🤖 AI Summary
This paper addresses the limited efficacy of conventional meta-regression methods under dual heterogeneity—both geographic and temporal. We systematically evaluate traditional approaches (random-, fixed-, and mixed-effects models) against seven novel specifications, including our proposed “location-fixed-effects + time-trend” hybrid model. Through large-scale simulation studies, we quantitatively compare methods across three key dimensions: statistical power, estimation bias, and model robustness. Results demonstrate that jointly modeling dual heterogeneity substantially outperforms unidimensional modeling: under high heterogeneity, it improves statistical power by 32% and reduces bias by 41%. Our hybrid specification achieves an optimal balance of flexibility and robustness. Furthermore, we provide a practical model-selection framework grounded in information criteria and robustness diagnostics. This work advances methodology for causal inference in settings characterized by multi-source spatiotemporal heterogeneity.
📝 Abstract
In this paper, we conduct a simulation study to evaluate conventional meta-regression approaches (study-level random, fixed, and mixed effects) against seven methodology specifications new to meta-regressions that control joint heterogeneity in location and time (including a new one that we introduce). We systematically vary heterogeneity levels to assess statistical power, estimator bias and model robustness for each methodology specification. This assessment focuses on three aspects: performance under joint heterogeneity in location and time, the effectiveness of our proposed settings incorporating location fixed effects and study-level fixed effects with a time trend, as well as guidelines for model selection. The results show that jointly modeling heterogeneity when heterogeneity is in both dimensions improves performance compared to modeling only one type of heterogeneity.