openEBGM - EBGM Disproportionality Scores for Adverse Event Data Mining
An implementation of DuMouchel's (1999)
<doi:10.1080/00031305.1999.10474456> Bayesian data mining
method for the market basket problem. Calculates Empirical
Bayes Geometric Mean (EBGM) and posterior quantile scores using
the Gamma-Poisson Shrinker (GPS) model to find unusually large
cell counts in large, sparse contingency tables. Can be used to
find unusually high reporting rates of adverse events
associated with products. In general, can be used to mine any
database where the co-occurrence of two variables or items is
of interest. Also calculates relative and proportional
reporting ratios. Builds on the work of the 'PhViD' package,
from which much of the code is derived. Some of the added
features include stratification to adjust for confounding
variables and data squashing to improve computational
efficiency. Includes an implementation of the EM algorithm for
hyperparameter estimation loosely derived from the 'mederrRank'
package.