Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA.
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Stat Med. 2021 Aug 30;40(19):4269-4278. doi: 10.1002/sim.9027. Epub 2021 May 9.
Vaccination safety is critical for individual and public health. Many existing methods have been used to conduct safety studies with the VAERS (Vaccine Adverse Event Reporting System) database. However, these methods frequently identify many adverse event (AE) signals and they are often hard to interpret in a biological context. The AE ontology introduces biologically meaningful structures to the Vaccine Adverse Event Reporting System (VAERS) database by connecting similar AEs, which provides meaningful interpretation for the underlying safety issues. In this paper, we develop rigorous statistical methods to identify "interesting" AE groups by performing AE enrichment analysis. We extend existing gene enrichment tests to perform AE enrichment analysis, while incorporating the special features of the AE data. The proposed methods were evaluated using simulation studies and were further illustrated on two studies using VAERS data. The proposed methods were implemented in R package AEenrich and can be installed from the Comprehensive R Archive Network, CRAN, and source code are available at https://github.com/umich-biostatistics/AEenrich.
疫苗接种安全对于个人和公共健康至关重要。许多现有的方法已经被用于使用 VAERS（疫苗不良事件报告系统）数据库进行安全性研究。然而，这些方法经常会识别出许多不良事件（AE）信号，并且在生物学背景下往往难以解释。AE 本体论通过连接相似的 AE，为疫苗不良事件报告系统（VAERS）数据库引入了具有生物学意义的结构，为潜在的安全问题提供了有意义的解释。在本文中，我们通过进行 AE 富集分析，开发了严格的统计方法来识别“有趣”的 AE 组。我们扩展了现有的基因富集测试来进行 AE 富集分析，同时结合了 AE 数据的特殊特征。所提出的方法通过模拟研究进行了评估，并进一步在使用 VAERS 数据的两项研究中进行了说明。所提出的方法已在 R 包 AEenrich 中实现，可从 Comprehensive R Archive Network，CRAN 安装，源代码可在 https://github.com/umich-biostatistics/AEenrich 获得。
