Literature DB >> 12369084

Estimation of the area under the ROC curve.

David Faraggi1, Benjamin Reiser.   

Abstract

The area under the receiver operating characteristic curve is frequently used as a measure for the effectiveness of diagnostic markers. In this paper we discuss and compare estimation procedures for this area. These are based on (i) the Mann-Whitney statistic; (ii) kernel smoothing; (iii) normal assumptions; (iv) empirical transformations to normality. These are compared in terms of bias and root mean square error in a large variety of situations by means of an extensive simulation study. Overall we find that transforming to normality usually is to be preferred except for bimodal cases where kernel methods can be effective. Copyright 2002 John Wiley & Sons, Ltd.

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Year:  2002        PMID: 12369084     DOI: 10.1002/sim.1228

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


  70 in total

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4.  Bayesian multivariate hierarchical transformation models for ROC analysis.

Authors:  A James O'Malley; Kelly H Zou
Journal:  Stat Med       Date:  2006-02-15       Impact factor: 2.373

5.  A Bayesian hierarchical non-linear regression model in receiver operating characteristic analysis of clustered continuous diagnostic data.

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Journal:  Biom J       Date:  2005-08       Impact factor: 2.207

6.  Youden Index and the optimal threshold for markers with mass at zero.

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7.  Nonparametric sequential evaluation of diagnostic biomarkers.

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8.  Area under ROC curve, sensitivity, specificity of N-terminal probrain natriuretic peptide in predicting mortality in various subsets of patients with ischemic heart disease.

Authors:  G Ndrepepa; S Braun; A Kastrati; A Schömig
Journal:  Clin Res Cardiol       Date:  2007-08-15       Impact factor: 5.460

Review 9.  Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation.

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Journal:  Caspian J Intern Med       Date:  2013

10.  Predicting the timeline to the final menstrual period: the study of women's health across the nation.

Authors:  Gail A Greendale; Shinya Ishii; Mei-Hua Huang; Arun S Karlamangla
Journal:  J Clin Endocrinol Metab       Date:  2013-03-26       Impact factor: 5.958

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