2019
DOI: 10.1002/jrsm.1332
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Testing for funnel plot asymmetry of standardized mean differences

Abstract: Publication bias and other forms of outcome reporting bias are critical threats to the validity of findings from research syntheses. A variety of methods have been proposed for detecting selective outcome reporting in a collection of effect size estimates, including several methods based on assessment of asymmetry of funnel plots, such as the Egger's regression test, the rank correlation test, and the Trim‐and‐Fill test. Previous research has demonstated that the Egger's regression test is miscalibrated when a… Show more

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Cited by 220 publications
(172 citation statements)
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“…The funnel plot asymmetry test examines the association between the magnitude of effect size estimates and a measure of their precision. To measure precision, we used the scaled standard error, which was calculated as the standard error of the numerator of the effect size estimate, divided by the denominator of the effect size estimate; the scaled standard error was used to avoid an artifactual association between the effect size estimate and its standard error . The funnel plot asymmetry test also used robust variance estimation to account for the dependence of effect size estimates nested within studies.…”
Section: Methodsmentioning
confidence: 99%
“…The funnel plot asymmetry test examines the association between the magnitude of effect size estimates and a measure of their precision. To measure precision, we used the scaled standard error, which was calculated as the standard error of the numerator of the effect size estimate, divided by the denominator of the effect size estimate; the scaled standard error was used to avoid an artifactual association between the effect size estimate and its standard error . The funnel plot asymmetry test also used robust variance estimation to account for the dependence of effect size estimates nested within studies.…”
Section: Methodsmentioning
confidence: 99%
“…The most commonly used methods are visual inspection of a funnel plot and Egger’s test of funnel plot symmetry (Sterne, Egger, & Moher, 2011). These methods, however, do not perform well particularly when there is heterogeneity among effect sizes (Macaskill, Walter, & Irwig, 2001; Pustejovsky & Rodgers, 2019). It is increasingly common for meta-analysts to use selection modeling strategies to explore the robustness of meta-analysis to publication bias (Citkowicz & Vevea, 2017; Vevea & Hedges, 1995).…”
Section: Best Practice Meta-analysismentioning
confidence: 99%
“…In other words, the method assumes that the probability of an effect not to be suppressed is a function of its p-value. As recommended by Pustejovsky and Rodgers (2019), the weights used in the publication bias analyses were not a function of the effect sizes (for more details, see Appendices C and D in the Supplemental Online Materials). We performed these analyses with the Metafor R package (Viechtbauer, 2010).…”
Section: Publication Biasmentioning
confidence: 99%