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Is There a "Best" Way to Detect and Minimize Publication Bias?
An Empirical Evaluation
Ba Pham
Childrens Hospital of Eastern Ontario Research Institute
Robert Platt
McGill University
Laura McAuley
Canadian Coordinating Office for Health Technology Assessment (CCOHTA)
Terry P. Klassen
University of Alberta
David Moher
University of Ottawa
Using 14 meta-analyses that included both published (n = 199) and unpublished (n = 50) randomized trials, we evaluated the utility of different analytical approaches to detect, assess robustness, and minimize publication bias in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 Rosenthals 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.
Evaluation & the Health Professions, Vol. 24, No. 2,
109-125 (2001)
DOI: 10.1177/016327870102400202

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