Literature DB >> 29609206

Relationship between Roe and Metz simulation model for multireader diagnostic data and Obuchowski-Rockette model parameters.

Stephen L Hillis1.   

Abstract

For the typical diagnostic radiology study design, each case (ie, patient) undergoes several diagnostic tests (or modalities) and the resulting images are interpreted by several readers. Often, each reader is asked to assign a confidence-of-disease rating to each case for each test, and the diagnostic tests are compared with respect to reader-performance outcomes that are functions of the reader receiver operating characteristic (ROC) curves, such as the area under the ROC curve. These reader-performance outcomes are frequently analyzed using the Obuchowski and Rockette method, which allows conclusions to generalize to both the reader and case populations. The simulation model proposed by Roe and Metz (RM) in 1997 emulates confidence-of-disease data collected from such studies and has been an important tool for empirically evaluating various reader-performance analysis methods. However, because the RM model parameters are expressed in terms of a continuous decision variable rather than in terms of reader-performance outcomes, it has not been possible to evaluate the realism of the RM model. I derive the relationships between the RM and Obuchowski-Rockette model parameters for the empirical area under the ROC curve reader-performance outcome. These relationships make it possible to evaluate the realism of the RM parameter models and to assess the performance of Obuchowski-Rockette parameter estimates. An example illustrates the application of the relationships for assessing the performance of a proposed upper one-sided confidence bound for the Obuchowski-Rockette test-by-reader variance component, which is useful for sample size estimation.
Copyright © 2018 John Wiley & Sons, Ltd.

Entities:  

Keywords:  Obuchowski-Rockette; Roe and Metz model; diagnostic radiology; receiver operating characteristic (ROC) curve

Mesh:

Year:  2018        PMID: 29609206      PMCID: PMC5980727          DOI: 10.1002/sim.7616

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


  40 in total

1.  Continuous versus categorical data for ROC analysis: some quantitative considerations.

Authors:  R F Wagner; S V Beiden; C E Metz
Journal:  Acad Radiol       Date:  2001-04       Impact factor: 3.173

2.  Components-of-variance models and multiple-bootstrap experiments: an alternative method for random-effects, receiver operating characteristic analysis.

Authors:  S V Beiden; R F Wagner; G Campbell
Journal:  Acad Radiol       Date:  2000-05       Impact factor: 3.173

3.  Visual detection and localization of radiographic images.

Authors:  S J Starr; C E Metz; L B Lusted; D J Goodenough
Journal:  Radiology       Date:  1975-09       Impact factor: 11.105

4.  Observer studies involving detection and localization: modeling, analysis, and validation.

Authors:  Dev P Chakraborty; Kevin S Berbaum
Journal:  Med Phys       Date:  2004-08       Impact factor: 4.071

5.  Multireader multicase reader studies with binary agreement data: simulation, analysis, validation, and sizing.

Authors:  Weijie Chen; Adam Wunderlich; Nicholas Petrick; Brandon D Gallas
Journal:  J Med Imaging (Bellingham)       Date:  2014-12-04

6.  A comparison of denominator degrees of freedom methods for multiple observer ROC analysis.

Authors:  Stephen L Hillis
Journal:  Stat Med       Date:  2007-02-10       Impact factor: 2.373

7.  One-shot estimate of MRMC variance: AUC.

Authors:  Brandon D Gallas
Journal:  Acad Radiol       Date:  2006-03       Impact factor: 3.173

8.  Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis.

Authors:  Stephen L Hillis; Kevin S Berbaum; Charles E Metz
Journal:  Acad Radiol       Date:  2008-05       Impact factor: 3.173

9.  The use of the 'binormal' model for parametric ROC analysis of quantitative diagnostic tests.

Authors:  J A Hanley
Journal:  Stat Med       Date:  1996-07-30       Impact factor: 2.373

10.  Sample size tables for computer-aided detection studies.

Authors:  Nancy A Obuchowski; Stephen L Hillis
Journal:  AJR Am J Roentgenol       Date:  2011-11       Impact factor: 3.959

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  7 in total

1.  Paired split-plot designs of multireader multicase studies.

Authors:  Weijie Chen; Qi Gong; Brandon D Gallas
Journal:  J Med Imaging (Bellingham)       Date:  2018-05-17

2.  Determining Roe and Metz model parameters for simulating multireader multicase confidence-of-disease rating data based on real-data or conjectured Obuchowski-Rockette parameter estimates.

Authors:  Stephen L Hillis
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2020-03-17

3.  Multi-reader multi-case analysis of variance software for diagnostic performance comparison of imaging modalities.

Authors:  Brian J Smith; Stephen L Hillis
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2020-03-16

4.  Determining Roe and Metz model parameters for simulating multireader multicase confidence-of-disease rating data based on real-data or conjectured Obuchowski-Rockette parameter estimates.

Authors:  Stephen L Hillis; Brian J Smith; Weijie Chen
Journal:  J Med Imaging (Bellingham)       Date:  2022-07-08

5.  MATLAB toolbox for ROC analysis of multi-reader multi-case diagnostic imaging studies.

Authors:  Brian J Smith; Stephen L Hillis
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2022-04-04

6.  Identical-test Roe and Metz simulation model for validating multi-reader methods of analysis for comparing different radiologic imaging modalities.

Authors:  Stephen L Hillis
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2022-04-04

7.  Multireader sample size program for diagnostic studies: demonstration and methodology.

Authors:  Stephen L Hillis; Kevin M Schartz
Journal:  J Med Imaging (Bellingham)       Date:  2018-11-30
  7 in total

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