Exploring and accounting for publication bias in mental health: a brief overview of methods. Issue 1 (29th January 2014)

• Record Type: Journal Article
• Authors: Mavridis, Dimitris; Salanti, Georgia
• Is Part Of: Evidence-based mental health. Volume 17:Issue 1(2014)
• Page Start: 11
• Page End: 15
• Publication Date: 2014-01-29
• Subjects: Psychotherapy -- Periodicals; Psychiatry -- Periodicals; Mental health -- Periodicals; 616.891
• DOI: 10.1136/eb-2013-101700
• Languages: English
• ISSNs: 1362-0347

Abstract

Abstract : 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 small-study effects.