EBGM Disproportionality Scores for Adverse Event Data Mining
Print an openEBGM object
Process raw data
Calculate Qn
Find quantiles of the posterior distribution
Squash data for hyperparameter estimation
Semi-automated hyperparameter estimation
Automated data squashing
Calculate EBGM scores
Construct an openEBGM object
Explore various hyperparameter estimates
Estimate hyperparameters using an EM algorithm
Likelihood without zero counts
Likelihood with data squashing and no zero counts
Likelihood with zero counts
Likelihood with data squashing & zero counts
openEBGM: EBGM Disproportionality Scores for Adverse Event Data Mining
Plot an openEBGM object
Summarize an openEBGM object
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.