title: Perspectives on the use of data mining in pharmaco-vigilance.
abstract: In the last 5 years, regulatory agencies and drug monitoring centres have been developing computerised data-mining methods to better identify reporting relationships in spontaneous reporting databases that could signal possible adverse drug reactions. At present, there are no guidelines or standards for the use of these methods in routine pharmaco-vigilance. In 2003, a group of statisticians, pharmaco-epidemiologists and pharmaco-vigilance professionals from the pharmaceutical industry and the US FDA formed the Pharmaceutical Research and Manufacturers of America-FDA Collaborative Working Group on Safety Evaluation Tools to review best practices for the use of these methods.In this paper, we provide an overview of: (i) the statistical and operational attributes of several currently used methods and their strengths and limitations; (ii) information about the characteristics of various postmarketing safety databases with which these tools can be deployed; (iii) analytical considerations for using safety data-mining methods and interpreting the results; and (iv) points to consider in integration of safety data mining with traditional pharmaco-vigilance methods. Perspectives from both the FDA and the industry are provided. Data mining is a potentially useful adjunct to traditional pharmaco-vigilance methods. The results of data mining should be viewed as hypothesis generating and should be evaluated in the context of other relevant data. The availability of a publicly accessible global safety database, which is updated on a frequent basis, would further enhance detection and communication about safety issues.
authors.affiliation: GlaxoSmithKline, Research Triangle Park, North Carolina, USA. june.s.almenoff@gsk.com
authors.forename: June
authors.initials: J
authors.lastname: Almenoff
authors.forename: Joseph M
authors.initials: JM
authors.lastname: Tonning
authors.forename: A Lawrence
authors.initials: AL
authors.lastname: Gould
authors.forename: Ana
authors.initials: A
authors.lastname: Szarfman
authors.forename: Manfred
authors.initials: M
authors.lastname: Hauben
authors.forename: Rita
authors.initials: R
authors.lastname: Ouellet-Hellstrom
authors.forename: Robert
authors.initials: R
authors.lastname: Ball
authors.forename: Ken
authors.initials: K
authors.lastname: Hornbuckle
authors.forename: Louisa
authors.initials: L
authors.lastname: Walsh
authors.forename: Chuen
authors.initials: C
authors.lastname: Yee
authors.forename: Susan T
authors.initials: ST
authors.lastname: Sacks
authors.forename: Nancy
authors.initials: N
authors.lastname: Yuen
authors.forename: Vaishali
authors.initials: V
authors.lastname: Patadia
authors.forename: Michael
authors.initials: M
authors.lastname: Blum
authors.forename: Mike
authors.initials: M
authors.lastname: Johnston
authors.forename: Charles
authors.initials: C
authors.lastname: Gerrits
authors.forename: Harry
authors.initials: H
authors.lastname: Seifert
authors.forename: Karol
authors.initials: K
authors.lastname: Lacroix
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D003625:Data Collection / Q000379:methods*; D016208:Databases, Factual; D004345:Drug Industry; D006801:Humans; D016247:Information Storage and Retrieval; D011358:Product Surveillance, Postmarketing / Q000706:statistics & numerical data*; D009626:Terminology as Topic; D014481:United States; D014486:United States Food and Drug Administration