Literature DB >> 23508757

On use of partial area under the ROC curve for evaluation of diagnostic performance.

Hua Ma1, Andriy I Bandos, Howard E Rockette, David Gur.   

Abstract

Evaluation of diagnostic performance is a necessary component of new developments in many fields including medical diagnostics and decision making. The methodology for statistical analysis of diagnostic performance continues to develop, offering new analytical tools for conventional inferences and solutions for novel and increasingly more practically relevant questions. In this paper, we focus on the partial area under the Receiver Operating Characteristic (ROC) curve or pAUC. This summary index is considered to be more practically relevant than the area under the entire ROC curve (AUC), but because of several perceived limitations, it is not used as often. To improve interpretation, results for pAUC analysis are frequently reported using a rescaled index such as the standardized partial AUC proposed by McClish (1989). We derive two important properties of the relationship between the 'standardized' pAUC and the defined range of interest, which could facilitate a wider and more appropriate use of this important summary index. First, we mathematically prove that the 'standardized' pAUC increases with increasing range of interest for practically common ROC curves. Second, using comprehensive numerical investigations, we demonstrate that, contrary to common belief, the uncertainty about the estimated standardized pAUC can either decrease or increase with an increasing range of interest. Our results indicate that the partial AUC could frequently offer advantages in terms of statistical uncertainty of the estimation. In addition, selection of a wider range of interest will likely lead to an increased estimate even for standardized pAUC.
Copyright © 2013 John Wiley & Sons, Ltd.

Entities:  

Keywords:  ROC; evaluation of diagnostic performance; partial area under the Receiver Operating Characteristics; standardized pAUC; summary index; variance of standardized pAUC

Mesh:

Year:  2013        PMID: 23508757      PMCID: PMC3744586          DOI: 10.1002/sim.5777

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


  11 in total

1.  A non-parametric method for the comparison of partial areas under ROC curves and its application to large health care data sets.

Authors:  Dong D Zhang; Xia-Hua Zhou; Daniel H Freeman; Jean L Freeman
Journal:  Stat Med       Date:  2002-03-15       Impact factor: 2.373

2.  Partial AUC estimation and regression.

Authors:  Lori E Dodd; Margaret S Pepe
Journal:  Biometrics       Date:  2003-09       Impact factor: 2.571

3.  A COMPARISON OF DATA OBTAINED WITH DIFFERENT FALSE-ALARM RATES.

Authors:  D A NORMAN
Journal:  Psychol Rev       Date:  1964-05       Impact factor: 8.934

4.  Use of likelihood ratios for comparisons of binary diagnostic tests: underlying ROC curves.

Authors:  Andriy I Bandos; Howard E Rockette; David Gur
Journal:  Med Phys       Date:  2010-11       Impact factor: 4.071

Review 5.  Receiver operating characteristic (ROC) methodology: the state of the art.

Authors:  J A Hanley
Journal:  Crit Rev Diagn Imaging       Date:  1989

6.  Analyzing a portion of the ROC curve.

Authors:  D K McClish
Journal:  Med Decis Making       Date:  1989 Jul-Sep       Impact factor: 2.583

7.  Nonparametric statistical inference method for partial areas under receiver operating characteristic curves, with application to genomic studies.

Authors:  Yaohua He; Michael Escobar
Journal:  Stat Med       Date:  2008-11-10       Impact factor: 2.373

8.  Digital breast tomosynthesis: observer performance study.

Authors:  David Gur; Gordon S Abrams; Denise M Chough; Marie A Ganott; Christiane M Hakim; Ronald L Perrin; Grace Y Rathfon; Jules H Sumkin; Margarita L Zuley; Andriy I Bandos
Journal:  AJR Am J Roentgenol       Date:  2009-08       Impact factor: 3.959

9.  Sample size determination for diagnostic accuracy studies involving binormal ROC curve indices.

Authors:  N A Obuchowski; D K McClish
Journal:  Stat Med       Date:  1997-07-15       Impact factor: 2.373

10.  The meaning and use of the area under a receiver operating characteristic (ROC) curve.

Authors:  J A Hanley; B J McNeil
Journal:  Radiology       Date:  1982-04       Impact factor: 11.105

View more
  34 in total

1.  Genome-Wide Association Analyses Based on Broadly Different Specifications for Prior Distributions, Genomic Windows, and Estimation Methods.

Authors:  Chunyu Chen; Juan P Steibel; Robert J Tempelman
Journal:  Genetics       Date:  2017-06-21       Impact factor: 4.562

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.  Jackknife variance of the partial area under the empirical receiver operating characteristic curve.

Authors:  Andriy I Bandos; Ben Guo; David Gur
Journal:  Stat Methods Med Res       Date:  2014-09-16       Impact factor: 3.021

4.  Never forget a face: Verbalization facilitates recollection as evidenced by flexible responding to contrasting recognition memory tests.

Authors:  Dawn R Weatherford; Mitchell A Meltzer; Curt A Carlson; James C Bartlett
Journal:  Mem Cognit       Date:  2021-02

5.  Nature-Inspired Algorithm for Training Multilayer Perceptron Networks in e-health Environments for High-Risk Pregnancy Care.

Authors:  Mário W L Moreira; Joel J P C Rodrigues; Neeraj Kumar; Jalal Al-Muhtadi; Valery Korotaev
Journal:  J Med Syst       Date:  2018-02-01       Impact factor: 4.460

6.  Estimating the Area Under ROC Curve When the Fitted Binormal Curves Demonstrate Improper Shape.

Authors:  Andriy I Bandos; Ben Guo; David Gur
Journal:  Acad Radiol       Date:  2016-11-21       Impact factor: 3.173

7.  Informativeness of Diagnostic Marker Values and the Impact of Data Grouping.

Authors:  Hua Ma; Andriy I Bandos; David Gur
Journal:  Comput Stat Data Anal       Date:  2017-08-08       Impact factor: 1.681

8.  Comparison of Statistical Tests and Power Analysis for Phosphoproteomics Data.

Authors:  Lei J Ding; Hannah M Schlüter; Matthew J Szucs; Rushdy Ahmad; Zheyang Wu; Weifeng Xu
Journal:  J Proteome Res       Date:  2019-12-26       Impact factor: 4.466

9.  Prediction of extraprostatic extension on multi-parametric magnetic resonance imaging in patients with anterior prostate cancer.

Authors:  Hyungwoo Ahn; Sung Il Hwang; Hak Jong Lee; Hyoung Sim Suh; Gheeyoung Choe; Seok-Soo Byun; Sung Kyu Hong; Sangchul Lee; Joongyub Lee
Journal:  Eur Radiol       Date:  2019-08-05       Impact factor: 5.315

10.  On the use of min-max combination of biomarkers to maximize the partial area under the ROC curve.

Authors:  Hua Ma; Susan Halabi; Aiyi Liu
Journal:  J Probab Stat       Date:  2019-02-03
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.