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Activity Number: 159 - Novel Approaches for Diagnostics and Prediction with Complex Data
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: International Chinese Statistical Association
Abstract #301692
Title: Assessing the Incremental Value of New Biomarkers Based on or Rules
Author(s): Ying Huang* and Lu Wang and Alex R. Luedtke
Companies: Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center and Dept. of Statistics- University of Washington
Keywords: Bootstrap; Combining biomarkers; Cross-validation; Fuzzy p-value; Incremental value; OR/AND rules

In early detection of disease, a single biomarker often has inadequate classification performance, making it important to identify new biomarkers to combine with the existing marker for improved performance. A biologically natural method for combining biomarkers is to use logic rules, e.g., the OR/AND rules. In our motivating example of early detection of pancreatic cancer, the established biomarker CA19-9 is only present in a subclass of cancers; it is of interest to identify new biomarkers present in the other subclasses and declare disease when either marker is positive. While there has been research on developing biomarker combinations using the OR/AND rules, inference regarding the incremental value of the new marker within this framework is lacking and challenging due to statistical non-regularity. In this article, we aim to answer the inferential question of whether combining the new biomarker achieves better classification performance than using the existing biomarker alone, based on a nonparametrically estimated OR rule that maximizes the weighted average of sensitivity and specificity. We propose and compare various procedures for testing the incremental value of the new

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