Testing Categorical Moderators in Mixed-Effects Meta-analysis in the Presence of Heteroscedasticity

Abstract

Mixed-effects models can be used to examine the association between a categorical moderator and the magnitude of the effect size. Two approaches are available to estimate the residual between-studies variance, ${\tau }_{res}^2$—namely, separate estimation within each category of the moderator versus pooled estimation across all categories. We examine, by means of a Monte Carlo simulation study, both approaches for ${\tau }_{res}^2$ estimation in combination with two methods, the Wald-type ${\chi }^2$ and F tests, to test the statistical significance of the moderator. Results suggest that the F test using a pooled estimate of ${\tau }_{res}^2$ across categories is the best option in most conditions, although the F test using separate estimates of ${\tau }_{res}^2$ is preferable if the residual heterogeneity variances are heteroscedastic.

Keywords:

• Meta-analysis
• mixed-effects model
• subgroup analyses
• residual between-studies variance

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.