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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Veröffentlicht in: | The Journal of experimental education 2021-01, Vol.89 (1), p.125-144 |
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Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 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. |
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ISSN: | 0022-0973 1940-0683 |
DOI: | 10.1080/00220973.2019.1582470 |