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Maximum likelihood estimation of reviewers' acumen in central review setting: categorical data

Successfully evaluating pathologists' acumen could be very useful in improving the concordance of their calls on histopathologic variables. We are proposing a new method to estimate the reviewers' acumen based on their histopathologic calls. The previously proposed method includes redundan...

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Detalles Bibliográficos
Autores principales: Zhao, Wei, Boyett, James M, Kocak, Mehmet, Ellison, David W, Wu, Yanan
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3077320/
https://www.ncbi.nlm.nih.gov/pubmed/21439071
http://dx.doi.org/10.1186/1742-4682-8-3
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author Zhao, Wei
Boyett, James M
Kocak, Mehmet
Ellison, David W
Wu, Yanan
author_facet Zhao, Wei
Boyett, James M
Kocak, Mehmet
Ellison, David W
Wu, Yanan
author_sort Zhao, Wei
collection PubMed
description Successfully evaluating pathologists' acumen could be very useful in improving the concordance of their calls on histopathologic variables. We are proposing a new method to estimate the reviewers' acumen based on their histopathologic calls. The previously proposed method includes redundant parameters that are not identifiable and results are incorrect. The new method is more parsimonious and through extensive simulation studies, we show that the new method relies less on the initial values and converges to the true parameters. The result of the anesthetist data set by the new method is more convincing.
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spelling pubmed-30773202011-04-15 Maximum likelihood estimation of reviewers' acumen in central review setting: categorical data Zhao, Wei Boyett, James M Kocak, Mehmet Ellison, David W Wu, Yanan Theor Biol Med Model Research Successfully evaluating pathologists' acumen could be very useful in improving the concordance of their calls on histopathologic variables. We are proposing a new method to estimate the reviewers' acumen based on their histopathologic calls. The previously proposed method includes redundant parameters that are not identifiable and results are incorrect. The new method is more parsimonious and through extensive simulation studies, we show that the new method relies less on the initial values and converges to the true parameters. The result of the anesthetist data set by the new method is more convincing. BioMed Central 2011-03-25 /pmc/articles/PMC3077320/ /pubmed/21439071 http://dx.doi.org/10.1186/1742-4682-8-3 Text en Copyright ©2011 Zhao et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Zhao, Wei
Boyett, James M
Kocak, Mehmet
Ellison, David W
Wu, Yanan
Maximum likelihood estimation of reviewers' acumen in central review setting: categorical data
title Maximum likelihood estimation of reviewers' acumen in central review setting: categorical data
title_full Maximum likelihood estimation of reviewers' acumen in central review setting: categorical data
title_fullStr Maximum likelihood estimation of reviewers' acumen in central review setting: categorical data
title_full_unstemmed Maximum likelihood estimation of reviewers' acumen in central review setting: categorical data
title_short Maximum likelihood estimation of reviewers' acumen in central review setting: categorical data
title_sort maximum likelihood estimation of reviewers' acumen in central review setting: categorical data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3077320/
https://www.ncbi.nlm.nih.gov/pubmed/21439071
http://dx.doi.org/10.1186/1742-4682-8-3
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