EBGM Disproportionality Scores for Adverse Event Data Mining
Description
An implementation of DuMouchel's (1999)
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.