Tom M. Palmer () (Department of Heath Sciences, University of Leicester) Jaime L. Peters (School of Mathematical Sciences, Queensland University of Technology) Alex J. Sutton (Department of Heath Sciences, University of Leicester) Santiago G. Moreno (Department of Heath Sciences, University of Leicester)
Abstract
Funnel plots are commonly used to investigate publication and related biases in meta-analysis. Although asymmetry in the appearance of a funnel plot is often interpreted as being caused by publication bias, in reality the asymmetry could be due to other factors that cause systematic differences in the results of large and small studies, for example, confounding factors such as differential study quality. Funnel plots can be enhanced by adding contours of statistical significance to aid in interpreting the funnel plot. If studies appear to be missing in areas of low statistical significance, then it is possible that the asymmetry is due to publication bias. If studies appear to be missing in areas of high statistical significance, then publication bias is a less likely cause of the funnel asymmetry. It is proposed that this enhancement to funnel plots should be used routinely for meta-analyses where it is possible that results could be suppressed on the basis of their statistical significance. Copyright 2008 by StataCorp LP.
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Article provided by StataCorp LP in its journal Stata Journal.