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.

翻译：元分析是一种重要的统计技术，用于综合针对相同或密切相关研究问题的多项研究结果。所谓的元回归通过考虑研究层面的协变量，扩展了元分析模型。混合效应元回归模型通过恰当处理研究间异质性，为证据合成提供了有力工具。事实上，将研究效应建模为随机效应和调节变量，不仅可以检验调节变量的影响，而且常常能更准确地估计相关参数。然而，由于特定研究主题所涉及的研究数量通常较少，元回归中往往忽略交互效应。本研究考虑以下研究问题：（i）调节变量交互如何影响混合效应元回归模型中的推断；（ii）某些推断方法是否比其他方法更可靠。本文综述了包含交互效应的元回归模型中置信区间的稳健方法。这些方法基于稳健三明治估计量来估计模型系数向量的方差-协方差矩阵。此外，我们通过一项广泛的模拟研究比较了这些稳健估计量的不同版本，考察了七种不同置信区间在不同条件下的覆盖率和长度，并最终提出了一些实用建议。