Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, Minnesota, USA.
Earl E. Bakken Center for Spirituality & Healing, University of Minnesota, Minneapolis, Minnesota, USA.
Res Synth Methods. 2024 Jan;15(1):61-72. doi: 10.1002/jrsm.1671. Epub 2023 Sep 11.
Meta-analysis is commonly used to combine results from multiple clinical trials, but traditional meta-analysis methods do not refer explicitly to a population of individuals to whom the results apply and it is not clear how to use their results to assess a treatment's effect for a population of interest. We describe recently-introduced causally interpretable meta-analysis methods and apply their treatment effect estimators to two individual-participant data sets. These estimators transport estimated treatment effects from studies in the meta-analysis to a specified target population using the individuals' potentially effect-modifying covariates. We consider different regression and weighting methods within this approach and compare the results to traditional aggregated-data meta-analysis methods. In our applications, certain versions of the causally interpretable methods performed somewhat better than the traditional methods, but the latter generally did well. The causally interpretable methods offer the most promise when covariates modify treatment effects and our results suggest that traditional methods work well when there is little effect heterogeneity. The causally interpretable approach gives meta-analysis an appealing theoretical framework by relating an estimator directly to a specific population and lays a solid foundation for future developments.
元分析常用于合并多个临床试验的结果，但传统的元分析方法并没有明确提及结果适用的个体人群，也不清楚如何使用它们的结果来评估特定人群的治疗效果。我们描述了最近引入的因果可解释元分析方法，并将其治疗效果估计量应用于两个个体参与者数据集。这些估计量使用个体的潜在效应修饰协变量，将元分析中研究的估计治疗效果传输到指定的目标人群。我们在该方法内考虑了不同的回归和加权方法，并将结果与传统的汇总数据元分析方法进行了比较。在我们的应用中，因果可解释方法的某些版本的表现略优于传统方法，但后者通常表现良好。当协变量改变治疗效果时，因果可解释方法最有前途，而当治疗效果异质性较小时，传统方法通常效果良好。因果可解释方法通过将估计量直接与特定人群联系起来，为未来的发展奠定了坚实的基础，为元分析提供了一个有吸引力的理论框架。
