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.

翻译：监测医疗产品的安全性是当代药物警戒的核心关切。为支持药物安全评估，自发报告系统（SRS）收集已批准医疗产品的疑似不良事件报告，为识别临床试验中可能未显现的潜在安全问题提供了关键资源。现代非参数经验贝叶斯方法是一种灵活的统计方法，能够从SRS数据中准确识别并估计不良事件与药物之间关联的强度。然而，目前尚无全面且易于获取的这些方法实现。本文介绍R软件包pvEBayes，该包实现了一套用于药物警戒的非参数经验贝叶斯方法，并包含后处理工具和图形摘要，以简化这些方法的应用。通过分析从公开可用的FDA FAERS数据库中整理的两个真实世界SRS数据集，提供了详细示例以演示该软件包的应用。