Drug Safety

, Volume 33, Issue 7, pp 527–534 | Cite as

A Decade of Data Mining and Still Counting

  • Manfred Haubenand
  • G. Niklas Norén

The introduction of database-wide disproportionality screening for signal detection in spontaneous reporting systems (SRS)[1] sparked a renaissance in pharmacovigilance research notable for numerous peer reviewed research articles, three expert working groups/white papers,[2, 3, 4] countless meetings, symposia, workshops, graduate school theses and aggressive promotion of proprietary software. In addition to expanding the pharmacovigilance toolkit, this research has yielded ancillary benefits beyond patient safety, including an increased awareness of data quality issues such as case report duplication,[5,6] the importance of adverse event coding terminology,[7,8] the proper definition of signal in drug safety,[9] the logic of signal detection [10] and an admonition that conflicts of interest, both intellectual and financial, may not only involve the ‘usual suspects’ such as software vendors, but also other stakeholders that may not normally come to mind, such as regulatory...



No sources of funding were used in the preparation of this editorial. Manfred Hauben is a full-time employee of Pfizer Inc., and owns stock/stock options in Pfizer Inc. and other pharmaceutical companies. Niklas Norén has no conflicts of interest to declare.


  1. 1.
    Bate A, Lindquist M, Edwards IR, et al. A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol 1998; 54(4): 315–21PubMedCrossRefGoogle Scholar
  2. 2.
    Almenoff J, Tonning JM, Gould AL, et al. Perspectives on the use of data mining in pharmacovigilance. Drug Saf 2005; 28(11): 981–1007PubMedCrossRefGoogle Scholar
  3. 3.
    European Medicines Agency, EudraVigilance Expert Working Group. Guideline on the use of statistical signal detection methods in the EudraVigilance data analysis system [online]. Available from URL: [Accessed 2010 May 5]
  4. 4.
    CIOMS Working Group VIII. Report on practical aspects of signal detection in pharmacovigilance. Geneva: CIOMS. In pressGoogle Scholar
  5. 5.
    Norén GN, Orre R, Bate A, et al. Duplicate detection in adverse drug reaction surveillance. Data Min Knowl Discov 2007; 14: 305–28CrossRefGoogle Scholar
  6. 6.
    Hauben M, Reich L, DeMicco J, et al. Extreme duplication in the US FDA Adverse Events Reporting System database. Drug Saf 2007; 30(6): 551–4PubMedCrossRefGoogle Scholar
  7. 7.
    Henegar C, Bousquet C, Lillo-Le Louët A, et al. Building an ontology of adverse drug reactions for automated signal generation in pharmacovigilance. Comput Biol Med 2006; 36(7–8): 748–67PubMedCrossRefGoogle Scholar
  8. 8.
    Brown EG. Effects of coding dictionary on signal generation: a consideration of use of MedDRA compared with WHO-ART. Drug Saf 2002; 25(6): 445–52PubMedCrossRefGoogle Scholar
  9. 9.
    Hauben M, Aronson JK. Defining ‘signal’ and its subtypes in pharmacovigilance based on a systematic review of previous definitions. Drug Saf 2009; 32(2): 99–110PubMedCrossRefGoogle Scholar
  10. 10.
    Meyboom RH, Lindquist M, Egberts AC, et al. Signal selection and follow-up in pharmacovigilance. Drug Saf 2002; 25(6): 459–65PubMedCrossRefGoogle Scholar
  11. 11.
    Erratum. Br J Clin Pharmacol 2007; 64 (1): 118Google Scholar
  12. 12.
    Lindquist M, Ståhl M, Bate A, et al. A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database. Drug Saf 2000; 23(6): 533–42PubMedCrossRefGoogle Scholar
  13. 13.
    Hochberg AM, Hauben M, Pearson RK, et al. An evaluation of three signal-detection algorithms using a highly inclusive reference event database. Drug Saf 2009; 32(6): 509–25PubMedCrossRefGoogle Scholar
  14. 14.
    Bailey S, Singh A, Azadian R, et al. Prospective data mining of six products in the US FDA Adverse Event Reporting System: disposition of events identified and impact on product safety profiles. Drug Saf 2010; 33(2): 139–46PubMedCrossRefGoogle Scholar
  15. 15.
    Alvarez Y, Hidalgo A, Maignen F, et al. Validation of statistical signal detection procedures in EudraVigilance post-authorisation data: a retrospective evaluation of the potential for earlier signalling. Drug Saf 2010; 33(6): 475–87PubMedCrossRefGoogle Scholar
  16. 16.
    Ståhl M, Lindquist M, Edwards IR, et al. Introducing triage logic as a new strategy for the detection of signals in the WHO Drug Monitoring Database. Pharmacoepidemiol Drug Saf 2004; 13(6): 355–63PubMedCrossRefGoogle Scholar
  17. 17.
    Levitan B, Yee CL, Russo L, et al. A model for decision support in signal triage. Drug Saf 2008; 31(9): 727–35PubMedCrossRefGoogle Scholar
  18. 18.
    Walker AM. Orthogonal predictions: follow-up questions for suggestive data. Pharmacoepidemiol Drug Saf 2010; 19(5): 529–32PubMedGoogle Scholar
  19. 19.
    Hauben M, Reich L, Gerrits CM, et al. Illusions of objectivity and a recommendation for reporting data mining results. Eur J Clin Pharmacol 2007; 63(5): 517–21PubMedCrossRefGoogle Scholar
  20. 20.
    Hauben M, Bate A. Data mining in drug safety: side effects of drugs essay. In: Aronson JK, editor. Side effects of drugs annual. Vol. 29. Amsterdam: Elsevier, 2007: xxxiii–xlviCrossRefGoogle Scholar
  21. 21.
    Taubes G. Epidemiology faces its limits. Science 1995; 269(5221): 164–9PubMedCrossRefGoogle Scholar
  22. 22.
    Meyboom RH, Hekster YA, Egberts AC, et al. Causal or casual? The role of causality assessment in pharmacovigilance. Drug Saf 1997; 17(6): 374–89PubMedCrossRefGoogle Scholar
  23. 23.
    Aronson JK, Hauben M. Anecdotes that provide definitive evidence. BMJ 2006; 333(7581): 1267–9PubMedCrossRefGoogle Scholar
  24. 24.
    Hauben M, Reich L. Response to letter by Levine et al. Br J Clin Pharmacol 2006; 61(1): 115–7CrossRefGoogle Scholar
  25. 25.
    Almenoff JS, LaCroix KK, Yuen NA, et al. Comparative performance of two quantitative safety signalling methods: implications for use in a pharmacovigilance department. Drug Saf 2006; 29(10): 875–87PubMedCrossRefGoogle Scholar
  26. 26.
    Hochberg AM, Hauben M. Time-to-signal comparison for drug safety data-mining algorithms vs. traditional signaling criteria. Clin Pharmacol Ther 2009; 85(6): 600–6PubMedCrossRefGoogle Scholar
  27. 27.
    Bate A, Edwards IR. Data mining in spontaneous reports. Basic Clin Pharmacol Toxicol 2006; 98(3): 324–30PubMedCrossRefGoogle Scholar

Copyright information

© Adis Data Information BV 2010

Authors and Affiliations

  1. 1.Pfizer Inc.New YorkUSA
  2. 2.New York University School of MedicineNew YorkUSA
  3. 3.New York Medical CollegeValhallaUSA
  4. 4.Brunel UniversityWest LondonUK
  5. 5.Uppsala Monitoring CentreUppsalaSweden
  6. 6.Stockholm UniversityStockholmSweden

Personalised recommendations