View article

Although primary studies often report multiple outcomes, the covariances between these outcomes are rarely reported. This leads to difficulties when combining studies in a meta-analysis. This problem was recently addressed with the introduction of robust variance estimation. This new method enables the estimation of meta-regression models with dependent effect sizes, even when the dependence structure is unknown. Although robust variance estimation has been shown to perform well when the number of studies in the meta-analysis is large, previous simulation studies suggest that the associated tests often have Type I error rates that are much larger than nominal. In this article, I introduce 6 estimators with better small sample properties and study the effectiveness of these estimators via 2 simulation studies. The results of these simulations suggest that the best estimator involves correcting both the residuals …