Variants of Regression Tests for Assessing Publication Bias

Music City Center 

Publication bias has long been a critical issue in meta-analysis that compromises the certainty of synthesized evidence. Egger's regression test is one of the most popular methods to detect the presence of publication bias by examining the asymmetry of funnel plot. We proposed five variants of Egger-type regression tests incorporating different assumptions for the error term in the model, within both fixed-effect and random-effects settings. This work aims to empirically evaluate the performance for the Egger regression variants. We implemented five Egger-type regressions to a collection of 51 high-quality meta-analyses from the BMJ papers focusing on medical research. Cohen's kappa was utilized to assess the pairwise agreement among the different regression tests, with kappa values varying from approximately 50% to over 90%, indicating moderate to almost perfect agreement. Given the variation in empirical evaluation observed among the Egger-type regressions, it is crucial for meta-analysts to choose and specify the error term employed in Egger regression test when assessing publication bias in practice .

meta-analysis

publication bias

Egger's regression 

Health Policy Statistics Section