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The article offers an overview of meta-regression models and possible assumptions that can be employed in mental health disorder trials to account for differences in baseline characteristics of participants between the studies of a meta-analysis. Two datasets of antidepressant trials including fluoxetine, venlafaxine, nefazodone and parosetine that applied the meta-regression models versus placebo are presented. The paper investigates the underlying risks for developing depression.

Objective: Publication bias undermines the integrity of published research. The aim of this paper is to present a synopsis of methods for exploring and accounting for publication bias. Methods: We discussed the main features of the following methods to assess publication bias: funnel plot analysis; trim-and-fill methods; regression techniques and selection models. We applied these methods to a well-known example of antidepressants trials that compared trials submitted to the Food and Drug Administration (FDA) for regulatory approval. Results: The funnel plot-related methods (visual inspection, trim-and-fill, regression models) revealed an association between effect size and SE. Contours of statistical significance showed that asymmetry in the funnel plot is probably due to publication bias. Selection model found a significant correlation between effect size and propensity for publication. Conclusions: Researchers should always consider the possible impact of publication bias. Funnel plot-related methods should be seen as a means of examining for small-study effects and not be directly equated with publication bias. Possible causes for funnel plot asymmetry should be explored. Contours of statistical significance may help disentangle whether asymmetry in a funnel plot is caused by publication bias or not. Selection models, although underused, could be useful resource when publication bias and heterogeneity are suspected because they address directly the problem of publication bias and not that of smal-study effects. [ABSTRACT FROM AUTHOR]