ArticleStatistics in medicine2023
Mining adverse events in large frequency tables with ontology, with an application to the vaccine adverse event reporting system.
Article in Statistics in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions.Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.