Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1782201863362314240 |
---|---|
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. |
format | Text |
id | pubmed-3077320 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT zhaowei maximumlikelihoodestimationofreviewersacumenincentralreviewsettingcategoricaldata AT boyettjamesm maximumlikelihoodestimationofreviewersacumenincentralreviewsettingcategoricaldata AT kocakmehmet maximumlikelihoodestimationofreviewersacumenincentralreviewsettingcategoricaldata AT ellisondavidw maximumlikelihoodestimationofreviewersacumenincentralreviewsettingcategoricaldata AT wuyanan maximumlikelihoodestimationofreviewersacumenincentralreviewsettingcategoricaldata |