Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, the Netherlands.
Department of Preventive Medicine and Epidemiology, School of Medicine, Boston University, Boston, USA.
Stat Methods Med Res. 2024 Mar;33(3):359-375. doi: 10.1177/09622802231206474. Epub 2024 Mar 9.
Simulation studies are commonly used to evaluate the performance of newly developed meta-analysis methods. For methodology that is developed for an aggregated data meta-analysis, researchers often resort to simulation of the aggregated data directly, instead of simulating individual participant data from which the aggregated data would be calculated in reality. Clearly, distributional characteristics of the aggregated data statistics may be derived from distributional assumptions of the underlying individual data, but they are often not made explicit in publications. This article provides the distribution of the aggregated data statistics that were derived from a heteroscedastic mixed effects model for continuous individual data and a procedure for directly simulating the aggregated data statistics. We also compare our simulation approach with other simulation approaches used in literature. We describe their theoretical differences and conduct a simulation study for three meta-analysis methods: DerSimonian and Laird method for pooling aggregated study effect sizes and the Trim & Fill and precision-effect test and precision-effect estimate with standard errors method for adjustment of publication bias. We demonstrate that the choice of simulation model for aggregated data may have an impact on (the conclusions of) the performance of the meta-analysis method. We recommend the use of multiple aggregated data simulation models to investigate the sensitivity in the performance of the meta-analysis method. Additionally, we recommend that researchers try to make the individual participant data model explicit and derive from this model the distributional consequences of the aggregated statistics to help select appropriate aggregated data simulation models.
模拟研究通常用于评估新开发的荟萃分析方法的性能。对于针对汇总数据荟萃分析开发的方法，研究人员通常倾向于直接模拟汇总数据，而不是从实际计算汇总数据的个体参与者数据中进行模拟。显然，汇总数据统计量的分布特征可能源自基础个体数据的分布假设，但这些假设在出版物中通常没有明确说明。本文提供了从异方差混合效应模型得出的连续个体数据的汇总数据统计量的分布，并提供了直接模拟汇总数据统计量的过程。我们还将我们的模拟方法与文献中使用的其他模拟方法进行了比较。我们描述了它们在理论上的差异，并针对三种荟萃分析方法进行了模拟研究：用于汇总聚合研究效果大小的 DerSimonian 和 Laird 方法，以及用于调整发表偏倚的 Trim & Fill 和精度效应检验和精度效应估计与标准误差方法。我们证明了选择汇总数据的模拟模型可能会对荟萃分析方法的性能（结论）产生影响。我们建议使用多个汇总数据模拟模型来研究荟萃分析方法性能的敏感性。此外，我们建议研究人员尝试明确个体参与者数据模型，并从该模型中推导出汇总统计数据的分布后果，以帮助选择合适的汇总数据模拟模型。
