Adverse event enrichment tests using VAERS

TL;DR

This paper introduces novel statistical enrichment tests, AEFisher and AEKS, tailored for VAERS adverse event data, addressing data peculiarities like zeros and ties, to improve safety signal interpretation.

Contribution

It extends gene enrichment analysis methods to adverse event data, proposing two new tests that handle count data features for better safety signal detection.

Findings

AE enrichment tests outperform existing methods in simulations

The methods effectively identify meaningful AE groups in VAERS data

The R package AEenrich facilitates practical application of these tests

Abstract

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.