Abstract
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. The AE ontology contains important information about biological similarities between AEs. In this paper, we develop a model to estimate vaccine-AE associations while incorporating the AE ontology. We model a group of AEs using the zero-inflated negative binomial model and then estimate the vaccine-AE association using the empirical Bayes approach. This model handles the AE count data with excess zeros and allows borrowing information from related AEs. The proposed approach was evaluated by simulation studies and was further illustrated by an application to the Vaccine Adverse Event Reporting System (VAERS) dataset. The proposed method is implemented in an R package available at https://github.com/umich-biostatistics/zGPS.AO.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1512-1524 |
| Number of pages | 13 |
| Journal | Statistics in Medicine |
| Volume | 42 |
| Issue number | 10 |
| DOIs | |
| State | Published - May 10 2023 |
Funding
Research reported in this publication was supported by the National Institute Of Allergy And Infectious Diseases of the National Institutes of Health under Award Number R01AI158543. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Keywords
- adverse event ontology
- empirical Bayes
- vaccine adverse event
- VAERS
- zero-inflated negative binomial distribution
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability
