The role of data mining in pharmacovigilance.
A principle concern of pharmacovigilance is the timely detection of adverse drug reactions that are novel by virtue of their clinical nature, severity and/or frequency. The cornerstone of this process is the scientific acumen of the pharmacovigilance domain expert. There is understandably an interest in developing database screening tools to assist human reviewers in identifying associations worthy of further investigation (i.e., signals) embedded within a database consisting largely of background 'noise' containing reports of no substantial public health significance. Data mining algorithms are, therefore, being developed, tested and/or used by health authorities, pharmaceutical companies and academic researchers. After a focused review of postapproval drug safety signal detection, the authors explain how the currently used algorithms work and address key questions related to their validation, comparative performance, deployment in naturalistic pharmacovigilance settings, limitations and potential for misuse. Suggestions for further research and development are offered.
Hauben
Manfred
M
Pfizer, Inc., Risk Management Strategy, New York, NY, USA.
Madigan
David
D
Gerrits
Charles M
CM
Walsh
Louisa
L
Van Puijenbroek
Eugene P
EP
D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D000465:Algorithms; D016208:Databases, Factual; D006801:Humans; D016247:Information Storage and Retrieval; D011159:Population Surveillance; D011634:Public Health