Abstract
The US Food and Drug Administration (FDA) recently published a warning regarding pancreatitis in association with the use of exenatide, an incretin mimetic used for the treatment of patients with diabetes mellitus. We note that this safety issue is not associated with a signal of disproportionate reporting (SDR) in the FDA Adverse Event Reporting System (AERS) database or the World Health Organization (Uppsala Monitoring Centre) Vigibase for any of four data-mining algorithms we tested (proportional reporting ratio, the multi-item gamma-Poisson shrinker, an urn model and the Bayesian Confidence Propagation Neural Network). Exenatide and acute pancreatitis may thus represent a ‘false-negative’ result for disproportionality-based data-mining methodology generally. We evaluate the possibility that this lack of an SDR is caused by the phenomenon known as ‘masking’ (or ‘cloaking’) and reject this hypothesis. While positive findings are understandably more exciting, we discuss why publishing negative findings, such as in this example, is important for placing the capabilities and limitations of drug safety data mining into proper perspective.
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Acknowledgements
Manfred Hauben and Alan Hochberg are both responsible for the design and conduct of this study; collection, management, analysis and interpretation of the data; and preparation, review and approval of the manuscript. We thank Steph Reisinger for comments on the manuscript, and we thank the reviewers for further constructive questions and comments.
Alan Hochberg is an employee of ProSanos Corporation, a vendor of drug-safety data-mining software and services related to methodology discussed in this paper. Manfred Hauben is an employee of Pfizer Inc., a company that manufacturers and markets drugs in the same therapeutic class as exenatide.
Manfred Hauben discloses that he owns stock options and/or stock in Pfizer and other pharmaceutical companies that may manufacture drugs in the same class as those discussed in this article.
No specific funding was received for the conduct of this study or the preparation of this paper.
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Hauben, M., Hochberg, A. The Importance of Reporting Negative Findings in Data Mining. Pharm Med 22, 215–219 (2008). https://doi.org/10.1007/BF03256706
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DOI: https://doi.org/10.1007/BF03256706