Comparison of statistical signal detection methods within and across spontaneous reporting databases

Drug Safety

Volumn 38, Issue 6, 2015, Pages 577-587

  Candore, Gianmario ^a   Juhlin, Kristina ^b   Manlik, Katrin ^c   Thakrar, Bharat ^d   Quarcoo, Naashika ^e   Seabroke, Suzie ^f   Wisniewski, Antoni ^g   Slattery, Jim ^a  

^a EUROPEAN MEDICINES AGENCY   (United Kingdom)

^b UPPSALA MONITORING CENTRE   (Sweden)

^c BAYER PHARMA AG   (Germany)

^d F HOFFMANN LA ROCHE LTD   (Switzerland)

^e GLAXOSMITHKLINE   (United Kingdom)

^f MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY   (United Kingdom)

^g ASTRAZENECA   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DRUG; VACCINE;

ACCURACY; ADVERSE DRUG REACTION; ALGORITHM; ARTICLE; CLINICAL TRIAL (TOPIC); COMPARATIVE STUDY; DATA BASE; DRUG INDUSTRY; DRUG MARKETING; DRUG SURVEILLANCE PROGRAM; HUMAN; PRIORITY JOURNAL; SIGNAL DETECTION; STATISTICAL ANALYSIS; VALIDATION STUDY; DRUG-RELATED SIDE EFFECTS AND ADVERSE REACTIONS; FACTUAL DATABASE; SENSITIVITY AND SPECIFICITY; STATISTICS AND NUMERICAL DATA;

ADVERSE DRUG REACTION REPORTING SYSTEMS; ALGORITHMS; DATABASES, FACTUAL; DRUG-RELATED SIDE EFFECTS AND ADVERSE REACTIONS; HUMANS; PHARMACOVIGILANCE; SENSITIVITY AND SPECIFICITY;

EID: 84938093529     PISSN: 01145916     EISSN: 11791942     Source Type: Journal    
DOI: 10.1007/s40264-015-0289-5     Document Type: Article

References (35)

