Abstract
BackgroundFunnel 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.MethodsVisual 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.ResultsThe 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.ConclusionsIn 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 materialThe online version of this article (doi:10.1186/s13643-015-0004-8) contains supplementary material, which is available to authorized users.
Highlights
Funnel plots are widely used to investigate possible publication bias in meta-analyses
Publication bias arises where studies with results that go against the prior opinion of the authors or are not statistically significant are not published, and so are not included in a systematic review and meta-analysis, leading to biassed conclusions [1]
These values can be taken to represent the level of Imbalance needed to reject the null hypothesis of no funnel plot asymmetry
Summary
Funnel plots are widely used to investigate possible publication bias in meta-analyses. Publication bias arises where studies with results that go against the prior opinion of the authors (such as when a new drug is less effective than placebo) or are not statistically significant are not published, and so are not included in a systematic review and meta-analysis, leading to biassed conclusions [1]. A commonly used method to assess whether this is the case is to examine a funnel plot This is a plot of the estimate of effect size in each study against an estimate of its precision (typically its standard error) [2]. If there is publication bias, studies with low precision that have negative or non-significant results will be missing from the plot because they were not published, producing a funnel plot that is asymmetric
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