Extending Classification Algorithms to Case-Control Studies

doi:10.1177/1179597219858954

Title: Extending Classification Algorithms to Case-Control Studies

Abstract

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.

Authors:

^[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)

Publication Date:: 2019-07-15

Research Org.:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Org.:: USDOE

Contributing Org.:: TEDDY Study Group

OSTI Identifier:: 1556893

Report Number(s):: PNNL-SA-135302
Journal ID: ISSN 1179-5972

Grant/Contract Number:: AC05-76RL01830

Resource Type:: Accepted Manuscript

Journal Name:: Biomedical Engineering and Computational Biology

Additional Journal Information:: Journal Volume: 10; Journal ID: ISSN 1179-5972

Country of Publication:: United States

Language:: English

Subject:: 59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Diabetes; machine learning; support vector machines; biomarker discovery; variable selection

Citation Formats


                    Stanfill, Bryan A., Reehl, Sarah M., Bramer, Lisa M., Nakayasu, Ernesto S., Rich, Stephen S., Metz, Thomas O., Rewers, Marian, and Webb-Robertson, Bobbie-Jo M. Extending Classification Algorithms to Case-Control Studies.  United States: N. p., 2019. 
        Web.  doi:10.1177/1179597219858954.

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                    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. Extending Classification Algorithms to Case-Control Studies.  United States.  doi:10.1177/1179597219858954.

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                    Stanfill, Bryan A., Reehl, Sarah M., Bramer, Lisa M., Nakayasu, Ernesto S., Rich, Stephen S., Metz, Thomas O., Rewers, Marian, and Webb-Robertson, Bobbie-Jo M. Mon .  
        "Extending Classification Algorithms to Case-Control Studies".  United States.  doi:10.1177/1179597219858954.  https://www.osti.gov/servlets/purl/1556893.

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@article{osti_1556893,

  title        = {Extending Classification Algorithms to Case-Control Studies},

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

  abstractNote = {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.},

  doi          = {10.1177/1179597219858954},

  journal      = {Biomedical Engineering and Computational Biology},

  number       = ,

  volume       = 10,

  place        = {United States},

  year         = {2019},

  month        = {7}

}

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Journal Article:

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DOI: 10.1177/1179597219858954

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Works referenced in this record:

Random Forests
journal, January 2001

Breiman, Leo
Machine Learning, Vol. 45, Issue 1, p. 5-32
DOI: 10.1023/A:1010933404324

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Title: Extending Classification Algorithms to Case-Control Studies

Abstract

Citation Formats

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