Detecting interaction effects (IEs) in meta-regression is challenging, especially when few studies are available and many plausible interactions are considered. In many meta-analyses, interpretability is essential, which limits the use of complex machine learning methods. Tree-based approaches offer a potentially useful compromise, but their role in meta-regression with random effects is not yet well understood. This paper examines how traditional linear and tree-based methods can support variable selection for IEs in random effects meta-regression. We compare test-based and information-criterion-based linear selection procedures with meta-CART approaches. These include fixed effect and random effects trees and their stability-selected ensemble variants. All methods are evaluated using a real-world meta-analytic dataset and a plasmode simulation study. The data-generating process assumes linear IEs and is complemented by settings with nonlinear interactions. Our results show that under strictly linear interactions, linear selection methods perform as expected and achieve superior performance for IE detection. Tree-based methods are more conservative when the number of studies is small, but become competitive as sample size increases, particularly the stability-selected variants. When IEs deviate from strict linearity, even in simple ways, the performance of linear methods deteriorates, whereas tree-based approaches, especially stability-selected fixed effect trees, provide a more robust alternative. Overall, stability-selected random effects trees are useful complementary tools for IE detection in applied meta-regression, particularly for metric covariates. They are well suited for pre-selection and sensitivity analyses, and selection frequency patterns in tree ensembles can help reveal structural patterns in the data.

翻译：在元回归中检测交互效应具有挑战性，尤其是在研究数量有限且需考虑多种可能交互作用的情况下。许多元分析中，可解释性至关重要，这限制了复杂机器学习方法的应用。基于树的方法提供了一个潜在有用的折中方案，但它们在包含随机效应的元回归中的作用尚未得到充分理解。本文探讨了传统的线性方法与基于树的方法如何支持随机效应元回归中交互效应的变量选择。我们将基于检验和基于信息准则的线性选择程序与元CART方法进行比较。这些方法包括固定效应树、随机效应树及其稳定性选择集成变体。所有方法均通过一个真实世界的元分析数据集和一项质体模拟研究进行评估。数据生成过程假设线性交互效应，并辅以非线性交互作用的设定。我们的结果表明，在严格线性交互作用下，线性选择方法表现符合预期，并在交互效应检测方面取得优异性能。当研究数量较少时，基于树的方法更为保守，但随着样本量增加，其表现变得具有竞争力，尤其是稳定性选择变体。当交互效应偏离严格线性时（即使偏离方式简单），线性方法的性能会下降，而基于树的方法（特别是稳定性选择的固定效应树）则提供了更稳健的替代方案。总体而言，稳定性选择的随机效应树是应用元回归中交互效应检测的有用补充工具，尤其适用于度量协变量。它们非常适合于预筛选和敏感性分析，且树集成中的选择频率模式有助于揭示数据的结构模式。