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Assessing the incremental value of new biomarkers based on OR rules.

Authors
  • Wang, Lu1
  • Luedtke, Alexander R2
  • Huang, Ying3
  • 1 Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N., Seattle, WA 98109, USA.
  • 2 Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N., Seattle, WA 98109, USA and Department of Statistics, University of Washington, Seattle, WA 98195, USA.
  • 3 Fred Hutchinson Cancer Research Center, Seattle, 1100 Fairview Ave N., WA 98109, USA and Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
Type
Published Article
Journal
Biostatistics (Oxford, England)
Publication Date
Jul 01, 2020
Volume
21
Issue
3
Pages
594–609
Identifiers
DOI: 10.1093/biostatistics/kxy070
PMID: 30590454
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

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 biomarker and constructing its confidence interval, using bootstrap, cross-validation, and a novel fuzzy p-value-based technique. We compare the performance of different methods via extensive simulation studies and apply them to the pancreatic cancer example. © The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected]

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