Data mining in pharmacovigilance: the need for a balanced perspective.
Data mining is receiving considerable attention as a tool for pharmacovigilance and is generating many perspectives on its uses. This paper presents four concepts that have appeared in various professional venues and represent potential sources of misunderstanding and/or entail extended discussions: (i) data mining algorithms are unvalidated; (ii) data mining algorithms allow data miners to objectively screen spontaneous report data; (iii) mathematically more complex Bayesian algorithms are superior to frequentist algorithms; and (iv) data mining algorithms are not just for hypothesis generation. Key points for a balanced perspective are that: (i) validation exercises have been done but lack a gold standard for comparison and are complicated by numerous nuances and pitfalls in the deployment of data mining algorithms. Their performance is likely to be highly situation dependent; (ii) the subjective nature of data mining is often underappreciated; (iii) simpler data mining models can be supplemented with 'clinical shrinkage', preserving sensitivity; and (iv) applications of data mining beyond hypothesis generation are risky, given the limitations of the data. These extended applications tend to 'creep', not pounce, into the public domain, leading to potential overconfidence in their results. Most importantly, in the enthusiasm generated by the promise of data mining tools, users must keep in mind the limitations of the data and the importance of clinical judgment and context, regardless of statistical arithmetic. In conclusion, we agree that contemporary data mining algorithms are promising additions to the pharmacovigilance toolkit, but the level of verification required should be commensurate with the nature and extent of the claimed applications.
Hauben
Manfred
M
Risk Management Strategy, Pfizer Inc, New York, New York 10017, USA.
Patadia
Vaishali
V
Gerrits
Charles
C
Walsh
Louisa
L
Reich
Lester
L
D016907:Adverse Drug Reaction Reporting Systems; D000465:Algorithms; D001499:Bayes Theorem; D003625:Data Collection / Q000379:methods*; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D012449:Safety