Egger’s test is commonly used to assess potential publication bias in a meta-analysis via funnel plot asymmetry (Egger’s test is a linear regression of the intervention effect estimates on their standard errors weighted by their inverse variance). The performance of Egger’s and related tests has been extensively studied for binary outcomes, but not for continuous ones. Comparative continuous outcomes are commonly measured on an absolute (mean) difference scale, and it is not uncommon for the magnitude of effect to be related to response in the control arm (i.e. baseline risk). When this is the case, funnel plots can appear highly asymmetric, even when publication bias is not present, since correlations between outcome and both effect size and its standard error exist. Through application to a motivating collection of meta-analyses of post-operative analgesics, and simulation studies, we will show that Egger’s test is potentially misleading for continuous outcomes and a test which regresses the residuals from a meta-regression model, including baseline risk as a study-level covariate, against inverse sample size has better statistical properties.
This session is highly relevant to reviewers, editors and statisticians with interests in dealing with bias in meta-analysis. Although there is some statistical content, concepts will be explained visually where possible keeping much of the material accessible.
Suzanne Freeman is an NIHR Research Fellow and member of the NIHR Complex Reviews Support Unit. Her research interests include network meta-analysis, individual participant data meta-analysis, synthesis of time-to-event and continuous outcomes and diagnostic test accuracy meta-analysis.
Alex Sutton has had a long-standing interest in methodology for evidence synthesis and has worked on developing methods for publication bias, diagnostic test accuracy meta-analysis and network meta-analysis. He is currently particularly interested in methods for visual communication of information and its application to synthesis methods.
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