Abstract
<title>Abstract</title> <p>The FDA Adverse Event Reporting System (FAERS) is a critical pillar of post-marketing pharmacovigilance, yet its utility is constrained by data heterogeneity, pervasive reporting redundancies, and inconsistent medical terminology, which impede reproducible large-scale analyses and precision drug safety surveillance. To address these barriers, we developed faers, an open-source R package that delivers a standardized, end-to-end workflow for transforming raw FAERS data into analysis-ready formats through a regulatory-compliant multi-level deduplication strategy, automated MedDRA terminology mapping, and an R S4-based object-oriented system that ensures data integrity, traceability, and efficient management of complex relational structures, while integrating 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). Benchmarked on large-scale quarterly FAERS datasets, the package exhibited high computational efficiency and near-linear scalability, and its validity was confirmed by reproducing published findings on anti-PD-1/PD-L1-associated cardiotoxicity and CAR-T cell therapy outcomes; further application to an immune-related adverse events (irAEs) cohort identified a statistically significant age-by-sex interaction in risk patterns, demonstrating the tool’s capacity to uncover nuanced demographic signals often overlooked by conventional approaches. In conclusion, faers provides a transparent, scalable, and fully reproducible framework for FAERS-based pharmacovigilance, lowering technical barriers for researchers and regulators and promoting high-quality, open pharmacoepidemiological research to strengthen drug safety monitoring.</p>