To get started with the latest version of faers:
# Official Bioconductor installation
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("faers")faers is a high-performance R package designed to bridge the gap between raw FDA Adverse Event Reporting System (FAERS) data and actionable clinical insights. By integrating advanced deduplication, standardized medical vocabularies, and robust statistical models, it ensures high-fidelity real-world evidence generation.
Modern pharmacovigilance demands both computational efficiency and medical rigor. faers addresses these needs through a streamlined pipeline that supports large-scale epidemiological studies and precision safety surveillance.
We provide modular documentation to support your research journey:
The essential guide for most users. Follow a complete clinical case study (Insulin) from raw data ingestion to disproportionality signal detection.
For technical deep-dives. Detailed API documentation for every exported function, including parameter definitions and return values.
Understand how faers handles S4 objects and integrates
with MedDRA/RxNorm hierarchies.
faers?
The following snippet demonstrates the clarity of the
faers syntax:
library(faers)
# Download -> Standardize -> De-duplicate -> Signal Detection
data <- faers(2023, "q1") %>%
faers_standardize(meddra_path) %>%
faers_dedup()
results <- faers_phv_signal(data_filtered, .full = data)📥Data Acquisition: Automated downloading and parsing of FAERS quarterly data (supporting both ASCII and XML formats).
🛠Rigorous Preprocessing: Advanced multi-quarter data merging and robust deduplication logic to ensure high data fidelity.
🔍Terminology Standardization: Seamless integration with MedDRA, RxNorm, and the FDA Drugs API for precise mapping of drugs and adverse events.
📊Advanced Signal Detection: Comprehensive support for disproportionality analysis, including ROR, PRR, BCPNN, and EBGM.
⚡High-Performance Computing: Integrated with BiocParallel for memory-efficient, parallelized processing of millions of records.
🌐Knowledge Integration: Direct support for Athena drug vocabularies and Standardised MedDRA Queries (SMQ) for mechanism-driven research.
If you encounter questions or issues, please choose the appropriate channel:
Technical Issues: Post a reproducible example on the GitHub Issue Tracker.
Scientific Discussion: Visit the Bioconductor Support Site(Tag: faers).
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