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

Background: The 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. Methods: We 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). Findings: The 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 tool's ability to uncover nuanced demographic signals that are often missed by conventional approaches. Interpretation: The 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.