Data Mining Techniques in Pharmacovigilance: Analysis of the Publicly Accessible FDA Adverse Event Reporting System (AERS)

Pharmacovigilance is a clinically oriented discipline, which may guide appropriate drug use through a balanced assessment of drug safety. Although much has been done in recent years, efforts are needed to expand the border of pharmacovigilance. We have provided insight into the FDA_Adverse Events Reporting Systems (FDA_AERS), a worldwide publicly available pharmacovigilance archive, to exemplify how to address major methodological issues. We believe that fostering discussion among researchers will increase transparency and facilitate definition of the most reliable approaches. By virtue of its large population coverage and free availability, the FDA_AERS has the potential to pave the way to a new way of looking to signal detection in PhV. Our key messages are: (1) before applying statistical tools (i.e., Data Mining Approaches - DMAs) to pharmacovigilance database for signal detection, all aspects related to data quality should be considered (e.g., drug mapping, missing data and duplicates); (2) at present, the choice of a given DMA mostly relies on local habits, expertise and attitude and there is room for improvement in this area; (3) DMA performance may be highly situation dependent; (4) over-reliance on these methods may have deleterious consequences, especially with the so-called "designated medical events", for which a case-by-case analysis is mandatory and complements disproportionality; and (5) the most appropriate selection of pharmacovigilance tools needs to be tailored to each situation, being mindful of the numerous biases and confounders that may influence performance and incremental utility of DMAs.