A simulation study to compare robust tests for linear mixed-effects meta-regression

The explanation of heterogeneity when synthesizing different studies is an important issue in meta-analysis. Besides including a heterogeneity parameter in the statistical model, it is also important to understand possible causes of between-study heterogeneity. One possibility is to incorporate study-specific covariates in the model that account for between-study variability. This leads to linear mixed-effects meta-regression models. A number of alternative methods have been proposed to estimate the (co)variance of the estimated regression coefficients in these models, which subsequently drives differences in the results of statistical methods. To quantify this, we compare the performance of hypothesis tests for moderator effects based upon different heteroscedasticity consistent covariance matrix estimators and the (untruncated) Knapp-Hartung method in an extensive simulation study. In particular, we investigate type 1 error and power under varying conditions regarding the underlying distributions, heterogeneity, effect sizes, number of independent studies, and their sample sizes. Based upon these results, we give recommendations for suitable inference choices in different scenarios and highlight the danger of using tests regarding the study-specific moderators based on inappropriate covariance estimators.