Inferences around Biomarker Cutoffs under Ternary Umbrella and Tree Stochastic Ordering Settings

Music City Center 

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 inaccessible 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 paper, we discuss the estimation and inference around optimized cutoffs when accounting for such configurations.

biomarker

TROC

ROC surface

box-cox

kernels

cutoff 

Section on Statistical Learning and Data Science