Bayesian pharmacovigilance signal detection methods revisited in a multiple comparison setting

Statistics in Medicine

Volumn 28, Issue 13, 2009, Pages 1774-1792

  Ahmed, Ismail ^a,b   Haramburu, Françoise ^c,d   Fourrier Réglat, Annie ^c,d,e   Thiessard, Frantz ^a,d,e   Kreft Jais, Carmen ^f   Miremont Salamé, Ghada ^c,d,e   Bégaud, Bernard ^c,d,e   Tubert Bitter, Pascale ^a,b  

^a INSERM   (France)

^b UNIVERSITÉ PARIS SACLAY   (France)

^c INSERM U657   (France)

^d HÔPITAL PELLEGRIN   (France)

^e UNIVERSITÉ DE BORDEAUX   (France)

^f AFSSAPS   (France)

Author keywords

Bayesian decision; Data mining; False discovery rate; Multiple comparisons; Pharmacovigilance

Indexed keywords

DRUG;

ADVERSE DRUG REACTION; ARTICLE; BAYES THEOREM; BAYESIAN CONFIDENCE PROPAGATION NEURAL NETWORK; CALCULATION; CONTROLLED STUDY; DRUG SURVEILLANCE PROGRAM; GAMMA POISON SHRINKAGE; HUMAN; INTERMETHOD COMPARISON; PROBABILITY; SENSITIVITY AND SPECIFICITY; SIGNAL DETECTION; SIMULATION; STATISTICAL MODEL; UNSPECIFIED SIDE EFFECT;

ADVERSE DRUG REACTION REPORTING SYSTEMS; BAYES THEOREM; BIOMETRY; DATABASES, FACTUAL; HUMANS; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 69949106074     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3586     Document Type: Article

References (32)

