Bayesian learning of COVID-19 Vaccine safety while incorporating Adverse Events ontology

TL;DR

This paper introduces a Bayesian graphical model that incorporates adverse event ontology to improve COVID-19 vaccine safety analysis, effectively detecting true signals and reducing false positives in VAERS data.

Contribution

It develops a novel Bayesian model that explicitly integrates AE relationships, enhancing signal detection and bias mitigation in vaccine safety studies.

Findings

Effective in simulation studies

Identifies AE groups of concern

Reduces false positives

Abstract

While vaccines are crucial to end the COVID-19 pandemic, public confidence in vaccine safety has always been vulnerable. Many statistical methods have been applied to VAERS (Vaccine Adverse Event Reporting System) database to study the safety of COVID-19 vaccines. However, all these methods ignored the adverse event (AE) ontology. AEs are naturally related; for example, events of retching, dysphagia, and reflux are all related to an abnormal digestive system. Explicitly bringing AE relationships into the model can aid in the detection of true AE signals amid the noise while reducing false positives. We propose a Bayesian graphical model to estimate all AEs while incorporating the AE ontology simultaneously. We proposed strategies to construct conjugate forms leading to an efficient Gibbs sampler. Built upon the posterior distributions, we proposed a negative control approach to mitigate reporting bias and an enrichment approach to detect AE groups of concern. The proposed methods were evaluated using simulation studies and were further illustrated on studying the safety of COVID-19 vaccines. The proposed methods were implemented in R package \textit{BGrass} and source code are available at https://github.com/BangyaoZhao/BGrass.