Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study

A comprehensive simulation study is presented to assess the performance of different adjustment methods including the novel application of several regression-based methods (which are commonly applied to detect publication bias rather than adjust for it) and the popular Trim & Fill algorithm. Meta-analyses with binary outcomes, analysed on the log odds ratio scale, were simulated by considering scenarios with and without i) publication bias and; ii) heterogeneity. Publication bias was induced through two underlying mechanisms assuming the probability of publication depends on i) the study effect size; or ii) the p-value.The performance of all methods tended to worsen as unexplained heterogeneity increased and the number of studies in the meta-analysis decreased. Applying the methods conditional on an initial test for the presence of funnel plot asymmetry generally provided poorer performance than the unconditional use of the adjustment method. Several of the regression based methods consistently outperformed the Trim & Fill estimators.Regression-based adjustments for publication bias and other small study effects are easy to conduct and outperformed more established methods over a wide range of simulation scenarios.Publication bias (PB) has the potential to distort the scientific literature [1,2]; since it is the "interest level", or statistical significance of findings, not study rigour or quality, that determines which research gets published and is subsequently available [3]. A meta-analysis of the published literature will be biased and may adversely affect decision making if PB exists.More generally, the tendency for smaller studies to show a greater effect than larger studies, when evaluating interventions, has been named small-study effects [4]. These could be due to publication bias, or other factors. Any factor which confounds the relationship between-study effect and study size may cause small-study effects. For example, if an intervention is more effective