Disentangling effect size heterogeneity in meta-analysis: A latent mixture approach.
Disentangling effect size heterogeneity in meta-analysis: A latent mixture approach.
复制标题
荟萃分析中解开效应大小异质性：一种潜在的混合方法。
DOI：
10.1037/met0000368
复制
发表时间：
2022
影响因子：
7
通讯作者：
Xu, Heng
中科院分区：
文献类型：
--
作者：
Zhang, Nan;Wang, Mo;Xu, Heng
荟萃分析的一项重要任务是观察、量化和解释主要研究报告效应大小的异质性。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物的方法，用于在meta分析中解开效应大小的异质性。数学分析和模拟研究表明，当观察到的异质性来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高得多的统计能力，而不需要研究者在分析观察到的异质性时判断需要考虑哪些因素或纠正哪些因素。我们还进行了一个真实世界数据的案例研究，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。（PsycInfo数据库记录(c) 2022 APA，版权所有）
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) 2022 APA, all rights reserved)