1. Bate A, Lindquist M, Edwards IR, Olsson S, Orre R, Lansner A, et al. A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol. 1998;54(4):315–21.
2. 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(6):483–6.
3. Lindquist M, Stahl M, Bate A, Edwards IR, Meyboom RHB. 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):533–42.
4. 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):381–92.
5. Van Puijenbroek EP, Diemont WL, Van Grootheest K. Application of quantitative signal detection in the Dutch spontaneous reporting system for adverse drug reactions. Drug Saf. 2003;26(5):293–301.
6. Almenoff J, Tonning JM, Gould AL, Szarfman A, Hauben M, Ouellet-Hellstrom R, et al. Perspectives on the use of data mining in pharmacovigilance. Drug Saf. 2005;28(11):981–1007.
7. Roux E, Thiessard F, Fourrier A, Begaud B, Tubert-Bitter P. Evaluation of statistical association measures for the automatic signal generation in pharmacovigilance. IEEE Trans Inf Technol Biomed. 2005;9(4):518–27.
8. Czarnecki A, Voss S. Safety signals using proportional reporting ratios from company and regulatory authority databases. Drug Inf J 2008. 2008;42(3):205–210.
9. Hauben M, Horn S, Reich L. Potential use of data-mining algorithms for the detection of ‘surprise’ adverse drug reactions. Drug Saf. 2007;30(2):143–55.
10. Bate A, Lindquist M, Edwards IR. The application of knowledge discovery in databases to post-marketing drug safety: example of the WHO database. Fundam Clin Pharmacol. 2008;22(2):127–40.
11. Banks D, Woo EJ, Burwen DR, Perucci P, Braun MM, Ball R. Comparing data mining methods on the VAERS database. Pharmacoepidemiol Drug Saf. 2005;14(9):601–9.
12. Gipson G. A shrinkage-based comparative assessment of observed-to-expected disproportionality measures. Pharmacoepidemiol Drug Saf. 2012;21(6):589–96.
13. Hauben M, Aronson JK. Defining ‘signal’ and its subtypes in pharmacovigilance based on a systematic review of previous definitions. Drug Saf. 2009;32(2):99–110.
14. Finney DJ. Statistical logic in the monitoring of reactions to therapeutic drugs. Methods Inf Med. 1971;10(4):237–45.
15. Declerck G, Bousquet C, Jaulent MC. Automatic generation of MedDRA terms groupings using an ontology. Stud Health Technol Inform. 2012;180:73–7.
16. Hochberg AM, Hauben M, Pearson RK, OHara DJ, Reisinger SJ, Goldsmith DI, et al. An evaluation of three signal-detection algorithms using a highly inclusive reference event database. Drug Saf. 2009;32(6):509–25.
17. Slattery J, Alvarez Y, Hidalgo A. Choosing thresholds for statistical signal detection with the proportional reporting ratio. Drug Saf. 2013;36(8):687–92.
18. Gogolak VV. The effect of backgrounds in safety analysis: The impact of comparison cases on what you see. Pharmacoepidemiol Drug Saf. 2003;12(3):249–52.
19. Hammond IW, Gibbs TG, Seifert HA, Rich DS. Database size and power to detect safety signals in pharmacovigilance. Expert Opin Drug Saf. 2007;6(6):713–21.
20. Hammond IW, Rich DS, Gibbs TG. Effect of consumer reporting on signal detection: Using disproportionality analysis. Expert Opin Drug Saf. 2007;6(6):705–12.
21. Almenoff JS, LaCroix KK, Yuen NA, Fram D, DuMouchel W. Comparative performance of two quantitative safety signalling methods: implications for use in a pharmacovigilance department. Drug Saf. 2006;29(10):875–87.
22. Brown JS, Petronis K, Bate A, Zhang F, Dashevsky I, Kulldorff M, et al. Comparing two methods for detecting adverse event signals in observational data: empirical Bayes gamma poisson shrinker vs. tree-based scan statistic. Pharmacoepidemiol Drug Saf. 2011;20:S144.
23. Bunchuailua W, Zuckerman I, Kulsomboon V, Suwankesawong W, Singhasivanon P, Kaewkungwal J. A comparison of signal detection performance between reporting ODDS ratio and Bayesian confidence propagation neural network methods on adverse drug reaction spontaneous reporting database of the Thai FDA. Value Health. 2010;13(7):A508.
24. Chen Y, Guo JJ, Steinbuch M, Lin X, Buncher CR, Patel NC. Comparison of sensitivity and timing of early signal detection of four frequently used signal detection methods: An empirical study based on the US FDA adverse event reporting system database. Pharm Med. 2008;22(6):359–65.
25. Harpaz R, Dumouchel W, Lependu P, Bauer-Mehren A, Ryan P, Shah NH. Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system. Clin Pharmacol Ther. 2013;93(6):539–46.
26. Van Puijenbroek EP, Bate A, Leufkens HGM, Lindquist M, Orre R, Egberts ACG. A comparison of measures of disproportionality for signal detection is spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiol Drug Saf. 2002;11(1):3–10.
27. Caster O, Noren G, Niklas, Madigan D, Bate A. Large-scale regression-based pattern discovery: the example of screening the WHO global drug safety database. Stat Anal Data Min. 2010;3(4):197–208.
28. Tatonetti NP, Ye PP, Daneshjou R, Altman RB. Data-driven prediction of drug effects and interactions. Sci Transl Med. 2012;4(125):125ra31.
29. Kubota K, Koide D, Hirai T. Comparison of data mining methodologies using Japanese spontaneous reports. Pharmacoepidemiol Drug Saf. 2004;13(6):387–94.
30. Kurz X, Slattery J, Addis A, Durand J, Segec A, Skibicka I, et al. The EudraVigilance database of spontaneous adverse reactions as a tool for H1N1 vaccine safety monitoring. Pharmacoepidemiol Drug Saf. 2010;19:S330–1.
31. Alvarez Y, Hidalgo A, Maignen F, Slattery J. Validation of statistical signal detection procedures in EudraVigilance post-authorization data: a retrospective evaluation of the potential for earlier signalling. Drug Saf. 2010;33(6):475–87.
32. IMI PROTECT. ADR database. http://www.imi-protect.eu/methodsRep.shtml. Accessed 17 Mar 2014.
33. Clark JA, Klincewicz SL, Stang PE. Spontaneous adverse event signaling methods: classification and use with health care treatment products. Epidemiol Rev. 2001;23(2):191–210.
34. Norén GN, Hopstadius J, Bate A. Shrinkage observed-to-expected ratios for robust and transparent large-scale pattern discovery. Stat Methods Med Res. 2013;22(1):57–69.
35. DuMouchel W, Pregibon D. Empirical Bayes screening for multi-item associations. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining. San Francisco: ACM Press; 2001. pp. 67–76.