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Quantifying the risk of error when interpreting funnel plots

BACKGROUND: Funnel plots are widely used to investigate possible publication bias in meta-analyses. There has, however, been little formal assessment of whether a visual inspection of a funnel plot is sufficient to identify publication bias. METHODS: Visual assessment of bias in a funnel plot is qua...

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Autor principal: Simmonds, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4460648/
https://www.ncbi.nlm.nih.gov/pubmed/25875027
http://dx.doi.org/10.1186/s13643-015-0004-8
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author Simmonds, Mark
author_facet Simmonds, Mark
author_sort Simmonds, Mark
collection PubMed
description BACKGROUND: Funnel plots are widely used to investigate possible publication bias in meta-analyses. There has, however, been little formal assessment of whether a visual inspection of a funnel plot is sufficient to identify publication bias. METHODS: Visual assessment of bias in a funnel plot is quantified using two new statistics: the Imbalance and the Asymmetry Distance, both intended to replicate how a funnel plot is typically assessed. A simulation study was performed to assess the performance of these two statistics for identifying publication bias. RESULTS: The two statistics both have high type I error and low statistical power, unless the number of studies in the meta-analysis is very large. These results suggest that visual inspection of a funnel plot is unlikely to lead to a valid assessment of publication bias. CONCLUSIONS: In most systematic reviews, visual inspection of a funnel plot may give a misleading impression of the presence or absence of publication bias. Formal statistical tests for bias should generally be preferred. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13643-015-0004-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-44606482015-06-10 Quantifying the risk of error when interpreting funnel plots Simmonds, Mark Syst Rev Methodology BACKGROUND: Funnel plots are widely used to investigate possible publication bias in meta-analyses. There has, however, been little formal assessment of whether a visual inspection of a funnel plot is sufficient to identify publication bias. METHODS: Visual assessment of bias in a funnel plot is quantified using two new statistics: the Imbalance and the Asymmetry Distance, both intended to replicate how a funnel plot is typically assessed. A simulation study was performed to assess the performance of these two statistics for identifying publication bias. RESULTS: The two statistics both have high type I error and low statistical power, unless the number of studies in the meta-analysis is very large. These results suggest that visual inspection of a funnel plot is unlikely to lead to a valid assessment of publication bias. CONCLUSIONS: In most systematic reviews, visual inspection of a funnel plot may give a misleading impression of the presence or absence of publication bias. Formal statistical tests for bias should generally be preferred. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13643-015-0004-8) contains supplementary material, which is available to authorized users. BioMed Central 2015-03-11 /pmc/articles/PMC4460648/ /pubmed/25875027 http://dx.doi.org/10.1186/s13643-015-0004-8 Text en © Simmonds. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology
Simmonds, Mark
Quantifying the risk of error when interpreting funnel plots
title Quantifying the risk of error when interpreting funnel plots
title_full Quantifying the risk of error when interpreting funnel plots
title_fullStr Quantifying the risk of error when interpreting funnel plots
title_full_unstemmed Quantifying the risk of error when interpreting funnel plots
title_short Quantifying the risk of error when interpreting funnel plots
title_sort quantifying the risk of error when interpreting funnel plots
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4460648/
https://www.ncbi.nlm.nih.gov/pubmed/25875027
http://dx.doi.org/10.1186/s13643-015-0004-8
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