Data mining in pharmacovigilance: lessons from phantom ships

Abstract

Pharmacovigilance experts devote considerable effort to post-marketing surveillance of adverse drug reactions (ADRs). Although the prepared mind of the pharmacovigilance expert remains the cornerstone of this process [1], statistical algorithms, also known as data mining algorithms (DMAs), are being promoted as supplementary tools for safety reviewers. Opinions vary on their utility and optimum deployment mainly because their use has not been completely validated for various reasons, including a lack of consensus on gold standards for causality. True positive associations may be inherently more interesting, but constructing reference sets for validation also require identification of “true negatives” for measuring performance of DMAs. Occasionally, drug-event associations (DEAs), originally considered credible based on traditional pharmacovigilance monitoring, are discounted with various levels of certitude after further investigation. We refer to these DEAs “phantom ships” [2]. Phantom associations may be discounted through epidemiological evidence, careful clinical analysis of the individual cases, and/or based on fundamental clinical pharmacological principles [3–9].