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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2015
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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. |
format | Online Article Text |
id | pubmed-4460648 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT simmondsmark quantifyingtheriskoferrorwheninterpretingfunnelplots |