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      bcROCsurface: an R package for correcting verification bias in estimation of the ROC surface and its volume for continuous diagnostic tests

      research-article
      BMC Bioinformatics
      BioMed Central
      Software, R package, ROC surface analysis, Missing at random

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          Abstract

          Background

          Receiver operating characteristic (ROC) surface analysis is usually employed to assess the accuracy of a medical diagnostic test when there are three ordered disease status (e.g. non-diseased, intermediate, diseased). In practice, verification bias can occur due to missingness of the true disease status and can lead to a distorted conclusion on diagnostic accuracy. In such situations, bias–corrected inference tools are required.

          Results

          This paper introduce an R package, named bcROCsurface, which provides utility functions for verification bias–corrected ROC surface analysis. The shiny web application of the correction for verification bias in estimation of the ROC surface analysis is also developed.

          Conclusion

          bcROCsurface may become an important tool for the statistical evaluation of three–class diagnostic markers in presence of verification bias. The R package, readme and example data are available on CRAN. The web interface enables users less familiar with R to evaluate the accuracy of diagnostic tests, and can be found at http://khanhtoduc.shinyapps.io/bcROCsurface_shiny/.

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          Most cited references7

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          Seamless R and C++ Integration with Rcpp

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            Ordered multiple-class ROC analysis with continuous measurements.

            Receiver operating characteristic (ROC) curves have been useful in two-group classification problems. In three- and multiple-class diagnostic problems, an ROC surface or hyper-surface can be constructed. The volume under these surfaces can be used for inference using bootstrap techniques or U-statistics theory. In this article, ROC surfaces and hyper-surfaces are defined and their behaviour and utility in multi-group classification problems is investigated. The formulation of the problem is equivalent to what has previously been proposed in the general multi-category classification problem but the definition of ROC surfaces here is less complex and addresses directly the narrower problem of ordered categories in the three-class and, by extension, the multi-class problem applied to continuous and ordinal data. Non-parametric manipulation of both continuous and discrete test data and comparison between two diagnostic tests applied to the same subjects are considered. A three-group classification example in the context of HIV neurological disease is presented and the results are discussed. 2004 John Wiley & Sons, Ltd.
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              HUM calculator and HUM package for R: easy-to-use software tools for multicategory receiver operating characteristic analysis.

              Receiver operating characteristic (ROC) analysis is usually applied in bioinformatics to evaluate the abilities of biological markers to differentiate between the presence or absence of a disease. It includes the derivation of the useful scalar performance measure area under the ROC curve for binary classification tasks. As real applications often deal with more than two classes, multicategory ROC analysis and the corresponding hypervolume under the manifold (HUM) measure have become a topic of growing interest. To support researchers in carrying out multicategory ROC analysis, we have developed two tools in different programming environments which feature user-friendly, object-oriented and flexible interfaces and enable the user to compute HUM values and plot 2D- and 3D-ROC curves.
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                Author and article information

                Contributors
                toduc@stat.unipd.it
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                18 November 2017
                18 November 2017
                2017
                : 18
                : 503
                Affiliations
                ISNI 0000 0004 1757 3470, GRID grid.5608.b, Department of Statistical Sciences, , University of Padova, ; via C. Battisti, 241, Padova, 35121 Italy
                Author information
                http://orcid.org/0000-0002-4641-0764
                Article
                1914
                10.1186/s12859-017-1914-3
                5694622
                29151019
                8fac7b3d-e743-46a8-b754-5bd57f52ff55
                © The Author(s) 2017

                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.

                History
                : 21 December 2016
                : 1 November 2017
                Categories
                Software
                Custom metadata
                © The Author(s) 2017

                Bioinformatics & Computational biology
                software,r package,roc surface analysis,missing at random

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