Extending Classification Algorithms to Case-Control Studies

Stanfill, Bryan A.; Reehl, Sarah M.; Bramer, Lisa M.; Nakayasu, Ernesto S.; Rich, Stephen S.; Metz, Thomas O.; Rewers, Marian; Webb-Robertson, Bobbie-Jo M.

doi:10.1177/1179597219858954

Title: Extending Classification Algorithms to Case-Control Studies

Journal Article · 14 July 2019 · Biomedical Engineering and Computational Biology

DOI: https://doi.org/10.1177/1179597219858954 · OSTI ID:1556893

^[1]; Reehl, Sarah M. ^[1];

^[1];

^[1]; Rich, Stephen S. ^[2];

^[1]; Rewers, Marian ^[3]; Webb-Robertson, Bobbie-Jo M. ^[1]

Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Univ. of Virginia, Charlottesville, VA (United States)
Univ. of Colorado Denver, Aurora, CO (United States)

Classification is a common technique applied to ’omics data to build predictive models and identify potential markers of biomedical outcomes. Despite the prevalence of case-control studies, the number of classification methods available to analyze data generated by such studies is extremely limited. Conditional logistic regression is the most commonly used technique, but the associated modeling assumptions limit its ability to identify a large class of sufficiently complicated ’omic signatures. We propose a data preprocessing step which generalizes and makes any linear or nonlinear classification algorithm, even those typically not appropriate for matched design data, available to be used to model case-control data and identify relevant biomarkers in these study designs. We demonstrate on simulated case-control data that both the classification and variable selection accuracy of each method is improved after applying this processing step and that the proposed methods are comparable to or outperform existing variable selection methods. Finally, we demonstrate the impact of conditional classification algorithms on a large cohort study of children with islet autoimmunity.

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE

Contributing Organization:: TEDDY Study Group

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 1556893

Report Number(s):: PNNL-SA--135302; {"","Journal ID: ISSN 1179-5972"}

Journal Information:: Biomedical Engineering and Computational Biology, Journal Name: Biomedical Engineering and Computational Biology Vol. 10; ISSN 1179-5972

Publisher:: SAGECopyright Statement

Country of Publication:: United States

Language:: English

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Supplemental_Material – Supplemental material for Extending Classification Algorithms to Case-Control Studies Stanfill, Bryan; Reehl, Sarah; Bramer, Lisa https://doi.org/10.25384/sage.8924111 The current record is supplemented by this text	text	January 2019
Supplemental_Material – Supplemental material for Extending Classification Algorithms to Case-Control Studies Stanfill, Bryan; Reehl, Sarah; Bramer, Lisa https://doi.org/10.25384/sage.8924111.v1 The current record is supplemented by this text	text	January 2019
Extending Classification Algorithms to Case-Control Studies Stanfill, Bryan; Reehl, Sarah; Bramer, Lisa https://doi.org/10.25384/sage.c.4577918.v1 The current record is supplemented by this collection	collection	January 2019
Online_Appendix_Final – Supplemental material for Extending Classification Algorithms to Case-Control Studies Stanfill, Bryan; Reehl, Sarah; Bramer, Lisa https://doi.org/10.25384/sage.8924099.v1 The current record is supplemented by this text	text	January 2019
Extending Classification Algorithms to Case-Control Studies Stanfill, Bryan; Reehl, Sarah; Bramer, Lisa https://doi.org/10.25384/sage.c.4577918 The current record is supplemented by this collection	collection	January 2019
Online_Appendix_Final – Supplemental material for Extending Classification Algorithms to Case-Control Studies Stanfill, Bryan; Reehl, Sarah; Bramer, Lisa https://doi.org/10.25384/sage.8924099 The current record is supplemented by this text	text	January 2019

Title: Extending Classification Algorithms to Case-Control Studies

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