An important task of meta-analysis is to observe, quantify, and explain the heterogeneity across the reported effect sizes of primary studies. A primary issue that challenges this task is the myriad of subtle factors that could have contributed to the observed heterogeneity. We leveraged the recent advances in theoretical machine learning to develop a novel latent mixture-based method for disentangling effect-size heterogeneity in meta-analysis. Mathematical analysis and simulation studies were carried out to demonstrate that, when the observed heterogeneity stems from more than 1 factor, our method can attain a substantially higher statistical power than the traditional methods for moderator analysis without requiring researchers to make judgment calls on which factors to consider or correct for in analyzing the observed heterogeneity. We also conducted a case study with real-world data to show how our method may be used to address long-standing inconsistencies in the literature. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

中文翻译：

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在荟萃分析中解开效应大小异质性：一种潜在的混合方法。

荟萃分析的一项重要任务是观察、量化和解释初级研究报告的效应大小的异质性。挑战这项任务的一个主要问题是可能导致观察到的异质性的无数微妙因素。我们利用理论机器学习的最新进展开发了一种新的基于潜在混合的方法，用于解开元分析中的效应大小异质性。数学分析和模拟研究表明，当观察到的异质性源于一个以上的因素时，我们的方法可以获得比传统的调节分析方法更高的统计功效，而无需研究人员对哪些因素做出判断。在分析观察到的异质性时考虑或纠正。我们还对真实世界的数据进行了案例研究，以展示我们的方法如何用于解决文献中长期存在的不一致问题。（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）