Rodriguez-Girondo, M, Salo, P, Burzykowski, T et al. (3 more authors) (2018) Sequential double cross-validation for assessment of added predictive ability in high-dimensional omic applications. Annals of Applied Statistics, 12 (3). pp. 1655-1678. ISSN 1932-6157
Abstract
Enriching existing predictive models with new biomolecular markers is an important task in the new multi-omic era. Clinical studies increasingly include new sets of omic measurements which may prove their added value in terms of predictive performance. We introduce a two-step approach for the assessment of the added predictive ability of omic predictors, based on sequential double cross-validation and regularized regression models. We propose several performance indices to summarize the two-stage prediction procedure and a permutation test to formally assess the added predictive value of a second omic set of predictors over a primary omic source. The performance of the test is investigated through simulations. We illustrate the new method through the systematic assessment and comparison of the performance of transcriptomics and metabolomics sources in the prediction of body mass index (BMI) using longitudinal data from the Dietary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome (DILGOM) study, a population-based cohort from Finland.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This paper was originally published in Annals of Applied Statistics, https://www.e-publications.org/ims/submission/AOAS/user/submissionFile/27338?confirm=d13de40e. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Added predictive ability; double cross-validation; regularized regression; multiple omics sets |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Nov 2017 12:28 |
Last Modified: | 01 Oct 2018 14:51 |
Published Version: | https://www.e-publications.org/ims/submission/AOAS... |
Status: | Published |
Publisher: | Institute of Mathematical Statistics |
Identification Number: | 10.1214/17-AOAS1125 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:124334 |