openEBGM: An R Implementation of the Gamma-Poisson Shrinker Data Mining Model

Authors

Travis Canida, U.S. Food and Drug Administration
John Ihrie, U.S. Food and Drug Administration

Date of this Version

12-2017

Document Type

Article

Citation

The R Journal (December 2017) 9(2); Editor: Roger Bivand

Comments

Copyright 2017, The R Foundation. Open access material. License: CC BY 4.0

Abstract

We introduce the R package openEBGM, an implementation of the Gamma-Poisson Shrinker (GPS) model for identifying unexpected counts in large contingency tables using an empirical Bayes approach. The Empirical Bayes Geometric Mean (EBGM) and quantile scores are obtained from the GPS model estimates. openEBGM provides for the evaluation of counts using a number of different methods, including the model-based disproportionality scores, the relative reporting ratio (RR), and the proportional reporting ratio (PRR). Data squashing for computational efficiency and stratification for confounding variable adjustment are included. Application to adverse event detection is discussed.