  Meta-analysis is an important statistical technique for synthesizing the
results of multiple studies regarding the same or closely related research
question. So-called meta-regression extends meta-analysis models by accounting
for studylevel covariates. Mixed-effects meta-regression models provide a
powerful tool for evidence synthesis, by appropriately accounting for
betweem-study heterogeneity. In fact, modelling the study effect in terms of
random effects and moderators not only allows to examine the impact of the
moderators, but often leads to more accurate estimates of the involved
parameters. Nevertheless, due to the often small number of studies on a
specific research topic, interactions are often neglected in meta-regression.
In this work, we consider the research questions (i) how moderator interactions
influence inference in mixed-effects meta-regression models and (ii) whether
some inference methods are more reliable than others. Here, we review robust
methods for confidence intervals in meta-regression models including
interaction effects. These methods are based on the application of robust
sandwich estimators for estimating the variance-covariance matrix of the vector
of model coefficients. Furthermore, we compare different versions of these
robust estimators in an extensive simulation study. We thereby investigate
coverage and length of seven different confidence intervals under varying
conditions. We conclude with some practical recommendations.
