A0923
Title: Biomarker cutoff estimation and their confidence intervals under ternary umbrella and tree stochastic ordering settings
Authors: Benjamin Brewer - University of Kansas Medical Center (United States)
Leonidas Bantis - University of Kansas Medical Center (United States) [presenting]
Abstract: Tuberculosis (TB) studies often involve four different states under consideration, namely, healthy, latent infection, pulmonary active disease, and extra-pulmonary active disease. While diagnostic 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 in 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. The purpose is to discuss the estimation and inference around optimized cutoffs when accounting for such configurations. Different cutoff alternatives are explored, and parametric is provided, flexible parametric and non-parametric kernel-based approaches for estimation and inference. The approaches are evaluated using simulations and are illustrated through a real data set that involves TB patients.
