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Estimating the AUC with a graphical lasso method for high-dimensional biomarkers with LOD

Estimating the AUC with a graphical lasso method for high-dimensional biomarkers with LOD This manuscript estimates the area under the receiver operating characteristic curve (AUC) of combined biomarkers in a high-dimensional setting. We propose a penalization approach to the inference of precision matrices in the presence of the limit of detection. A new version of expectation-maximization algorithm is then proposed for the penalized likelihood, with the use of numerical integration and the graphical lasso method. The estimated precision matrix is then applied to the inference of AUCs. The proposed method outperforms the existing methods in numerical studies. We apply the proposed method to a data set of brain tumor study. The results show a higher accuracy on the estimation of AUC compared with the existing methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Estimating the AUC with a graphical lasso method for high-dimensional biomarkers with LOD

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Estimating the AUC with a graphical lasso method for high-dimensional biomarkers with LOD

Abstract

This manuscript estimates the area under the receiver operating characteristic curve (AUC) of combined biomarkers in a high-dimensional setting. We propose a penalization approach to the inference of precision matrices in the presence of the limit of detection. A new version of expectation-maximization algorithm is then proposed for the penalized likelihood, with the use of numerical integration and the graphical lasso method. The estimated precision matrix is then applied to the inference...
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Publisher
Taylor & Francis
Copyright
This work was authored as part of the Contributor's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2021.1898731
Publisher site
See Article on Publisher Site

Abstract

This manuscript estimates the area under the receiver operating characteristic curve (AUC) of combined biomarkers in a high-dimensional setting. We propose a penalization approach to the inference of precision matrices in the presence of the limit of detection. A new version of expectation-maximization algorithm is then proposed for the penalized likelihood, with the use of numerical integration and the graphical lasso method. The estimated precision matrix is then applied to the inference of AUCs. The proposed method outperforms the existing methods in numerical studies. We apply the proposed method to a data set of brain tumor study. The results show a higher accuracy on the estimation of AUC compared with the existing methods.

Journal

Biostatistics & Epidemiology – Taylor & Francis

Published: Jul 3, 2021

Keywords: Receiver operating characteristic curve; area under the ROC curve; graphical lasso; EM algorithm; high-dimensional biomarkers

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