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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

Overview of attention for article published in BMC Bioinformatics, November 2017
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Title
bcROCsurface: an R package for correcting verification bias in estimation of the ROC surface and its volume for continuous diagnostic tests
Published in
BMC Bioinformatics, November 2017
DOI 10.1186/s12859-017-1914-3
Pubmed ID
Authors

Khanh To Duc

Abstract

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. 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. 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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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 18%
Student > Bachelor 2 18%
Student > Master 2 18%
Lecturer 1 9%
Researcher 1 9%
Other 0 0%
Unknown 3 27%
Readers by discipline Count As %
Mathematics 2 18%
Agricultural and Biological Sciences 1 9%
Sports and Recreations 1 9%
Medicine and Dentistry 1 9%
Materials Science 1 9%
Other 0 0%
Unknown 5 45%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 November 2017.
All research outputs
#18,349,015
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#6,088
of 7,400 outputs
Outputs of similar age
#309,234
of 440,733 outputs
Outputs of similar age from BMC Bioinformatics
#100
of 148 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,400 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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