Detecting Selection Bias in Meta-Analyses with Multiple Outcomes: A Simulation Study

This study explores the performance of classical methods for detecting publication bias-namely, Egger's regression test, Funnel Plot test, Begg's Rank Correlation and Trim and Fill method-in meta-analysis of studies that report multiple effects. Publication bias, outcome reporting bias, an...

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Bibliographic Details
Published inThe Journal of experimental education Vol. 89; no. 1; pp. 125 - 144
Main Authors Fernández-Castilla, Belén, Declercq, Lies, Jamshidi, Laleh, Beretvas, S. Natasha, Onghena, Patrick, Van den Noortgate, Wim
Format Journal Article
LanguageEnglish
Published Washington Routledge 02.01.2021
Taylor & Francis Inc
Subjects
Bias
Effect Size
Error of Measurement
Meta Analysis
multiple effect sizes
publication bias
Regression (Statistics)
selective outcome reporting bias
Simulation
simulation study
Statistical Bias
Systematic review
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Summary:This study explores the performance of classical methods for detecting publication bias-namely, Egger's regression test, Funnel Plot test, Begg's Rank Correlation and Trim and Fill method-in meta-analysis of studies that report multiple effects. Publication bias, outcome reporting bias, and a combination of these were generated. Egger's regression test and the Funnel Plot test were extended to three-level models, and possible cutoffs for the estimator of the Trim and Fill method were explored. Furthermore, we checked whether the combination of results of several methods yielded a better control of Type I error rates. Results show that no method works well across all conditions and that performance depends mainly on the population effect size value and the total variance.
ISSN:0022-0973
1940-0683
DOI:10.1080/00220973.2019.1582470