School of Mathematical and Physical Sciences, Macquarie University, Sydney, Australia.
Section of Biostatistics, Department of Public Health, University of Copenhagen, Denmark.
Stat Methods Med Res. 2023 May;32(5):994-1009. doi: 10.1177/09622802231163330. Epub 2023 Mar 16.
Subgroup meta-analysis can be used for comparing treatment effects between subgroups using information from multiple trials. If the effect of treatment is differential depending on subgroup, the results could enable personalization of the treatment. We propose using linear mixed models for estimating treatment effect modification in aggregate data meta-analysis. The linear mixed models capture existing subgroup meta-analysis methods while allowing for additional features such as flexibility in modeling heterogeneity, handling studies with missing subgroups and more. Reviews and simulation studies of the best suited models for estimating possible differential effect of treatment depending on subgroups have been studied mostly within individual participant data meta-analysis. While individual participant data meta-analysis in general is recommended over aggregate data meta-analysis, conducting an aggregate data subgroup meta-analysis could be valuable for exploring treatment effect modifiers before committing to an individual participant data subgroup meta-analysis. Additionally, using solely individual participant data for subgroup meta-analysis requires collecting sufficient individual participant data which may not always be possible. In this article, we compared existing methods with linear mixed models for aggregate data subgroup meta-analysis under a broad selection of scenarios using simulation and two case studies. Both the case studies and simulation studies presented here demonstrate the advantages of the linear mixed model approach in aggregate data subgroup meta-analysis.
亚组荟萃分析可用于使用来自多个试验的信息来比较亚组之间的治疗效果。如果治疗效果因亚组而异，则结果可以实现治疗的个性化。我们建议在汇总数据荟萃分析中使用线性混合模型来估计治疗效果的改变。线性混合模型可以捕捉现有的亚组荟萃分析方法，同时允许具有更多功能，例如在建模异质性、处理缺少亚组的研究以及更多方面的灵活性。针对估计治疗效果可能因亚组而异的最佳模型的综述和模拟研究主要在个体参与者数据荟萃分析中进行。虽然一般建议使用个体参与者数据荟萃分析而不是汇总数据荟萃分析，但在承诺进行个体参与者数据亚组荟萃分析之前，进行汇总数据亚组荟萃分析可能对于探索治疗效果调节剂非常有价值。此外，仅使用个体参与者数据进行亚组荟萃分析需要收集足够的个体参与者数据，而这在某些情况下可能无法实现。在本文中，我们使用模拟和两个案例研究在广泛的场景下比较了现有的汇总数据亚组荟萃分析方法和线性混合模型。本文提出的案例研究和模拟研究均表明线性混合模型方法在汇总数据亚组荟萃分析中的优势。
