Evaluation of Disproportionality Safety Signaling Applied to Healthcare Databases
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To evaluate the performance of a disproportionality design, commonly used for analysis of spontaneous reports data such as the FDA Adverse Event Reporting System database, as a potential analytical method for an adverse drug reaction risk identification system using healthcare data.
We tested the disproportionality design in 5 real observational healthcare databases and 6 simulated datasets, retrospectively studying the predictive accuracy of the method when applied to a collection of 165 positive controls and 234 negative controls across 4 outcomes: acute liver injury, acute myocardial infarction, acute kidney injury, and upper gastrointestinal bleeding.
We estimate how well the method can be expected to identify true effects and discriminate from false findings and explore the statistical properties of the estimates the design generates. The primary measure was the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.
For each combination of 4 outcomes and 5 databases, 48 versions of disproportionality analysis (DPA) were carried out and the AUC computed. The majority of the AUC values were in the range of 0.35 < AUC < 0.6, which is considered to be poor predictive accuracy, since the value AUC = 0.5 would be expected from mere random assignment. Several DPA versions achieved AUC of about 0.7 for the outcome Acute Renal Failure within the GE database. The overall highest DPA version across all 20 outcome-database combinations was the Bayesian Information Component method with no stratification by age and gender, using first occurrence of outcome and with assumed time-at-risk equal to duration of exposure + 30d, but none were uniformly optimal. The relative risk estimates for the negative control drug-event combinations were very often biased either upward or downward by a factor of 2 or more. Coverage probabilities of confidence intervals from all methods were far below nominal.
The disproportionality methods that we evaluated did not discriminate true positives from true negatives using healthcare data as they seem to do using spontaneous report data.
KeywordsSitagliptin Coverage Probability Acute Liver Injury Proportional Reporting Ratio True Effect Size
The Observational Medical Outcomes Partnership is funded by the Foundation for the National Institutes of Health (FNIH) through generous contributions from the following: Abbott, Amgen Inc., AstraZeneca, Bayer Healthcare Pharmaceuticals, Inc., Biogen Idec, Bristol-Myers Squibb, Eli Lilly & Company, GlaxoSmithKline, Janssen Research and Development, Lundbeck, Inc., Merck & Co., Inc., Novartis Pharmaceuticals Corporation, Pfizer Inc, Pharmaceutical Research Manufacturers of America (PhRMA), Roche, Sanofi-aventis, Schering-Plough Corporation, and Takeda. Dr. DuMouchel is an employee of Oracle Health Sciences. Drs. Ryan and Schuemie are employees of Janssen Research and Development. Dr. Schuemie received a fellowship from the Office of Medical Policy, Center for Drug Evaluation and Research, Food and Drug Administration. Drs. Schuemie and Madigan have received grants from FNIH.
This article was published in a supplement sponsored by the Foundation for the National Institutes of Health (FNIH). The supplement was guest edited by Stephen J.W. Evans. It was peer reviewed by Olaf H. Klungel who received a small honorarium to cover out-of-pocket expenses. S.J.W.E has received travel funding from the FNIH to travel to the OMOP symposium and received a fee from FNIH for the review of a protocol for OMOP. O.H.K has received funding for the IMI-PROTECT project.from the Innovative Medicines Initiative Joint Undertaking (http://www.imi.europa.eu) under Grant Agreement no 115004, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution.
- 3.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; 2001. p. 67–76.Google Scholar
- 4.Dumouchel W. Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system. Am Stat. 1999;53(3):177–90.Google Scholar
- 9.Zorych I, Madigan D, Ryan P, Bate A. Disproportionality methods for pharmacovigilance in longitudinal observational databases. Stat Methods Med Res. 2013;22(1):39–56.Google Scholar
- 12.Ryan PB, Schuemie M. Evaluating performance of risk identification methods through a large-scale simulation of observational data. Drug Saf. 2013 (in this supplement issue). doi: 10.1007/s40264-013-0110-2.
- 13.Overhage JM, Ryan PB, Schuemie MJ, Stang PE. Desideratum for evidence based epidemiology. Drug Saf. 2013 (in this supplement issue). doi: 10.1007/s40264-013-0102-2.
- 14.Hartzema AG, Reich CG, Ryan PB, Stang PE, Madigan D, Welebob E, et al. Managing data quality for a drug safety surveillance system. Drug Saf. 2013 (in this supplement issue). doi: 10.1007/s40264-013-0098-7.
- 16.Fram DM, Almenoff JS, DuMouchel W. Empirical Bayesian data mining for discovering patterns in post-marketing drug safety. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, DC: ACM; 2003. p. 359–68.Google Scholar
- 22.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.Google Scholar