The Importance of Reporting Negative Findings in Data Mining

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.