论文标题
贝叶斯对无症状的Covid-19感染率的推断
Bayesian inference for asymptomatic COVID-19 infection rates
论文作者
论文摘要
为了加强推断，通常用于总结一系列独立研究的信息。但是，在某些情况下，数据可能无法满足元分析的基础假设。使用三种比常见元分析方法更通用结构的贝叶斯方法，我们可以显示合理的合并的程度和性质。在本文中，我们重新分析了几种评论的数据，这些评论的目的是推断Covid-19的无症状感染率。如果不太可能，所有真正的效应大小都来自单个来源的研究人员应该谨慎地汇集所有研究的数据。我们的发现和方法论适用于其他COVID-19结果变量，更普遍。
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 paper, we re-analyze 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.
