Correcting for outcome reporting bias in a meta-analysis: A meta-regression approach
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Description: Outcome reporting bias (ORB) refers to the biasing effect caused by researchers selectively reporting outcomes based on their statistical significance. ORB leads to inflated average effect size estimates in a meta-analysis if only the outcome with the largest effect size is reported due to ORB. We propose a new method (CORB) to correct for ORB that includes an estimate of the variability of the outcomes' effect size as a moderator in a meta-regression model. An estimate of the variability of the outcomes' effect size can be computed by assuming a correlation among the outcomes. Results of a Monte-Carlo simulation study showed that effect size in meta-analyses may be severely overestimated without any correction for ORB. The CORB method accurately estimates effect size when overestimation caused by ORB is the largest. Applying the new method to a meta-analysis on the effect of playing violent video games on aggressive cognition showed that the average effect size estimate decreased when correcting for ORB. We recommend to routinely apply methods to correct for ORB in any meta-analysis. We provide annotated R code and functions to facilitate researchers to apply the CORB method.