Is There a “Best” Way to Detect and Minimize Publication Bias?

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Abstract
Using 14 meta-analyses that included both published (n = 199) and unpublished (n = 50) randomized trials, we evaluated the utility of different analytical approaches to detect, assess robustness, and minimize publication bias in meta-analysis. The rank correlation and graphical tests indicated funnel plot asymmetry in 3 and 7 of the 14 meta-analyses, respectively. The file drawer number estimates using Iyengar-Greenhouse method were between 1.5 and 4.7 times smaller compared to Rosenthal’s estimates. The median difference between the Trim and Fill estimates and the actual number of missing studies was 1 (range -4, 6). Weighted estimation methods adjusted for publication bias and provided estimates of intervention effect close to the reference standard, on average. We showed there are differences in the conclusions one would reach clinically based on the different analytical approaches dealing with publication bias. Our results also suggest that the appropriate use of these methods improves the reliability and accuracy of meta-analysis.