Bayesian learning of Covid-19 vaccine safety while incorporating adverse events ontology

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, none of these methods considered 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 graph-assisted signal selection (BGrass) model to simultaneously estimate all AEs while incorporating the network of dependence between AEs. Under a fully Bayesian inference framework, we also propose a negative control approach to mitigate the reporting bias and an enrichment approach to detecting AE groups of concern. For posterior computation we construct an equivalent model representation and develop an efficient Gibbs sampler. We evaluate the performance of BGrass via extensive simulations. To study the safety of COVID-19 vaccines, we apply BGrass to analyze approximately one million VAERS reports (01/01/2016–12/24/2021) involving more than 800 AEs. In particular, we found that blood clots (including deep vein thrombosis, thrombosis, and pulmonary embolism) are more likely to be reported after COVID-19 vaccination, compared to influenza vaccines. They are also reported more often for Johnson & Johnson–Janssen vaccine, compared to mRNA-based COVID-19 vaccines. A user-friendly $\mathtt{R}$ package $\mathtt{BGrass}$ that implements the proposed methods to assess vaccine safety is included in the Supplementary Material and is publicly available at https://github.com/BangyaoZhao/BGrass.