The vaccine adverse event reporting system (VAERS) is a vital resource for
post-licensure vaccine safety monitoring and has played a key role in assessing
the safety of COVID-19 vaccines. However it is difficult to properly identify
rare adverse events (AEs) associated with vaccines due to small or zero counts.
We propose a Bayesian model with a Dirichlet Process Mixture prior to improve
accuracy of the AE estimates with small counts by allowing data-guided
information sharing between AE estimates. We also propose a negative control
procedure embedded in our Bayesian model to mitigate the reporting bias due to
the heightened awareness of COVID-19 vaccines, and use it to identify
associated AEs as well as associated AE groups defined by the organ system in
the Medical Dictionary for Regulatory Activities (MedDRA) ontology. The
proposed model is evaluated using simulation studies, in which it outperforms
baseline models without information sharing and is applied to study the safety
of COVID-19 vaccines using VAERS data