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Drug Safety
Published in: Drug Safety 11/2005

01-11-2005 | Leading Article

Perspectives on the Use of Data Mining in Pharmacovigilance

Authors: Dr June Almenoff, Joseph M. Tonning, A. Lawrence Gould, Ana Szarfman, Manfred Hauben, Rita Ouellet-Hellstrom, Robert Ball, Ken Hornbuckle, Louisa Walsh, Chuen Yee, Susan T. Sacks, Nancy Yuen, Vaishali Patadia, Michael Blum, Mike Johnston, Charles Gerrits, Harry Seifert, Karol LaCroix

Published in: Drug Safety | Issue 11/2005

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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 pharmacovigilance. In 2003, a group of statisticians, pharmacoepidemiologists and pharmacovigilance 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 pharmacovigilance methods. Perspectives from both the FDA and the industry are provided.
Data mining is a potentially useful adjunct to traditional pharmacovigilance 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.
Footnotes
1
Access to the entire AERS database (public-release version) is available from commercial vendors on a subscription basis; these vendors offer the service of collecting all of the updates issued by the NTIS into one repository. Each vendor uses its own rules and algorithms to ‘clean’ the database by standardising drug names, accommodating changes to coding dictionaries and removing duplicate reports. Selection of a vendor may require an evaluation period to ensure that the methods used to format and clean the public data are acceptable to users.
 
