Affiliation:
1. Department of Biostatistics & Data Science The University of Kansas Medical Center Kansas City Kansas USA
Abstract
Tuberculosis (TB) studies often involve four different states under consideration, namely, “healthy,” “latent infection,” “pulmonary active disease,” and “extra‐pulmonary active disease.” While highly accurate clinical diagnosis tests do exist, they are expensive and generally not accessible in regions where they are most needed; thus, there is an interest in assessing the accuracy of new and easily obtainable biomarkers. For some such biomarkers, the typical stochastic ordering assumption might not be justified for all disease classes under study, and usual ROC methodologies that involve ROC surfaces and hypersurfaces are inadequate. Different types of orderings may be appropriate depending on the setting, and these may involve a number of ambiguously ordered groups that stochastically exhibit larger (or lower) marker scores than the remaining groups. Recently, there has been scientific interest on ROC methods that can accommodate these so‐called “tree” or “umbrella” orderings. However, there is limited work discussing the estimation of cutoffs in such settings. In this article, we discuss the estimation and inference around optimized cutoffs when accounting for such configurations. We explore different cutoff alternatives and provide parametric, flexible parametric, and non‐parametric kernel‐based approaches for estimation and inference. We evaluate our approaches using simulations and illustrate them through a real data set that involves TB patients.
Funder
Cancer Center, University of Kansas
CMH
Honorable Tina Brozman Foundation
National Cancer Institute
National Center for Advancing Translational Sciences
NIH
Ovarian Cancer Research Alliance
U.S. Department of Defense
Subject
Statistics and Probability,Epidemiology