Vaccine adverse event enrichment tests

Abstract

Background: : 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.Results: 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. Conclusion: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

Mining adverse events in large frequency tables with ontology, with an application to the vaccine adverse event reporting system

“…One recent study performed AE enrichment analysis using the AE ontology in MedDRA. 19 Their method focuses on identifying enriched AE groups where AEs are more likely to be disproportionately reported than AEs in other groups. However, this enrichment analysis is done after RRs of individual AEs have been estimated.…”

“…MedDRA is a dictionary containing thousands of PT terms for various symptoms and diseases. Therefore, a large number of AEs in VAERS were never mentioned for many vaccines; For example, in VAERS data from 2002 to 2018, approximately 40% AEs were never mentioned with the “FLU4” vaccine, resulting in 40% AEs with a zero count 19 . In this paper, we propose a model allowing information sharing between AEs within the same group while accommodating zero counts.…”

Likelihood Ratio Test-Based Drug Safety Assessment using R Package \pkg{pvLRT}

“…There currently exist a few R packages in CRAN with functionalities relevant for medical product safety. This includes the packages PhViD (Ahmed and Poncet 2016), openEBGM (Canida and Ihrie 2017), AEenrich (Li et al 2021), Sequential (Silva and Kulldorff 2021), sglr (Narasimhan and Shih 2012), and mds (Chung 2020). Among these PhViD and openEBGM provide functionalities for spontaneous adverse event data-driven pharmacovigilance: PhViD implements methods such as PRR, ROR, and BCPNN, and openEBGM implements the method of MGPS.…”

On the use of the likelihood ratio test methodology in pharmacovigilance

“…Additional articles include Maansson et al 16 that compares zero-inflated models with hurdle models with the aim to evaluate their ability to predict adverse events and concludes that zero-inflated models provide a better fit to adverse event data from healthy populations compared to the conventional parametric models. Finally, in the context of identification of potential adverse events associated with vaccines Li and Zhao 17 extend a recent approach used in bioinformatics, the Gene Set Enrichment Analysis (GSEA) method 18,19 to handle adverse event identification in vaccine data obtained from the Vaccine Adverse Event Reporting System. These authors however focus only on the Poisson model.…”