R package `pvEBayes`

pvEBayes is an R package that implements a suite of nonparametric empirical Bayes methods for pharmacovigilance, including Gamma-Poisson Shrinker (GPS), K-gamma, general-gamma, Koenker-Mizera (KM), and Efron models. It provides tools for fitting these models to the spontaneous reporting system (SRS) frequency tables, extracting summaries, performing hyperparameter tuning, and generating graphical summaries (eye plots and heatmaps) for signal detection and estimation.

Spontaneous Reporting System (SRS) Table: An drug safety SRS dataset catalogs AE reports on I AE rows across J drug columns. Let \({N_{ij}}\) denote the number of reported cases for the i-th AE and the j-th drug, where \({i = 1,..., I}\) and \({j = 1,..., J}\).

Empirical Bayes modeling for disproportionality analysis: We assume that for each AE-drug pair, \(N_{ij} \sim \text{Poisson}(\lambda_{ij} E_{ij})\), where \({E_{ij}}\) is the expected baseline value, measuring the expected count of the AE-drug pair when there is no association betweeni-th AE and j-th drug. The parameter \({\lambda_{ij} \geq 0}\) represents the relative reporting ratio, the signal strength, for the \({(i, j)}\)-th pair, measuring the ratio of the actual expected count arising due to dependence on the null baseline expected count. Current disproportionality analysis mainly focuses on signal detection which seeks to determine whether the observation \(N_{ij}\) is substantially greater than the corresponding null baseline \(E_{ij}\). Under the Poisson model, that is to say, its signal strength \(\lambda_{ij}\) is significantly greater than 1.

In addition to signal detection, Tan et al. (Stat. in Med., 2025) broaden the role of disproportionality to signal estimation. The use of the flexible non-parametric empirical Bayes models enables more nuanced empirical Bayes posterior inference (parameter estimation and uncertainty quantification) on signal strength parameter \(\{ \lambda_{ij} \}\). This allows researchers to distinguish AE-drug pairs that would appear similar under a binary signal detection framework. For example, the AE-drug pairs with signal strengths of 1.5 and 4.0 could both be significantly greater than 1 and detected as a signal. Such differences in signal strength may have distinct implications in medical and clinical contexts.

The methods included in pvEBayes differ by their assumptions on the prior distribution. Implemented methods include the Gamma-Poisson Shrinker (GPS), Koenker-Mizera (KM) method, Efron’s nonparametric empirical Bayes approach, the K-gamma model, and the general-gamma model. The selection of the prior distribution is critical in Bayesian analysis. The GPS model uses a gamma mixture prior by assuming the signal/non-signal structure in SRS data. However, in real-world setting, signal strengths \({(\lambda_{ij})}\) are often heterogeneous and thus follows a multi-modal distribution, making it difficult to assume a parametric prior. Non-parametric empirical Bayes models (KM, Efron, K-gamma and general-gamma) address this challenge by utilizing a flexible prior with a general mixture form and estimating the prior distribution in a data-driven way.

Implementations: The KM method has an existing implementation in the REBayes package, but it relies on Mosek, a commercial convex optimization solver, which may limit accessibility due to licensing issues. The pvEBayes package provides an alternative fully open-source implementation of the KM method using CVXR. Efron’s method also has a general nonparametric empirical Bayes implementation in the deconvolveR package; however, that implementation does not support an exposure or offset parameter in the Poisson model, which corresponds to the expected null value \({E_{ij}}\). In pvEBayes, the implementation of Efron’s method is adapted and modified from deconvolveR to support \({E_{ij}}\) in the Poisson model.

In addition, this package implements the novel bi-level Expectation Conditional Maximization (ECM) algorithm proposed by Tan et al. (2025) for efficient parameter estimation in gamma mixture prior based models mentioned above.

Installation

The stable version of pvEBayes can be installed from CRAN:

install.packages("pvEBayes")

The development version is available from GitHub:

# if (!requireNamespace("devtools")) install.packages("devtools")
devtools::install_github("YihaoTancn/pvEBayes")

Quick Example

Here is a minimal example analyzing the built-in FDA statin44 dataset with general-gamma model:


library(pvEBayes)

# Load the statin44 contingency table of 44 AEs for 6 statins
data("statin2025_44")

# Fit a general-gamma model with a specified alpha
fit <- pvEBayes(
  contin_table      = statin2025_44,
  model             = "general-gamma",
  alpha             = 0.3,
  n_posterior_draws = 1000
)

# Print out a concise description of the fitted model
fit

# Obtain a logical matrix for the detected signal
summary(fit, return = "detected signal")

# Visualize posterior distributions for top AE-drug pairs
plot(fit, type = "eyeplot")

For a more detailed illustration, please see ‘Vignette’.

License

pvEBayes is released under the GPL-3 license. See ‘LICENSE.md’ for details.

Code of Conduct

Please note that the pvEBayes project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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.

Tan Y, Markatou M and Chakraborty S. pvEBayes: An R Package for Empirical Bayes Methods in Pharmacovigilance. arXiv:2512.01057 (stat.AP). https://doi.org/10.48550/arXiv.2512.01057

Koenker R, Mizera I. Convex Optimization, Shape Constraints, Compound Decisions, and Empirical Bayes Rules. Journal of the American Statistical Association 2014; 109(506): 674–685, https://doi.org/10.1080/01621459.2013.869224

Efron B. Empirical Bayes Deconvolution Estimates. Biometrika 2016; 103(1); 1-20, https://doi.org/10.1093/biomet/asv068