.NBmix_EM: Fit gamma mixture based empirical Bayes models using ECM algorithm.

Description

Fit gamma mixture based empirical Bayes models using ECM algorithm.

Usage

.NBmix_EM(
  N,
  E,
  dirichlet = TRUE,
  alpha = NULL,
  K = NULL,
  maxi = NULL,
  h = NULL,
  eps = 1e-04
)

Value

a list of optimizer outputs

Arguments

N: an IxJ contingency table showing pairwise counts of adverse events for I AEs (along the rows) and J drugs (along the columns).
E: A matrix of expected counts under the null model for the SRS frequency table.
dirichlet: logical. Used for "general-gamma" model. If is TRUE, a dirichlet hyperprior for weights of gamma mixture prior is applied.
alpha: numeric between 0 and 1. The hyperparameter of "general-gamma" model. It is needed if "general-gamma" model is used.
K: integer greater than or equal to 2. It is needed if "K-gamma" model is used.
maxi: upper limit of iteration for the ECM algorithm.
h: a vector of initialization of parameter h.
eps: a tolerance parameter for ECM algorithm.

Details

This function implements the ECM algorithm proposed by Tan et al. (2025), providing a stable and efficient implementation of Gamma-Poisson Shrinker(GPS), K-gamma and "general-gamma" methods for signal estimation and signal detection in Spontaneous Reporting System (SRS) data table.

References

Tan Y, Markatou M and Chakraborty S. Flexible Empirical Bayesian Approaches to Pharmacovigilance for Simultaneous Signal Detection and Signal Strength Estimation in Spontaneous Reporting Systems Data. Statistics in Medicine. 2025; 44: 18-19, https://doi.org/10.1002/sim.70195.

DuMouchel W. Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system. The American Statistician. 1999; 1;53(3):177-90.