Receiver Operating Characteristic Analysis Under Tree Orderings of Disease Classes

• Activity Number: 261
• Type: Contributed
• Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
• Sponsor: Section on Medical Devices and Diagnostics
• Author(s): Dan Wang* and Lili Tian and Kristopher Attwood
• Companies: SUNY Buffalo and SUNY Buffalo and Roswell Park Cancer Institute
• Keywords: Receiver operating characteristic curve (ROC) ; Area under curve(AUC) ; Naive (AUC) ; $ROC$ for tree ordering (TROC) ; $AUC$ for tree ordering (TAUC)

Abstract

Receiver operating characteristic (ROC) curve and its summary statistics (e.g. the area under curve AU C), are commonly used to evaluate the diagnostic accuracy for disease processes with binary classi- fication. The ROC curve has been extended to ROC surface for scenarios with three ordinal classes or to hyper-surface for scenarios with more than three classes. For classifier under tree or umbrella order- ing in which the marker measurement for one class is lower or higher than those for the other classes, the commonly adopted diagnostic measures are the naive AU C (N AU C) based on a pooled class of all the unordered classes and the umbrella volume (U V ) based on the concept of volume under surface. However, both N AU C and U V have some limitations. For example, N AU C depends on the sampling weights for all the classes in population, and U V has only been introduced for three-class settings. In this article, we initiate the idea of a new ROC framework for tree or umbrella ordering (denoted as T ROC), and propose the area under T ROC curve (denoted as T AU C) as an appropriate diagnostic measure. The proposed T ROC and T AU C share many nice features with the traditional ROC an