Automated method for detecting increases in frequency of spontaneous adverse event reports over time.

A statistical methodology--focused on temporal change detection--was developed to highlight excursions from baseline spontaneous adverse event (AE) reporting. We used regression (both smooth trend and seasonal components) to model the time course of a drug's reports containing an AE, and then compared the sum of counts in the past 2 months with the fitted trend. The signaling threshold was tuned, using retrospective analysis, to yield acceptable sensitivity and specificity. The method may enhance pharmacovigilance by providing effective automated alerting of reporting aberrations when databases are small, when drugs have established safety profiles, and/or when product quality issues are of concern.