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Vaccine safety is a concerning problem of the public, and many signaldetecting methods have been developed to identify relative risks betweenvaccines and adverse events (AEs). Those methods usually focus on individualAEs, where the randomness of data is high. The results often turn out to beinaccurate and lack of clinical meaning. The AE ontology contains informationabout biological similarity of AEs. Based on this, we extend the concept ofrelative risks (RRs) to AE group level, which allows the possibility of moreaccurate and meaningful estimation by utilizing data from the whole group. Inthis paper, we propose the method zGPS.AO (Zero Inflated Gamma Poisson Shrinkerwith AE ontology) based on the zero inflated negative binomial distribution.This model has two purples: a regression model estimating group level RRs, anda empirical bayes framework to evaluate AE level RRs. The regression part canhandle both excess zeros and over dispersion in the data, and the empiricalmethod borrows information from both group level and AE level to reduce datanoise and stabilize the AE level result. We have demonstrate the unbiasenessand low variance features of our model with simulated data, and obtainedmeaningful results coherent with previous studies on the VAERS (Vaccine AdverseEvent Reporting System) database. The proposed methods are implemented in the Rpackage zGPS.AO, which can be installed from the Comprehensive R ArchiveNetwork, CRAN. The results on VAERS data are visualized using the interactiveweb app Rshiny.

Data Mining in Large Frequency Tables With Ontology, with an Application to the Vaccine Adverse Event Reporting System