title: Commonality of drug-associated adverse events detected by 4 commonly used data mining algorithms.
abstract: OBJECTIVE
  Data mining algorithms have been developed for the quantitative detection of drug-associated adverse events (signals) from a large database on spontaneously reported adverse events. In the present study, the commonality of signals detected by 4 commonly used data mining algorithms was examined.
  
  
  METHODS
  A total of 2,231,029 reports were retrieved from the public release of the US Food and Drug Administration Adverse Event Reporting System database between 2004 and 2009. The deletion of duplicated submissions and revision of arbitrary drug names resulted in a reduction in the number of reports to 1,644,220. Associations with adverse events were analyzed for 16 unrelated drugs, using the proportional reporting ratio (PRR), reporting odds ratio (ROR), information component (IC), and empirical Bayes geometric mean (EBGM).
  
  
  RESULTS
  All EBGM-based signals were included in the PRR-based signals as well as IC- or ROR-based ones, and PRR- and IC-based signals were included in ROR-based ones. The PRR scores of PRR-based signals were significantly larger for 15 of 16 drugs when adverse events were also detected as signals by the EBGM method, as were the IC scores of IC-based signals for all drugs; however, no such effect was observed in the ROR scores of ROR-based signals.
  
  
  CONCLUSIONS
  The EBGM method was the most conservative among the 4 methods examined, which suggested its better suitability for pharmacoepidemiological studies. Further examinations should be performed on the reproducibility of clinical observations, especially for EBGM-based signals.
authors.affiliation: 1. Center for Integrative Education in Pharmacy and Pharmaceutical Sciences, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan;
authors.forename: Toshiyuki
authors.initials: T
authors.lastname: Sakaeda
authors.affiliation: 1. Center for Integrative Education in Pharmacy and Pharmaceutical Sciences, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan;
authors.forename: Kaori
authors.initials: K
authors.lastname: Kadoyama
authors.affiliation: 1. Center for Integrative Education in Pharmacy and Pharmaceutical Sciences, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan;
authors.forename: Keiko
authors.initials: K
authors.lastname: Minami
authors.affiliation: 2. Department of Systems Biosciences for Drug Discovery, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan.
authors.forename: Yasushi
authors.initials: Y
authors.lastname: Okuno
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D000465:Algorithms; D001499:Bayes Theorem; D057225:Data Mining; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology*; D006801:Humans; D014481:United States; D014486:United States Food and Drug Administration
keywords: Adverse Event Reporting System; FAERS; adverse event; data mining; database; empirical Bayes geometric mean.; information component; proportional reporting ratio; reporting odds ratio; signal; signal detection