faers: A High-Fidelity Framework and R/Bioconductor Package for Precision Adverse Event Surveillance

BackgroundThe FDA Adverse Event Reporting System (FAERS) is a critical pillar of post-marketing pharmacovigilance; however, its utility is constrained by data heterogeneity, pervasive reporting redundancies, and inconsistent medical terminology. These structural barriers impede reproducible, large-scale analyses and the implementation of precision drug safety surveillance. MethodsWe developed faers, an open-source R package that delivers a standardized framework and an end-to-end workflow for transforming raw FAERS data into analysis-ready formats. The package implements a regulatory-compliant multi-level deduplication strategy, automated MedDRA terminology mapping, and an R S4-based object-oriented system to ensure data integrity, traceability, and efficient management of complex relational structures. It further integrates a full suite of disproportionality signal detection methods, including the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayes Geometric Mean (EBGM). Performance was benchmarked on large-scale FAERS datasets, and validity was confirmed by replicating published findings on anti-PD-1/PD-L1-associated cardiotoxicity and CAR-T cell therapy outcomes, with additional application to immune-related adverse events (irAEs). FindingsThe package demonstrated high computational efficiency and near-linear scalability when processing extensive quarterly FAERS data. Validation analyses of two case studies showed excellent concordance with prior literature. Application to an irAE cohort further identified a statistically significant age-by-sex interaction in risk patterns, demonstrating the tools ability to uncover nuanced demographic signals that are often missed by conventional approaches. InterpretationThe faers package provides a transparent, scalable, and fully reproducible framework for FAERS-based pharmacovigilance. By automating data cleaning, standardization, and advanced signal detection, it lowers technical barriers for researchers and regulators while promoting high-quality, open pharmacoepidemiological research to strengthen drug safety monitoring. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSExisting pharmacovigilance resources, ranging from R packages like PhViD and openEBGM to web-based platforms such as openFDA, have significantly advanced statistical signal detection. However, these tools primarily focus on specific algorithms and require pre-processed, high-quality input data, leaving a critical gap in end-to-end data management. No comprehensive, open-source framework currently integrates the entire workflow--from raw FAERS data ingestion and regulatory-compliant deduplication to advanced signal detection--within a single, reproducible environment. Consequently, large-scale studies often rely on fragmented, custom scripts that hinder scalability and research transparency. Added value of this studyWe developed faers, an integrated, high-fidelity R package that streamlines the pharmacovigilance workflow by unifying regulatory-compliant data preprocessing--including multi-level deduplication and MedDRA mapping--with a comprehensive suite of frequentist and Bayesian signal detection methods. Benchmarking demonstrated near-linear scalability on large-scale datasets, and the tools analytical accuracy was validated through the successful replication of representative studies on ICI-associated cardiotoxicity and CAR-T therapy outcomes. Furthermore, an exploratory analysis of immune-related adverse events revealed a significant age-by-sex interaction in reporting risk, illustrating the packages capacity to uncover nuanced demographic patterns that are often obscured by conventional analytical approaches. Implications of all the available evidenceThe R/Bioconductor package faers establishes a standardized, scalable, and fully reproducible framework for FAERS-based pharmacovigilance. By automating the transition from raw, heterogeneous data to actionable signal detection, it lowers technical barriers for researchers, clinicians, and regulators alike. This advancement promotes greater transparency and rigor in pharmacoepidemiology, ultimately strengthening post-marketing drug safety surveillance and providing a robust foundation for precision clinical practice and regulatory decision-making. AvailabilityThe faers package is freely available through the Bioconductor repository at https://bioconductor.org/packages/faers/. Its source code is openly hosted on GitHub at https://github.com/WangLabCSU/faers.