Department of Mathematics and Statistics, University of Houston-Downtown, Houston, Texas, USA.
Joint Program in Survey Methodology, University of Maryland, College Park, Maryland, USA.
Stat Med. 2022 Jul 20;41(16):3131-3148. doi: 10.1002/sim.9408. Epub 2022 May 18.
To strengthen inferences meta-analyses are commonly used to summarize information from a set of independent studies. In some cases, though, the data may not satisfy the assumptions underlying the meta-analysis. Using three Bayesian methods that have a more general structure than the common meta-analytic ones, we can show the extent and nature of the pooling that is justified statistically. In this article, we reanalyze data from several reviews whose objective is to make inference about the COVID-19 asymptomatic infection rate. When it is unlikely that all of the true effect sizes come from a single source researchers should be cautious about pooling the data from all of the studies. Our findings and methodology are applicable to other COVID-19 outcome variables, and more generally.
为了加强推论，荟萃分析通常用于总结一组独立研究的信息。然而，在某些情况下，数据可能不符合荟萃分析的基本假设。使用三种比常见的荟萃分析方法结构更广泛的贝叶斯方法，我们可以展示在统计学上有理由进行的汇总的程度和性质。在本文中，我们重新分析了几个旨在对 COVID-19 无症状感染率进行推论的综述的数据。当不太可能所有真实的效应大小都来自单一来源时，研究人员应该谨慎地将所有研究的数据进行汇总。我们的发现和方法适用于其他 COVID-19 结果变量，更普遍地适用于其他情况。