Literature
1.
O’Neill RT, Szarfman A. Some FDA perspectives on data mining for pediatric safety assessment. Curr Ther Res Clin Exp 2001; 62(9): 650–63CrossRef
2.
Szarfman A, Machado SG, O’Neill RT. Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA’s spontaneous reports database. Drug Saf 2002; 25(6): 381–92PubMedCrossRef
3.
Szarfman A, Tonning JM, Doraiswamy PM. Pharmacovigilance in the 21st century: new systematic tools for an old problem. Pharmacotherapy 2004; 24(9): 1099–104PubMedCrossRef
4.
Hauben M. Early postmarketing drug safety surveillance: data mining points to consider. Ann Pharmacother 2004; 38: 1625–30PubMedCrossRef
5.
Wolkenstein P, Latarget J, Roujeau J, et al. Randomised comparison of thalidomide versus placebo in toxic epidermal necrolysis. Lancet 1998; 352: 1586–9PubMedCrossRef
6.
7.
Lindquist M, Edwards IR, Bate A, et al. From association to alert: a revised approach to international signal analysis. Pharmacoepidemiol Drug Saf 1999; 8: S15–25PubMedCrossRef
8.
Report of CIOMS Working Group VI. Management of safety information from clinical trials. Geneva: Council for International Organization of Medical Sciences (CIOMS), 2005
9.
Evans SJW, Waller PC, Davis S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiol Drug Saf 2001; 10: 483–6PubMedCrossRef
10.
Gould AL. Practical pharmacovigilance analysis strategies. Pharmacoepidemiol Drug Saf 2003; 12: 559–74PubMedCrossRef
11.
12.
Hauben M, Zhou X. Quantitative methods in pharmacovigilance: focus on signal detection. Drug Saf 2003; 26(3): 159–86PubMedCrossRef
13.
Wilson AM, Thabane L, Holbrook A. Application of data mining techniques in pharmacovigilance. Br J Clin Pharmacol 2003; 57(2): 127–34CrossRef
14.
15.
Egberts AC, Meyboom RH, van Puijenbroek EP. Use of measures of disproportionality in pharmacovigilance: three Dutch examples. Drug Saf 2002; 25(6): 453–8PubMedCrossRef
16.
van der Heijden PGM, van Puijenbroek EP, van Buuren S, et al. On the assessment of adverse drug reactions from spontaneous reporting systems: the influence of under-reporting on odds ratios. Stat Med 2002; 21: 2027–44PubMedCrossRef
17.
van Puijenbroek EP, Bate A, Leufkens HGM, et al. A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiol Drug Saf 2002; 11: 3–10PubMedCrossRef
18.
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: 315–21PubMedCrossRef
19.
Lindquist M, Stahl 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): 533–42PubMedCrossRef
20.
DuMouchel W. Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system. Am Stat 1999; 53(3): 177–202
21.
DuMouchel W, Pregibon D. Empirical Bayes screening for multi-item associations. In: Conference on Knowledge Discovery in Data. Proceedings of the seventh ACM SigKDD International Conference on Knowledge Discovery and Data Mining. 2001 Aug 26-29; San Francisco (CA). New York: ACM Press, 2001: 67–76
22.
Council for International Organizations of Medical Sciences (CIOMS). Guidelines for Preparing Core Clinical-Safety Information on Drugs. 2nd ed. Report of CIOMS Working Groups III and V. Geneva: Council for International Orgnizations of Medical Sciences (CIOMS), 1999: 27–33
23.
Purcell P, Barty S. Statistical techniques for signal generation: the Australian experience. Drug Saf 2002; 25(6): 415–21PubMedCrossRef
24.
Mamedov MA, Saunders GW. Fuzzy set analysis of Australian drug safety data. Proceedings of HIC 2002: Tenth National Health Informatics Conference; 2002 Dec 4-6, Melbourne
25.
Mamedov MA, Saunders GW, Yearwood J. A fuzzy derivative approach to classification of outcomes from the ADRAC database. International Transactions in Operational Research 2004; 11(2): 169–79CrossRef
26.
Spiegelhalter D, Grigg O, Kinsman R, et al. Risk adjusted sequential probability ratio tests: application to Bristol, Ship-man and adult cardiac surgery. Int J Qual Health Care 2003; 15: 7–13PubMedCrossRef
27.
Grigg OA, Farewell VT, Spiegelhalter DJ. Use of risk-adjusted CUSUM and RSPRT charts for monitoring in medical contexts. Stat Methods Med Res 2003; 12(2): 147–70PubMed
28.
Kulldorf M, Fang Z, Walsh SJ. A tree-based scan statistic for database disease surveillance. Biometrics 2003; 59: 323–31CrossRef
29.
