Speaker
Description
Meta-analyses synthesise the results of multiple independent studies to obtain more comprehensive knowledge about a research topic. When study outcomes vary, meta-regression can be used to identify potential sources of heterogeneity across studies. One complication is the typically small number of studies available. Due to this, interaction terms are often omitted in meta-regression models, despite recommendations from previous research to consider them. In the meta-analysis on acute heart failure of Kimmoun et al. (2021) this caused possibly misleading or wrong results. This work aims to determine which variable selection method is able to identify moderator variables with an effect in a meta-regression, particularly in settings where interaction effects are suspected. The comparison includes commonly used methods, such as significance testing and information-theoretic criteria, as well as a tree-based algorithm called meta-CART introduced by Li et al. (2020). The latter machine learning approach promises a great potential in identifying interaction effects due to the underlying tree-structure. I conducted a simulation study varying the number of studies, the magnitude of heterogeneity, as well as the number and measurement scale of moderator variables to evaluate each method’s performance under realistic conditions. The methods were also compared to the results of the illustrative example by Kimmoun et al. (2021). The results demonstrate that, in comparison to meta-CART, conventional selection methods struggle with a high ratio of moderators to studies, which magnifies when interaction effects are included. Meta-CART is a robust method with a comparably low computational effort. Overall, the findings highlight the strong potential of tree-based methods for variable selection in the presence of interaction effects, while emphasising the continuing need for caution regarding spurious findings in meta-analytic research.
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