EBGM Disproportionality Scores for Adverse Event Data Mining
An implementation of DuMouchel's (1999)
Bayesian data mining method for the
market basket problem. Calculates Empirical Bayes Geometric Mean (EBGM) and
quantile scores from the posterior distribution 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. Now includes an
implementation of the EM algorithm for hyperparameter estimation loosely
derived from the 'mederrRank' package.