the performance of the ROC analysis especially in the evaluation of performance metrics such as Area Under ROC (AUC) and Optimal Operating Points. Supplement 2 reports detailed instructions to handle and propagate uncertainty through a Multiple Input Multiple Output system, in case of correlate output variables, such as TPR and FPR. After a detailed revision of the existing literature, the present paper describes and applies a novel methodology, totally framed in the GUM and its supplements, to represent and propagate the uncertainty contributions estimated in a medical context, throughout the ROC analysis, providing new concepts such as ROC confidence region and Optimal Operating Region