Correcting for outcome reporting bias in a meta-analysis: A meta-regression approach

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.

结果报告偏差（ORB）是指研究者基于统计显著性选择性地报告结果所导致的偏差效应。ORB导致仅在报告具有最大效应大小的结果时，元分析中平均效应大小估计值被夸大。我们提出了一种新的方法（CORB），用于纠正ORB，该方法将结果效应大小的变异性估计作为元回归模型中的调节因素。通过假设结果之间的相关性，可以计算出结果效应大小的变异性估计。蒙特卡洛模拟研究结果指出，在未对ORB进行任何纠正的情况下，元分析中的效应大小可能被严重高估。当由ORB引起的过度估计最大时，CORB方法能够准确估计效应大小。将该方法应用于关于玩暴力电子游戏对攻击性认知影响的元分析中，发现在纠正ORB后，平均效应大小估计值有所下降。我们建议在所有元分析中常规应用纠正ORB的方法。我们提供了注释的R代码和函数，以方便研究人员应用CORB方法。