title: Automated method for detecting increases in frequency of spontaneous adverse event reports over time.
abstract: 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.
authors: Oracle Health Sciences, Burlington, Massachusetts, USA.
authors: William
authors: W
authors: DuMouchel
authors: Nancy
authors: N
authors: Yuen
authors: Nassrin
authors: N
authors: Payvandi
authors: Wendy
authors: W
authors: Booth
authors: Andrew
authors: A
authors: Rut
authors: David
authors: D
authors: Fram
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000592:standards; D064420:Drug-Related Side Effects and Adverse Reactions; D001330:Electronic Data Processing / Q000379:methods*; D006801:Humans; D012189:Retrospective Studies; D013997:Time Factors