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B0948
Title: Estimation and construction of CIs for the cut points of cont. biomarkers under the Euclidean distance in 3D settings Authors:  Brian Mosier - University of Kansas Medical Center (United States) [presenting]
Leonidas Bantis - University of Kansas Medical Center (United States)
Abstract: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive type of cancer with a 5-year survival rate of less than 5\%. As in many other diseases, its diagnosis might involve progressive stages. It is common that in biomarker studies referring to PDAC, recruitment involves three groups: healthy individuals, patients that suffer from chronic pancreatitis and PDAC patients. Early detection and accurate classification of the state of the disease are crucial for patients' successful treatment. ROC analysis is the most popular way to evaluate the performance of a biomarker, and the Youden index is commonly employed for cutoff derivation. The so-called generalized Youden index has a drawback in the three-class case of not accommodating the full data set when estimating the optimal cutoffs. We explore the use of the Euclidean distance of the ROC to the perfection corner for the derivation of cutoffs in trichotomous settings. We construct an inferential framework that involves both parametric and non-parametric techniques. The proposed methods can accommodate the full information of a given data set and thus provide more accurate estimates in terms of the decision-making cutoffs compared to a Youden-based strategy. We evaluate our approaches through extensive simulations and illustrate them on a PDAC biomarker study.