Hauben M, Reich L. Drug-induced pancreatitis: lessons in data mining [letter]. Br J Clin Pharmacol 2004; 58(5): 560–2PubMedCrossRef
30.
Waller P, Heeley E, Moseley J. Impact analysis of signals detected from spontaneous adverse drug reaction reporting data. Drug Saf 2005; 28(10): 843–50PubMedCrossRef
31.
Waller PC, Heeley EL, Moseley JNS. Impact analysis of signals detected from spontaneous adverse reaction reporting data [abstract]. Pharmacoepidemiol Drug Saf 2004; 13: S323CrossRef
32.
Heeley E, Waller P, Moseley J. Testing and implementing signal impact analysis in a regulatory setting: results of a pilot study. Drug Saf 2005; 28(10): 901–6PubMedCrossRef
33.
Rothman KJ, Lanes S, Sacks ST. The reporting odds ratio and its advantages over the proportional reporting ratio. Pharmacoepidemiol Drug Saf 2004; 13: 519–23PubMedCrossRef
34.
Waller P, van Puijenbroek E, Egberts A, et al. The reporting odds ratio versus the proportional reporting ratio: ‘deuce’ [letter]. Pharmacoepidemiol Drug Saf 2004; 13: 525–6PubMedCrossRef
35.
Stahl 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: 355–63PubMedCrossRef
36.
Begaud B, Moride Y, Tubert-Bitter P, et al. False-positive in spontaneous reporting: should we worry about them? Br J Clin Pharmacol 1994; 38(5): 401–4PubMedCrossRef
37.
Hauben M. Application of an empiric Bayesian data mining algorithm to reports of pancreatitis associated with atypical antipsychotics. Pharmacotherapy 2004; 24(9): 1122–9PubMedCrossRef
38.
Hauben M. Trimethoprim-induced hyperkalaemia-lessons in data mining [letter]. Br J Clin Pharmacol 2004; 58(3): 338–9PubMedCrossRef
39.
Hauben M, Reich L. Safety related drug-labelling changes: findings from two data mining algorithms. Drug Saf 2004; 27(10): 735–44PubMedCrossRef
40.
Levine JG, Tonning JM, Szarfman A. Reply: the evaluation of data mining methods for the simultaneous and systematic detection of safety signals in large databases. Lessons to be learned [letter]. Br J Clin Pharmacol. In press
41.
Hauben M, Reich L. Response to letter by Levine et al. [letter]. Br J Clin Pharmacol. In press
42.
43.
44.
Cosentino M, Leoni O, Michielotto D, et al. Increased reporting of adverse reactions to ACE inhibitors associated with limitations to drug reimbursement for angiotensin-II antagonists. Eur J Clin Pharmacol 2001; 57: 509–12PubMedCrossRef
45.
Bate A, Edwards RI, Lindquist M, et al. The authors’ reply [letter]. Drug Saf 2003; 26(5): 364–6CrossRef
46.
Szarfman A, DuMouchel W, Fram D, et al. Lactic acidosis: unraveling the individual toxicities of drugs used in HIV and diabetes polytherapy by hierarchical Bayesian logistic regression data mining [abstract]. 11th Annual FDA Science Forum, 2005 Apr 27-28 [online]. Available from URL: http://​www.​cfsan.​fda.​gov/​~frf/​forum05/​H-30.​htm [Accessed 2005 Sep 14]
47.
Brown EG. Effects of coding dictionary on signal generation: a consideration of use of MedDRA compared with WHO-ART. Drug Saf 2002; 25(6): 445–52PubMedCrossRef
48.
Haramburu F, Begaud B, Moride Y. Temporal trends in spontaneous reporting of unlabelled adverse drug reactions. Br J Clin Pharmacol 1997; 44: 299–301PubMedCrossRef
49.
Manson JM, Freyssinges C, Ducrocq MB, et al. Postmarketing surveillance of lovastatin and simvastatin exposure during pregnancy. Reprod Toxicol 1996; 10(6): 439–46PubMedCrossRef
50.
Blais L, Ernst P, Suissa S. Confounding by indication and channeling over time: the risks of beta 2-agonists. Am J Epidemiol 1996; 15(12): 1161–9CrossRef
51.
Blais L, Ernst P, Suissa S. The authors’ reply [letter]. Am J Epidemiol 1997; 146(10): 886–7CrossRef
52.
Leufkens HG. Pharmacoepidemiology and gastroenterology: a close couple. Scand J Gastroenterol Suppl 2000; 232: 105–8PubMed
53.
Leufkens H, Urquhart J. Variability in patterns of drug usage. J Pharm Pharmacol 1994; 46Suppl. 1: 433–7PubMed
54.
Meijer WEE, Heerdink ER, Pepplinkhuizen LP, et al. Prescribing patterns in patients using new antidepressants. Br J Clin Pharmacol 2001; 51: 181–3PubMedCrossRef
55.
Pearce N, Beasley R, Crane J, et al. Confounding by indication and channeling over time: the risks of beta-2 agonists [letter]. Am J Epidemiol 1997; 146(10): 885–6PubMedCrossRef
