Evaluation of reporting bias in postmarketing risk assessment based on spontaneous reporting systems

Pharmaceutical Medicine

Volumn 23, Issue 4, 2009, Pages 195-200

  Alemayehu, Demissie ^a  

^a PFIZER INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EVALUATION; HUMAN; MATERIALS HANDLING; MEDICAL RESEARCH; NOTE; POSTMARKETING SURVEILLANCE; PRIORITY JOURNAL; RELIABILITY; RISK ASSESSMENT; STATISTICAL ANALYSIS;

EID: 70450160659     PISSN: 11782595     EISSN: 11791993     Source Type: Journal    
DOI: 10.1007/BF03256770     Document Type: Note

References (39)

1. European Medicinal Agency. Guidelines on the use of statistical signal detection methods in the EudraVigilance data analysis system [online]. Available from URL: http://eudravigilance.emea.europa.eu/human/docs/10646406en. pdf [Accessed 2009 May 21]
2. US Food and Drug Administration. Guidance for industry: good pharmacovigilance practices and pharmacoepidemiologic assessment [online]. Available from URL: http://www.fda.gov/cder/guidance/6359OCC.pdf [Accessed 2009 May 21]
3. Tubert P, Begaud B, Pere JC, et al. Power and weakness of spontaneous reporting: a probabilistic approach. J Clin Epidemiol 1992; 45: 283-286
4. Evans SJ. Pharmacovigilance: a science or fielding emergencies? Stat Med 2000; 19 (23): 199-209
5. 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: 483-486
6. Auriche M. Bayesian approach to the imputability of undesirable phenomena to drugs. Therapie 1985; 40 (5): 301-306
7. DuMouchel W, Pregibon D. Empirical Bayes screening for multi-item associations. Seventh ACM SigKDD International Conference on Knowledge Discovery and Data Mining; 2001 Aug 26-29; San Francisco (CA)
8. 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): 381-392
9. Bate A, Lindquits M, Edwards IR, et al. A data mining approach for signal detection and analysis. Drug Saf 2002; 25: 393-397
10. Bate A, Lindquits M, Edwards IR, et al. A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol 1998; 54: 315-321
11. Norén GN, Bate A, Orre R, et al. Extending the methods used to screen the WHO drug safety database towards analysis of complex associations and improved accuracy for rare events. Stat Med 2006; 25 (21): 3740-3757
12. Norén GN, Sundberg R, Bate A, et al.A statistical methodology for drug-drug interaction surveillance. Stat Med 2008; 27: 3057-3070
13. Norén GN, Orre R, Bate A, et al. Duplicate detection in adverse drug reaction surveillance. Data Mining Knowledge Discov 2007; 14 (3): 305-328
14. Hauben M, Vegni F, Reich L, et al. Postmarketing hepatic adverse event experience with PEGylated/non-PEGylated drugs: a disproportionality analysis. Eur J Gastroenterol Hepatol 2007; 19 (11): 934-941
15. Evans SJW, Waller P, Davis S. Proportional reporting ratios: the uses of epidemiological methods for signal generation. Pharmacoepidemiol Drug Saf 1998; 7: S102
16. Finney DJ. Systematic signalling of adverse reactions to drugs. Meth Inf Med 1974; 13: 1-10
17. Tubert-Bitter P, Bégaud B, Motide Y, et al. Comparing the toxicity of two drugs in the framework of spontaneous reporting: a confidence interval approach. J Clin Epidemiol 1996; 49: 121-123
18. Van Puijenbroek EP, Bate A, Leufkens HG, et al. A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiol Drug Saf 2002; 11 (1): 3-10
19. Waller PC, Coulson RA, Wood SM. Regulatory pharmacovigilance in the United Kingdom: current principles and practice. Pharmacoepidemiol Drug Saf 1996; 5: 363-375
20. Bégaud B, Tubert P, Haramburu F, et al. Comparing toxicity of drugs: use and misuse of spontaneous reporting. Post Market Surveill 1991; 5 (1): 59-67
21. Van Puijenbroek EP, Dietmont WL, van Grootheest K. Application of quantitative signal detection in the Dutch spontaneous reporting systems for adverse drug reactions. Drug Saf 2003; 26 (5): 293-301
22. Seber GAF. A review of estimating animal abundance. Biometrics 1986; 42: 267-292
23. Chanderasekar C, Deming WE. On a method of estimating birth and death rates and the extent of registration. J Am Stat Assoc 1949; 44: 101-115
24. Wittes JT. Applications of a multinomial capture-recapture model to epidemiological data. J Am Stat 1974; 69: 93-97
25. Wittes JT, Colton T, Sidel VW. Capture-recapture models for assessing the completeness of case ascertainment using multiple information sources. J Chronic Dis 1974; 27: 25-36
26. Hook EB, Regal RR. Capture-recapture methods in epidemiology: methods limitations. Epidemiol Rev 1995; 17 (2): 243-264
27. Evans MA, Bonett DG. A general theory for modeling capture-recapture data from a closed population. Biometrics 1994; 80: 27-38
28. Regal RR, Hook EB. Goodness-of-fit bases confidence intervals for estimation of the size of a closed population. Stat Med 1984; 3: 287-291
29. Huggins RM. On the statistical analysis of capture experiments. Biometrika 1989; 76: 133-140
30. Garthwaite PH, Buckland ST.GeneratingMonte Carlo confidence intervals by Robbins-Monroe process. Applied Stat 1992; 41: 159-171
31. Evans MA, Kim HM, O'Brien TE. An application of profile-likelihood based confidence interval to capture-recapture estimators. J Agri Biol Env Stat 1996; 1: 131-140
32. Cadwell BL, Smith PJ, Baughman AL. Methods for capture-recapture analysis when cases lack personal identifiers. Stat Med 2005; 24: 2041-2051
33. Bishop YMM, Feinberg SE, Holland PW. Discrete multivariate analysis. Cambridge (MA): MIT Press, 1975
34. Smith PJ. Bayesian analyses for a multiple capture-recapture model. Biometrika 1991; 78 (2): 399-407
35. Chavez-Demoulin V. Bayesian inference for small-sample capture-recapture data. Biometrics 1999; 55: 727-731
36. Madigan D, York JC. Bayesian methods for estimation of the size of a closed population. Biometrika 1997; 84: 19-31
37. Bunge J, Fitzpatrick M. Estimating the number of species: a review. J Am Stat Assoc 1993; 88: 364-373
38. Bunge J, Fitzpatrick M, Handley J. Comparison of three estimators of the number of species. J Appl Stat 1995; 22: 45-59
39. Rodrigues J, Milan LA, Leite JG. Hierarchical Bayesian estimation for the number of species. Biometrical J 2001; 43: 737-746