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September 2018 Sequential double cross-validation for assessment of added predictive ability in high-dimensional omic applications
Mar Rodríguez-Girondo, Perttu Salo, Tomasz Burzykowski, Markus Perola, Jeanine Houwing-Duistermaat, Bart Mertens
Ann. Appl. Stat. 12(3): 1655-1678 (September 2018). DOI: 10.1214/17-AOAS1125


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.


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Mar Rodríguez-Girondo. Perttu Salo. Tomasz Burzykowski. Markus Perola. Jeanine Houwing-Duistermaat. Bart Mertens. "Sequential double cross-validation for assessment of added predictive ability in high-dimensional omic applications." Ann. Appl. Stat. 12 (3) 1655 - 1678, September 2018.


Received: 1 July 2016; Revised: 1 November 2017; Published: September 2018
First available in Project Euclid: 11 September 2018

zbMATH: 06979646
MathSciNet: MR3852692
Digital Object Identifier: 10.1214/17-AOAS1125

Rights: Copyright © 2018 Institute of Mathematical Statistics


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Vol.12 • No. 3 • September 2018
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