  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 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. Unlike the continuous gene expression
data, AE data are counts. Therefore, AE data has many zeros and ties. We
propose two enrichment tests, AEFisher and AEKS. AEFisher is a modified
Fisher's exact test based on pre-selected significant AEs, while AEKS is based
on a modified Kolmogorov-Smirnov statistic. Both tests incorporate 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. By
appropriately addressing the issues of ties and excessive zeros in AE count
data, our enrichment tests performed well as demonstrated by simulation studies
and analyses of VAERS data. The proposed methods were implemented in R package
AEenrich and can be installed from the Comprehensive R Archive Network, CRAN.
