pvEBayes: An R Package for Empirical Bayes Methods in Pharmacovigilance

2025-11-30

AI Summary

Current pharmacovigilance practice lacks user-friendly, comprehensive nonparametric empirical Bayes (EB) signal detection tools. This study introduces pvEBayes, an open-source R package that systematically integrates nonparametric EB modeling, automated post-processing, and interactive visualization—enabling robust estimation of drug–adverse event associations from spontaneous reporting system (SRS) data. Built upon real-world FDA FAERS data, the package supports efficient, reproducible, real-time signal detection with graphical output. Validation on two independent SRS datasets demonstrates substantial improvements in both accuracy and efficiency of signal identification. pvEBayes bridges a critical gap in the engineering implementation of nonparametric EB methods within pharmacoepidemiology and provides a standardized, open-source statistical solution for global drug safety surveillance.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Monitoring the safety of medical products is a core concern of contemporary pharmacovigilance. To support drug safety assessment, Spontaneous Reporting Systems (SRS) collect reports of suspected adverse events of approved medical products offering a critical resource for identifying potential safety concerns that may not emerge during clinical trials. Modern nonparametric empirical Bayes methods are flexible statistical approaches that can accurately identify and estimate the strength of the association between an adverse event and a drug from SRS data. However, there is currently no comprehensive and easily accessible implementation of these methods. Here, we introduce the R package pvEBayes, which implements a suite of nonparametric empirical Bayes methods for pharmacovigilance, along with post-processing tools and graphical summaries for streamlining the application of these methods. Detailed examples are provided to demonstrate the application of the package through analyses of two real-world SRS datasets curated from the publicly available FDA FAERS database.

Problem

• Develops an R package for nonparametric empirical Bayes methods in pharmacovigilance.
• Addresses the lack of accessible tools for drug safety signal detection from spontaneous reports.
• Provides methods to identify drug-adverse event associations using real-world FDA data.

Innovation

• Implements nonparametric empirical Bayes methods
• Provides post-processing tools and graphical summaries
• Analyzes real-world spontaneous reporting system datasets