Inferences for the Correct Classification Fractions of a Continuous Biomarker in Trichotomous Settings with an Application to Patients with Hepatocellular Carcinoma

• Activity Number: 209 - Statistical methods for genomic and epigenetic data analysis
• Type: Contributed
• Date/Time: Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Section on Medical Devices and Diagnostics
• Author(s): Peng Shi* and Leonidas E Bantis
• Companies: University of Kansas Medical Center and University of Kansas Medical Center
• Keywords: ROC; Euclidean distance; biomarker; cutoff; 3-class; true class fraction

Abstract

Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver. As such, there is a strong clinical interest in finding new biomarkers for its early detection. When the disease status is trichotomous, the ROC surface is an appropriate tool for assessing the discriminatory ability of a marker. A popular approach for computing cutoffs for decision making is the Youden index and its recent 3-class generalization. However, this method treats the data in a pairwise fashion and is unable to accommodate biomarker scores from all three groups simultaneously. This may result in inappropriate cutoffs that are of no clinical interest. We propose methods for such inferences where the cutoffs are based on the minimized Euclidean distance of the ROC surface from the perfection corner. We provide an inferential framework, both parametric and non-parametric, for the derivation of marginal confidence intervals (CIs) and joint confidence spaces (CSs) for the optimized true class rates. We evaluate our approaches through extensive simulations and we finally illustrate the proposed methods using a real data set that refers to HCC patients.