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Comparing dependent kappa coefficients obtained on multilevel data

Reliability and agreement are two notions of paramount importance in medical and behavioral sciences. They provide information about the quality of the measurements. When the scale is categorical, reliability and agreement can be quantified through different kappa coefficients. The present paper pro...

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Autor principal: Vanbelle, Sophie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5600130/
https://www.ncbi.nlm.nih.gov/pubmed/28464322
http://dx.doi.org/10.1002/bimj.201600093
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author Vanbelle, Sophie
author_facet Vanbelle, Sophie
author_sort Vanbelle, Sophie
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description Reliability and agreement are two notions of paramount importance in medical and behavioral sciences. They provide information about the quality of the measurements. When the scale is categorical, reliability and agreement can be quantified through different kappa coefficients. The present paper provides two simple alternatives to more advanced modeling techniques, which are not always adequate in case of a very limited number of subjects, when comparing several dependent kappa coefficients obtained on multilevel data. This situation frequently arises in medical sciences, where multilevel data are common. Dependent kappa coefficients can result from the assessment of the same individuals at various occasions or when each member of a group is compared to an expert, for example. The method is based on simple matrix calculations and is available in the R package “multiagree”. Moreover, the statistical properties of the proposed method are studied using simulations. Although this paper focuses on kappa coefficients, the method easily extends to other statistical measures.
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spelling pubmed-56001302017-10-02 Comparing dependent kappa coefficients obtained on multilevel data Vanbelle, Sophie Biom J Other Topics Reliability and agreement are two notions of paramount importance in medical and behavioral sciences. They provide information about the quality of the measurements. When the scale is categorical, reliability and agreement can be quantified through different kappa coefficients. The present paper provides two simple alternatives to more advanced modeling techniques, which are not always adequate in case of a very limited number of subjects, when comparing several dependent kappa coefficients obtained on multilevel data. This situation frequently arises in medical sciences, where multilevel data are common. Dependent kappa coefficients can result from the assessment of the same individuals at various occasions or when each member of a group is compared to an expert, for example. The method is based on simple matrix calculations and is available in the R package “multiagree”. Moreover, the statistical properties of the proposed method are studied using simulations. Although this paper focuses on kappa coefficients, the method easily extends to other statistical measures. John Wiley and Sons Inc. 2017-05-02 2017-09 /pmc/articles/PMC5600130/ /pubmed/28464322 http://dx.doi.org/10.1002/bimj.201600093 Text en © 2017 The Author. Biometrical Journal Published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Other Topics
Vanbelle, Sophie
Comparing dependent kappa coefficients obtained on multilevel data
title Comparing dependent kappa coefficients obtained on multilevel data
title_full Comparing dependent kappa coefficients obtained on multilevel data
title_fullStr Comparing dependent kappa coefficients obtained on multilevel data
title_full_unstemmed Comparing dependent kappa coefficients obtained on multilevel data
title_short Comparing dependent kappa coefficients obtained on multilevel data
title_sort comparing dependent kappa coefficients obtained on multilevel data
topic Other Topics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5600130/
https://www.ncbi.nlm.nih.gov/pubmed/28464322
http://dx.doi.org/10.1002/bimj.201600093
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