Identifying and quantifying variation between healthcare organisations and geographical regions: using mixed-effects models

When the degree of variation between healthcare organisations or geographical regions is quantified, there is often a failure to account for the role of chance, which can lead to an overestimation of the true variation. Mixed-effects models account for the role of chance and estimate the true/underl...

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Detalles Bibliográficos
Autores principales: Abel, Gary, Elliott, Marc N
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2019
Materias:
Research and Reporting Methodology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6934242/
https://www.ncbi.nlm.nih.gov/pubmed/31533954
http://dx.doi.org/10.1136/bmjqs-2018-009165
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author Abel, Gary
Elliott, Marc N
author_facet Abel, Gary
Elliott, Marc N
author_sort Abel, Gary
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description When the degree of variation between healthcare organisations or geographical regions is quantified, there is often a failure to account for the role of chance, which can lead to an overestimation of the true variation. Mixed-effects models account for the role of chance and estimate the true/underlying variation between organisations or regions. In this paper, we explore how a random intercept model can be applied to rate or proportion indicators and how to interpret the estimated variance parameter.
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spelling pubmed-69342422020-01-06 Identifying and quantifying variation between healthcare organisations and geographical regions: using mixed-effects models Abel, Gary Elliott, Marc N BMJ Qual Saf Research and Reporting Methodology When the degree of variation between healthcare organisations or geographical regions is quantified, there is often a failure to account for the role of chance, which can lead to an overestimation of the true variation. Mixed-effects models account for the role of chance and estimate the true/underlying variation between organisations or regions. In this paper, we explore how a random intercept model can be applied to rate or proportion indicators and how to interpret the estimated variance parameter. BMJ Publishing Group 2019-12 2019-09-18 /pmc/articles/PMC6934242/ /pubmed/31533954 http://dx.doi.org/10.1136/bmjqs-2018-009165 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Research and Reporting Methodology
Abel, Gary
Elliott, Marc N
Identifying and quantifying variation between healthcare organisations and geographical regions: using mixed-effects models
title Identifying and quantifying variation between healthcare organisations and geographical regions: using mixed-effects models
title_full Identifying and quantifying variation between healthcare organisations and geographical regions: using mixed-effects models
title_fullStr Identifying and quantifying variation between healthcare organisations and geographical regions: using mixed-effects models
title_full_unstemmed Identifying and quantifying variation between healthcare organisations and geographical regions: using mixed-effects models
title_short Identifying and quantifying variation between healthcare organisations and geographical regions: using mixed-effects models
title_sort identifying and quantifying variation between healthcare organisations and geographical regions: using mixed-effects models
topic Research and Reporting Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6934242/
https://www.ncbi.nlm.nih.gov/pubmed/31533954
http://dx.doi.org/10.1136/bmjqs-2018-009165
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