Literature DB >> 1865785

The area under the ROC curve and its competitors.

J Hilden1.   

Abstract

The area under the receiver operating characteristic (ROC) curve is a popular measure of the power of a (two-disease) diagnostic test, but it is shown here to be an inconsistent criterion: tests of indistinguishable clinical impacts may have different areas. A class of diagnosticity measures (DMs) of proven optimality is proposed instead. Once a regret(-like) measure of diagnostic uncertainty is agreed upon, the associated DM is uniquely defined and, indeed, calculable from the ROC curve configuration. Two scaled variants of the ROC are introduced and used to advantage in the analysis. They may also be helpful to students of medical decision making.

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Mesh:

Year:  1991        PMID: 1865785     DOI: 10.1177/0272989X9101100204

Source DB:  PubMed          Journal:  Med Decis Making        ISSN: 0272-989X            Impact factor:   2.583


  23 in total

1.  Reference standards, judges, and comparison subjects: roles for experts in evaluating system performance.

Authors:  George Hripcsak; Adam Wilcox
Journal:  J Am Med Inform Assoc       Date:  2002 Jan-Feb       Impact factor: 4.497

Review 2.  The Reproducibility of Changes in Diagnostic Figures of Merit Across Laboratory and Clinical Imaging Reader Studies.

Authors:  Frank W Samuelson; Craig K Abbey
Journal:  Acad Radiol       Date:  2017-06-27       Impact factor: 3.173

3.  Quantifying risk stratification provided by diagnostic tests and risk predictions: Comparison to AUC and decision curve analysis.

Authors:  Hormuzd A Katki
Journal:  Stat Med       Date:  2019-04-30       Impact factor: 2.373

4.  The leap to ordinal: Detailed functional prognosis after traumatic brain injury with a flexible modelling approach.

Authors:  Shubhayu Bhattacharyay; Ioan Milosevic; Lindsay Wilson; David K Menon; Robert D Stevens; Ewout W Steyerberg; David W Nelson; Ari Ercole
Journal:  PLoS One       Date:  2022-07-05       Impact factor: 3.752

5.  Assessing the performance of prediction models: a framework for traditional and novel measures.

Authors:  Ewout W Steyerberg; Andrew J Vickers; Nancy R Cook; Thomas Gerds; Mithat Gonen; Nancy Obuchowski; Michael J Pencina; Michael W Kattan
Journal:  Epidemiology       Date:  2010-01       Impact factor: 4.822

6.  Prediction Accuracy Measures for a Nonlinear Model and for Right-Censored Time-to-Event Data.

Authors:  Gang Li; Xiaoyan Wang
Journal:  J Am Stat Assoc       Date:  2019-03-11       Impact factor: 5.033

7.  Comparative statistical properties of expected utility and area under the ROC curve for laboratory studies of observer performance in screening mammography.

Authors:  Craig K Abbey; Brandon D Gallas; John M Boone; Loren T Niklason; Lubomir M Hadjiiski; Berkman Sahiner; Frank W Samuelson
Journal:  Acad Radiol       Date:  2014-04       Impact factor: 3.173

8.  Predicting landscape-genetic consequences of habitat loss, fragmentation and mobility for multiple species of woodland birds.

Authors:  J Nevil Amos; Andrew F Bennett; Ralph Mac Nally; Graeme Newell; Alexandra Pavlova; James Q Radford; James R Thomson; Matt White; Paul Sunnucks
Journal:  PLoS One       Date:  2012-02-17       Impact factor: 3.240

9.  Estimating the decision curve and its precision from three study designs.

Authors:  Ruth M Pfeiffer; Mitchell H Gail
Journal:  Biom J       Date:  2019-08-08       Impact factor: 1.715

10.  Evaluating the forced oscillation technique in the detection of early smoking-induced respiratory changes.

Authors:  Alvaro C D Faria; Agnaldo J Lopes; José M Jansen; Pedro L Melo
Journal:  Biomed Eng Online       Date:  2009-09-25       Impact factor: 2.819

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