Accounting for multiplicity in the evaluation of "signals" obtained by data mining from spontaneous report adverse event databases

Biometrical Journal

Volumn 49, Issue 1, 2007, Pages 151-165

  Gould, A Lawrence ^a  

^a MERCK RESEARCH LABORATORIES   (United States)

Author keywords

Bayes; Diagnostic; Empirical bayes; Mixture model; Positive FDR; Screening

Indexed keywords

BAYESIAN NETWORKS; DATA MINING; DIAGNOSIS; DRUG PRODUCTS;

ADVERSE EVENTS; BAYE; BAYES METHOD; CLINICAL TRIAL; DIAGNOSTIC; EMPIRICAL BAYES; MIXTURE MODELING; POSITIVE FDR; QUANTITATIVE MEASURES; RANDOM NOISE;

DATABASE SYSTEMS;

EID: 33847337428     PISSN: 03233847     EISSN: 15214036     Source Type: Journal    
DOI: 10.1002/bimj.200610296     Document Type: Conference Paper

References (33)

1. Almenoff, J., Tonning, J. M., Gould, A. L., Szarfman, A., Hauben, M., Ouellet-Hellstrom, R., Ball, R., Hornbuckle, K., Walsh, L., Yee, C., Sacks, S. T., Yuen, N., Patadia, V., Blum, M., Johnston, M., Gerrits, C., Seifert, H., and LaCroix, K. (2005). Perspectives on the use of data mining in pharmacovigilance. Drug Safety 28, 981-1007.
2. Bate, A., Lindquist, M., Edwards, I. R., Olsson, S., Orre, R., Lansner, A., and De Freitas, R. M. (1998). A Bayesian neural network method for adverse drug reaction signal generation. European Journal of Clinical Pharmacology 54, 315-321.
3. Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B: Methodological 57, 289-300.
4. Casella, G., and Berger, R. L. (1987). Reconciling Bayesian and frequentist evidence in the one-sided testing problem. Journal of the American Statistical Association 82, 106-111.
5. Chan, K. A. and Hauben, M. (2005). Signal detection in pharmacovigilance: empirical evaluation of data mining tools. Pharmacoepidemiology and Drug Safety 14, 597-599.
6. Clark, J. A., Klincewicz, S. L., and Stang, P. E. (2001). Spontaneous adverse event signaling methods: Classification and use with health care treatment products. Epidemiologic Reviews 23, 191-210.
7. Dabney, A. and Storey, J. D. (2004). The qvalue package, http://cran.r-project.org/doc/packages/qvalue.pdf
8. Dass, S. C. and Berger, J. O. (2003). Unified conditional frequentist and Bayesian testing of composite hypotheses. Scandinavian Journal of Statistics 30, 193-210.
9. Datta, G. S. and Ghosh, J. K. (1995). On priors providing frequentist validity for bayesian-inference. Biometrika 82, 37-45.
10. DuMouchel, W. (1999). Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system (Disc: p. 190-202). The American Statistician 53, 177-190.
11. Eberly, L. E. and Casella, G. (2003). Estimating Bayesian credible intervals. Journal of Statistical Planning and Inference 112, 115-132.
12. Efron, B. (2004). Large-scale simultaneous hypothesis testing: The choice of a null hypothesis. Journal of the American Statistical Association 99, 96-104.
13. Evans, M. (1997). Bayesian inference procedures derived via the concept of relative surprise. Communications in Statistics-Theory and Methods 26, 1125-1143.
14. Finney, D. J. (1974). Systematic signalling of adverse reactions to drugs. Methods of Information in Medicine 13, 10.
15. Finney, D. J. (1982). The detection of adverse reactions to therapeutic drugs. Statistics in Medicine 1, 153-161.
16. Goodman, S. N. (1999). Toward evidence-based medical statistics. 2: The Bayes factor. Annals of Internal Medicine 130, 1005-1013.
17. Gould, A. L. (2003). Practical pharmacovigilance analysis strategies. Pharmacoepidemiology and Drug Safety 12, 559-574.
18. Gould, A. L. (2006). A Bayesian screening procedure for identifying 'signals' obtained by data mining, with application to spontaneous report adverse event databases, (unpublished ms)
19. Hauben, M., Patadia, V., Gerrits, C., Walsh, L., and Reich, L. (2005). Data mining in pharmacovigilance - The need for a balanced perspective. Drug Safety 28, 835-842.
20. Hauben, M. and Reich, L. (2005). Potential utility of data-mining algorithms for early detection of potentially fatal/disabling adverse drug reactions: A retrospective evaluation. Journal of Clinical Pharmacology 45, 378-384.
21. Lindquist, M., Stahl, M., Bate, A., Edwards, I. R., and Meyboom, R. H. B. (2000). A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO International Database. Drug Safety 23, 533-542.
22. Nicolaou, A. (1993). Bayesian intervals with good freauentist behaviour in the presence of nuisance parameters. Journal of the Royal Statistical Society, Series B: Methodological 55, 377-390.
23. O'Neill, R. T. and Szarfman, A. (2001). Some US Food and Drug Administration perspectives on data mining for pediatric safety assessment. Current Therapeutic Research-Clinical and Experimental 62, 650-663.
24. Royall, B. W. (1971). International aspects of the study of adverse reactions to drugs. Biometrics 27, 689-698.
25. Scott, J. G. and Berger, J. O. (2006). An exploration of aspects of Bayesian multiple testing. Journal of Statistical Planning and Inference 136, 2144-2162.
26. Severini, T. A. (1991). On the relationship between Bayesian and non-Bayesian interval estimates. Journal of the Royal Statistical Society, Series B: Methodological 51, 611-618.
27. Storey, J. D. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society, Series B: Statistical Methodology 64, 479-498.
28. Storey, J. D. (2003). The positive false discovery rate: A Bayesian interpretation and the q-value. The Annals of Statistics 31, 2013-2035.
29. Storey, J. D., Taylor, J. E., and Siegmund, D. (2004). Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach. Journal of the Royal Statistical Society, Series B: Statistical Methodology 66, 187-205.
30. Sweeting, T. J. (1995). A framework for bayesian and likelihood approximations in statistics. Biometrika 82, 1-23.
31. Sweeting, T. J. (2001). Coverage probability bias, objective Bayes and the likelihood principle. Biometrika 88, 657-675.
32. van Puijenbroek, E. P., Bate, A., Leufkens, H. G. M., Lindquist, M., Orre, R., and Egberts, A. C. G. (2002). A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiology and Drug Safety 11, 3-10.
33. Walley, P. (2002). Reconciling frequentist properties with the likelihood principle. Journal of Statistical Planning and Inference 105, 35-65.