56.
deBruin ML, van Puijenbroek EP, Egberts ACG, et al. Nonsedating antihistamine drugs and cardiac arrhythmias: biased risk estimates from spontaneous reporting systems? Br J Clin Pharmacol 2002; 53: 370–4CrossRef
57.
Tisonova J, Szalayova A, Kriska M. Factors influencing the spontaneous reporting of adverse drug reactions: the experience of the Slovak Republic. Pharmacoepidemiol Drug Saf 2003; 13: 333–7CrossRef
58.
Coster TS, Szarfman A, Tonning J. The application of data mining to analyze pre-publicity psychiatric signals with the use of mefloquine [abstract]. ASCPT Annual Meeting; 2004 Mar 4; Miami (FL)
59.
Varricchio F, Iskander J, Destefano F, et al. Understanding vaccine safety information from the vaccine adverse event reporting system (VAERS). Pediatr Infect Dis J 2004; 23: 287–94PubMedCrossRef
60.
61.
Yee CL, Klincewicz SL, Knight JF, et al. Practical considerations in developing an automated signaling program within a pharmacovigilance department. Drug Inf J 2004; 38: 293–300CrossRef
62.
63.
Niu MT, Erwin DE, Braun MM. Data mining in the US vaccine adverse event reporting system: early detection of intussusception and other events after rota virus vaccine. Vaccine 2001; 19: 4627–34PubMedCrossRef
64.
Banks D, Woo EJ, Burwen D, et al. Comparison of 4 data mining methods in the US Vaccine Adverse Event Reporting System (VAERS) [abstract]. Pharmacoepidemiol Drug Saf 2003; 12Suppl. 1: S138
65.
Begier EM, Burwen D, Haber P, et al. Post-marketing safety surveillance for typhoid fever vaccines from the Vaccine Adverse Event Reporting System, July 1990-June 2002. Clin Infect Dis 2004; 38: 771–9PubMedCrossRef
66.
McMahon AW, Bryant-Genevier MC, Woo EJ, et al. Photophobia following smallpox vaccination [letter]. Vaccine 2005; 23: 1097–8PubMedCrossRef
67.
Zhou W, Pool V, DeStefano F, et al. A potential signal of Bell’s palsy after parenteral inactivated influenza vaccines: reports to the vaccine adverse event reporting system (VAERS): United States, 1991-2001. Pharmacoepidemiol Drug Saf 2004; 13: 505–10PubMedCrossRef
68.
Shapiro S. Clinical judgment, common sense and adverse reaction reporting. Pharmacoepidemiol Drug Saf 2004; 13: 511–3PubMedCrossRef
69.
Zhou W, Pool V, DeStefano F, et al. Reply to the editorial. Pharmacoepidemiol Drug Saf 2004; 13: 515–7CrossRef
70.
van Puijenbroek EP, Egberts ACG, Heerdink ER, et al. Detecting drug-drug interactions using a database for spontaneous adverse drug reactions: an example with diuretics and nonsteroidal anti-inflammatory drugs. Eur J Clin Pharmacol 2000; 56: 733–8PubMedCrossRef
71.
Almenoff JS, DuMouchel W, Kindman A, et al. Disproportionality analysis using empirical Bayes data mining: a tool for the evaluation of drug interactions in the post-marketing setting. Pharmacoepidemiol Drug Saf 2003; 12(6): 517–21PubMedCrossRef
72.
73.
Yuen NA, Almenoff JS, DuMouchel W, et al. Disproportionality analysis to explore patient and treatment related factors associated with adverse events [abstract]. Pharmacoepidemiol Drug Saf 2004; 13: S259
74.
Szarfman A. Gender-related ‘higher-than-expected’ drug-event combinations in spontaneous adverse drug event reports [abstract no. D05]. 2000 FDA Science Forum - FDA and the science of safety: new perspectives; 2000 Feb 14-15; Washington, DC [online]. Available from URL: http://​www.​vm.​cfsan.​fda.​gov/​~frf/​forum00/​d05.​htm [Accessed 2004 Oct 12]
75.
Gogolak VV. The effect of backgrounds in safety analysis: the impact of comparison cases on what you see. Pharmacoepidemiol Drug Saf 2003; 12: 249–52PubMedCrossRef
Metadata
Title
Perspectives on the Use of Data Mining in Pharmacovigilance
Authors
Dr June Almenoff
Joseph M. Tonning
A. Lawrence Gould
Ana Szarfman
Manfred Hauben
Rita Ouellet-Hellstrom
Robert Ball
Ken Hornbuckle
Louisa Walsh
Chuen Yee
Susan T. Sacks
Nancy Yuen
Vaishali Patadia
Michael Blum
Mike Johnston
Charles Gerrits
Harry Seifert
Karol LaCroix
Publication date
01-11-2005
Publisher
Springer International Publishing
Published in
Drug Safety / Issue 11/2005
Print ISSN: 0114-5916
Electronic ISSN: 1179-1942
DOI
https://doi.org/10.2165/00002018-200528110-00002

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