| アブストラクト | 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. |
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| ジャーナル名 | Statistics in medicine |
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| 投稿日 | 2021/5/11 |
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| 投稿者 | Li, Shuoran; Zhao, Lili |
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| 組織名 | Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA.;Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/33969520/ |
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