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Flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy
BACKGROUND: In medical research, it is common to collect information of multiple continuous biomarkers to improve the accuracy of diagnostic tests. Combining the measurements of these biomarkers into one single score is a popular practice to integrate the collected information, where the accuracy of...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4628350/ https://www.ncbi.nlm.nih.gov/pubmed/26521228 http://dx.doi.org/10.1186/s12874-015-0085-z |
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author | Xu, Tu Fang, Yixin Rong, Alan Wang, Junhui |
author_facet | Xu, Tu Fang, Yixin Rong, Alan Wang, Junhui |
author_sort | Xu, Tu |
collection | PubMed |
description | BACKGROUND: In medical research, it is common to collect information of multiple continuous biomarkers to improve the accuracy of diagnostic tests. Combining the measurements of these biomarkers into one single score is a popular practice to integrate the collected information, where the accuracy of the resultant diagnostic test is usually improved. To measure the accuracy of a diagnostic test, the Youden index has been widely used in literature. Various parametric and nonparametric methods have been proposed to linearly combine biomarkers so that the corresponding Youden index can be optimized. Yet there seems to be little justification of enforcing such a linear combination. METHODS: This paper proposes a flexible approach that allows both linear and nonlinear combinations of biomarkers. The proposed approach formulates the problem in a large margin classification framework, where the combination function is embedded in a flexible reproducing kernel Hilbert space. RESULTS: Advantages of the proposed approach are demonstrated in a variety of simulated experiments as well as a real application to a liver disorder study. CONCLUSION: Linear combination of multiple diagnostic biomarkers are widely used without proper justification. Additional research on flexible framework allowing both linear and nonlinear combinations is in need. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12874-015-0085-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4628350 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-46283502015-11-01 Flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy Xu, Tu Fang, Yixin Rong, Alan Wang, Junhui BMC Med Res Methodol Research Article BACKGROUND: In medical research, it is common to collect information of multiple continuous biomarkers to improve the accuracy of diagnostic tests. Combining the measurements of these biomarkers into one single score is a popular practice to integrate the collected information, where the accuracy of the resultant diagnostic test is usually improved. To measure the accuracy of a diagnostic test, the Youden index has been widely used in literature. Various parametric and nonparametric methods have been proposed to linearly combine biomarkers so that the corresponding Youden index can be optimized. Yet there seems to be little justification of enforcing such a linear combination. METHODS: This paper proposes a flexible approach that allows both linear and nonlinear combinations of biomarkers. The proposed approach formulates the problem in a large margin classification framework, where the combination function is embedded in a flexible reproducing kernel Hilbert space. RESULTS: Advantages of the proposed approach are demonstrated in a variety of simulated experiments as well as a real application to a liver disorder study. CONCLUSION: Linear combination of multiple diagnostic biomarkers are widely used without proper justification. Additional research on flexible framework allowing both linear and nonlinear combinations is in need. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12874-015-0085-z) contains supplementary material, which is available to authorized users. BioMed Central 2015-10-31 /pmc/articles/PMC4628350/ /pubmed/26521228 http://dx.doi.org/10.1186/s12874-015-0085-z Text en © Xu et al. 2015 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Xu, Tu Fang, Yixin Rong, Alan Wang, Junhui Flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy |
title | Flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy |
title_full | Flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy |
title_fullStr | Flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy |
title_full_unstemmed | Flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy |
title_short | Flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy |
title_sort | flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4628350/ https://www.ncbi.nlm.nih.gov/pubmed/26521228 http://dx.doi.org/10.1186/s12874-015-0085-z |
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