Detecting Publication and Reporting Bias in Multilevel Meta-Analyses: A Simulation Study

• In Event: 43.072 - Learning About Meta-Analysis and Meta-Analyses About Learning: Methods and Applications of Research Synthesis

Abstract

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 both were generated. Egger’s Regression and Funnel Plot test were extended to three-level models, and possible cutoffs for the L_0^+ 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 their performance depends mainly on the population effect size value and on the total variance.

Authors

• Belén Fernández-Castilla, University of Leuven

• Lies Declercq, KU Leuven

• Laleh Jamshidi, KU Leuven

• Tasha Beretvas, The University of Texas at Austin

• Patrick Onghena, KU Leuven

• Wim Van den Noortgate, KU Leuven