A Standardized Dataset of a Spontaneous Adverse Event Reporting System

One of the largest spontaneous adverse events reporting databases in the world is the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Unfortunately, researchers face many obstacles in analyzing data from the FAERS database. One of the major obstacles is the unstructured entry of drug names into the FAERS, as reporters might use generic names or trade names with different naming structures from all over the world and, in some cases, with typographical errors. Moreover, report duplication is a known problem in spontaneous adverse event-reporting systems, including the FAERS database. Hence, thorough text processing for database entries, especially drug name entries, coupled with a practical case-deduplication logic, is a prerequisite to analyze the database, which is a time- and resource-consuming procedure. In this study, we provide a clean, deduplicated, and ready-to-import dataset into any relational database management software of the FAERS database up to September 2021. Drug names are standardized to the RxNorm vocabulary and normalized to the single active ingredient level. Moreover, a pre-calculated disproportionate analysis is provided, which includes the reporting odds ratio (ROR), proportional reporting ratio (PRR), Chi-squared analysis with Yates correction (x(2)), and information component (IC) for each drug-adverse event pair in the database.

Automated summary

FAERS is one of the largest spontaneous adverse events reporting databases in the world, but analyzing its data presents many challenges. This study provides a processed and deduplicated dataset from the FAERS database, with standardized and normalized drug names. Additionally, pre-calculated disproportionate analysis is included for each drug-adverse event pair in the database.