1. Moride Y, Haramburu F, Requejo AA, Bégaud B. Under-reporting of adverse drug reactions in general practice. British Journal of Clinical Pharmacology 1997; 43:177-181.
2. van der Heijden PG, Van Puijenbroek EP, Van Buuren S, Van der Hofstede JW. On the assessment of adverse drug reactions from spontaneous reporting systems: the influence of under-reporting on odds ratios. Statistics in Medicine 2002; 21(14):2027-2044. DOI: 10.1002/ sim.1157. Available from: http://dx.doi.org/10.1002/sim.1157.
3. Rossi AC, Knapp DE, Anello C, O'Neil RT, Graham CF, Mendelis PS, Stanley GR. Discovery of adverse drug reactions; a comparison of selected phase iv studies with spontaneous reporting methods. Journal of the American Medical Association 1983; 249(16):2226-2228.
4. Rawlins MD. Spontaneous reporting of adverse drug reactions part I and II. British Journal of Clinical Pharmacology 1988; 26:1-5, 7-11.
5. Arnaiz JA, Carne X, Riba N, Codina C, Trilla A. The use of evidence in pharmacovigilance case reports as the reference source for drugs withdrawals. European Journal of Clinical Pharmacology 2001; 57(1):89-91.
6. Almenoff JS, Tonning JM, Gould AL, Szarfman A, Hauben M, Ouellet-Hellstrom R, Ball R, Hornbuckle K, Walsh L, Yee C, Sacks ST, Yuen NA, Patadia V, Blum M, Johnston M, Gerrits C, Seifert H, LaCroix KK. Perspectives on the use of data mining in pharmacovigilance. Drug Safety 2005; 28(11):981-1007.
7. Thiessard F, Roux E, Miremont-Salamé G, Fourrier-Réglat A, Haramburu F, Tubert-Bitter P, Bégaud B. Trends in spontaneous adverse drug reaction reports to the French pharmacovigilance system (1986-2001). Drug Safety 2005; 28(8):731-740.
8. Evans SJ, Waller PC, Davis S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiology and Drug Safety 2001; 10(6):483-486.
9. van Puijenbroek EP, Bate A, Leufkens HG, Lindquist M, Orre R, Egberts AC. A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiology and Drug Safety 2002; 11(1):3-10. DOI: 10.1002/pds.668. Available from: http://dx.doi.org/10.1002/pds.668.
10. Bate A, Lindquist M, Edwards IR, Olsson S, Orre R, Lansner A, De Freitas RM. A Bayesian neural network method for adverse drug reaction signal generation. European Journal of Clinical Pharmacology 1998; 54(4):315-321.
11. Norén GN, Bate A, Orre R, Edwards IR. Extending the methods used to screen the who drug safety database towards analysis of complex associations and improved accuracy for rare events. Statistics in Medicine 2006; 25(21):3740-3757.
12. DuMouchel W. Bayesian data mining in large frequency tables, with an application to the fda spontaneous reporting system. The American Statistician 1999; 53(3):177-190.
13. DuMouchel W, Pregibon D. Empirical Bayes screening for multi-item associations. KDD '01: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, 2001; 67-76. Available from: http://doi.acm.org/10.1145/ 502512.502526.
14. Bate A, Lindquist M, Edwards IR. The application of knowledge discovery in databases to post-marketing drug safety: example of the who database. Fundamental and Clinical Pharmacology 2008; 22(2):127-140.
15. Rolka H, Bracy B, Russell C, Fram D, Ball R. Using simulation to assess the sensitivity and specificity of a signal detection tool for multidimensional public health surveillance data. Statistics in Medicine 2005; 24(4):551-562. DOI: 10.1002/sim.2035. Available from: http://dx.doi.org/10.1002/sim.2035.
16. Roux E, Thiessard F, Fourrier A, Bégaud B, Tubert-Bitter P. Evaluation of statistical association measures for the automatic signal generation in pharmacovigilance. IEEE Transactions on Information Technology in Biomedicine 2005; 9(4):518-527.
17. 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 Safety 2006; 29(10):875-887.
18. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B 1995; 57(1):289-300.
19. Efron B, Tibshirani R, Storey JD, Tusher V. Empirical Bayes analysis of a microarray experiment. Journal of the American Statistical Association 2001; 96(456):1151-1160.
20. Storey JD. A direct approach to false discovery rates. Journal of the Royal Statistical Society, Series B 2002; 64(3):479-498.
21. Storey JD. The positive false discovery rate: a Bayesian interpretation and the q-value. The Annals of Statistics 2003; 31(6):2013-2035.
22. Broët P, Lewin A, Richardson S, Dalmasso C, Magdelenat H. A mixture model-based strategy for selecting sets of genes in multiclass response microarray experiments. Bioinformatics 2004; 20(16):2562-2571. DOI: 10.1093/bioinformatics/bth285. Available from: http://dx.doi.org/10.1093/bioinformatics/bth285.
23. McLachlan GJ, Bean RW, Jones LB-T. A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics 2006; 22(13):1608-1615. DOI: 10.1093/bioinformatics/btl148. Available from: http://dx.doi.org/10.1093/ bioinformatics/btl148.
24. Ahmed I, Haramburu F, Fourrier A, Thiessard F, Bégaud B, Tubert-Bitter P. A new automatic signal detection method in pharmacovigilance: a Bayesian multiple comparison setting. Pharmacoepidemiology and Drug Safety 2006; S1:22.
25. Gould AL. Accounting for multiplicity in the evaluation of 'ignals' obtained by data mining from spontaneous report adverse event databases. Biometrical Journal 2007; 49:151-165.
26. Müller P, Parmigiani G, Robert C, Rousseau J. Optimal sample size for multiple testing: the case of gene expression microarrays. Journal of the American Statistical Association 2004; 99:990-1001.
27. Gould AL. Practical pharmacovigilance analysis strategies. Pharmacoepidemiology and Drug Safety 2003; 12(7):559-574. DOI: 10.1002/pds.771. Available from: http://dx.doi.org/10.1002/pds.771.
28. Szarfman A, Machado S, O'Neill R. 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 Safety 2002; 25(6):381-392.
29. Müller P, Parmigiani G, Rice K. FDR and Bayesian multiple comparisons rules. Valencia/ISBA Eighth World Meeting on Bayesian Statistics, Benidorm, Alicante, Spain, 2006.
30. Miller GC, Britt H. A new drug classification for computer systems: the ATC extension code. International Journal of Bio-Medical Computing 1995; 40(2):121-124.
31. Brown EG, Wood L, Wood S. The medical dictionary for regulatory activities (MedDRA). Drug Safety 1999; 20(2):1999.
32. Lindquist M, Ståhl M, Bate A, Edwards I, Meyboom RHB. A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the who international database. Drug Safety 2000; 23(6):533-542.