title: On the use of partial area under the ROC curve for comparison of two diagnostic tests.
abstract: Evaluation of diagnostic performance is typically based on the receiver operating characteristic (ROC) curve and the area under the curve (AUC) as its summary index. The partial area under the curve (pAUC) is an alternative index focusing on the range of practical/clinical relevance. One of the problems preventing more frequent use of the pAUC is the perceived loss of efficiency in cases of noncrossing ROC curves. In this paper, we investigated statistical properties of comparisons of two correlated pAUCs. We demonstrated that outside of the classic model there are practically reasonable ROC types for which comparisons of noncrossing concave curves would be more powerful when based on a part of the curve rather than the entire curve. We argue that this phenomenon stems in part from the exclusion of noninformative parts of the ROC curves that resemble straight-lines. We conducted extensive simulation studies in families of binormal, straight-line, and bigamma ROC curves. We demonstrated that comparison of pAUCs is statistically more powerful than comparison of full AUCs when ROC curves are close to a "straight line". For less flat binormal ROC curves an increase in the integration range often leads to a disproportional increase in pAUCs' difference, thereby contributing to an increase in statistical power. Thus, efficiency of differences in pAUCs of noncrossing ROC curves depends on the shape of the curves, and for families of ROC curves that are nearly straight-line shaped, such as bigamma ROC curves, there are multiple practical scenarios in which comparisons of pAUCs are preferable.
authors.affiliation: Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
authors.forename: Hua
authors.initials: H
authors.lastname: Ma
authors.forename: Andriy I
authors.initials: AI
authors.lastname: Bandos
authors.forename: David
authors.initials: D
authors.lastname: Gur
mesh_terms: D019540:Area Under Curve; D001699:Biometry / Q000379:methods*; D019937:Diagnostic Techniques and Procedures; D012372:ROC Curve
keywords: AUC; Bigamma ROC curves; Partial area; ROC; Statistical power