Extending Classification Algorithms to Case-Control Studies. (June 2019)

• Record Type: Journal Article
• Authors: Stanfill, Bryan; Reehl, Sarah; Bramer, Lisa; Nakayasu, Ernesto S; Rich, Stephen S; Metz, Thomas O; Rewers, Marian; Webb-Robertson, Bobbie-Jo
• Is Part Of: Biomedical engineering and computational biology. Volume 10(2019)
• Publication Date: 2019-06
• Subjects: Diabetes -- machine learning -- support vector machines -- biomarker discovery -- variable selection; Biomedical engineering -- Periodicals; Computational biology -- Periodicals; Biomedical Engineering; Computational Biology; Biomedical engineering; Computational biology; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals; Periodicals; 610.2805
• DOI: 10.1177/1179597219858954
• Languages: English
• ISSNs: 1179-5972

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.