Approaches for identifying and managing publication bias in meta-analysis

Abstract

The consequences of publication bias in meta-analysis pose significant risks, potentially leading to erroneous conclusions within the meta-analytic framework. The objective of this article was to explore the methodologies for identifying publication bias and approaches for mitigating its effects. The techniques employed to detect publication bias can generally be distinguished into two major categories: graphical and statistical methodologies. Graphical approaches utilize techniques such as funnel plots and meta-plots, which visually depict the distribution of effect sizes and standard errors across studies. Statistical methods encompass various computations, including Fail-Safe N, rank correlation, Egger regression, tests for excess significance (TES), and selection models tailored for evaluating publication bias through quantitative analyses. The combination of these methods is recommended for a more comprehensive assessment, rather than relying on individual approaches. Methods for addressing publication bias include the trim and fill (T&F) method, Publication Error and True Effect Size Estimation (PET-PEESE) method, and the Weight-Function Model, each offering unique strategies for adjusting effect size estimates. The selection of these methods should consider the specific characteristics of the meta-analysis under consideration, ensuring the most appropriate approach is employed. Publication bias poses a significant risk in the field of meta-analysis, and selecting methods for its identification and mitigation requires comprehensive consideration