Literature DB >> 20087877

Evaluating diagnostic tests: The area under the ROC curve and the balance of errors.

David J Hand1.   

Abstract

Because accurate diagnosis lies at the heart of medicine, it is important to be able to evaluate the effectiveness of diagnostic tests. A variety of accuracy measures are used. One particularly widely used measure is the AUC, the area under the receiver operating characteristic (ROC) curve. This measure has a well-understood weakness when comparing ROC curves which cross. However, it also has the more fundamental weakness of failing to balance different kinds of misdiagnoses effectively. This is not merely an aspect of the inevitable arbitrariness in choosing a performance measure, but is a core property of the way the AUC is defined. This property is explored, and an alternative, the H measure, is described. (c) 2010 John Wiley & Sons, Ltd.

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Year:  2010        PMID: 20087877     DOI: 10.1002/sim.3859

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  23 in total

1.  Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models.

Authors:  Michael J Pencina; Ralph B D'Agostino; Olga V Demler
Journal:  Stat Med       Date:  2011-12-07       Impact factor: 2.373

2.  Classifying highly imbalanced ICU data.

Authors:  Yazan F Roumani; Jerrold H May; David P Strum; Luis G Vargas
Journal:  Health Care Manag Sci       Date:  2012-11-07

3.  Diagnostic accuracy of behavioral, activity, ferritin, and clinical indicators of restless legs syndrome.

Authors:  Kathy C Richards; James E Bost; Valerie E Rogers; Lisa C Hutchison; Cornelia K Beck; Donald L Bliwise; Christine R Kovach; Norma Cuellar; Richard P Allen
Journal:  Sleep       Date:  2015-03-01       Impact factor: 5.849

Review 4.  Evaluation of markers and risk prediction models: overview of relationships between NRI and decision-analytic measures.

Authors:  Ben Van Calster; Andrew J Vickers; Michael J Pencina; Stuart G Baker; Dirk Timmerman; Ewout W Steyerberg
Journal:  Med Decis Making       Date:  2013-01-11       Impact factor: 2.583

5.  Beyond Self-Reports: Changes in Biomarkers as Predictors of Mortality.

Authors:  Dana A Glei; Noreen Goldman; Germán Rodríguez; Maxine Weinstein
Journal:  Popul Dev Rev       Date:  2014-06-01

6.  DiagTest3Grp: An R Package for Analyzing Diagnostic Tests with Three Ordinal Groups.

Authors:  Jingqin Luo; Chengjie Xiong
Journal:  J Stat Softw       Date:  2012-09-22       Impact factor: 6.440

7.  The subcortical basis of outcome and cognitive impairment in TBI: A longitudinal cohort study.

Authors:  Evan S Lutkenhoff; Matthew J Wright; Vikesh Shrestha; Courtney Real; David L McArthur; Manuel Buitrago-Blanco; Paul M Vespa; Martin M Monti
Journal:  Neurology       Date:  2020-09-09       Impact factor: 9.910

8.  Multivariate meta-analysis: potential and promise.

Authors:  Dan Jackson; Richard Riley; Ian R White
Journal:  Stat Med       Date:  2011-01-26       Impact factor: 2.373

9.  A comparison of MCC and CEN error measures in multi-class prediction.

Authors:  Giuseppe Jurman; Samantha Riccadonna; Cesare Furlanello
Journal:  PLoS One       Date:  2012-08-08       Impact factor: 3.240

10.  A novel method for classifying body mass index on the basis of speech signals for future clinical applications: a pilot study.

Authors:  Bum Ju Lee; Boncho Ku; Jun-Su Jang; Jong Yeol Kim
Journal:  Evid Based Complement Alternat Med       Date:  2013-03-14       Impact factor: 2.629

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