Incidental events in spontaneous reports -- a proposal for filtering 'noise'.
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Adverse Drug Reaction Reporting Systems↗
Biomedical subjects
Louisa Walsh
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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.
Adverse Drug Reaction Reporting Systems↗
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.
Adverse Drug Reaction Reporting Systems↗
Perspectives on the use of data mining in pharmaco-vigilance.
In the last 5 years, regulatory agencies and drug monitoring centres have been developing computerised data-mining methods to better identify reporting relationships in spontaneous reporting databases that could signal possible adverse drug reactions. At present, there are no guidelines or standards for the use of these methods in routine pharmaco-vigilance. In 2003, a group of statisticians, pharmaco-epidemiologists and pharmaco-vigilance professionals from the pharmaceutical industry and the US FDA formed the Pharmaceutical Research and Manufacturers of America-FDA Collaborative Working Group on Safety Evaluation Tools to review best practices for the use of these methods.In this paper, we provide an overview of: (i) the statistical and operational attributes of several currently used methods and their strengths and limitations; (ii) information about the characteristics of various postmarketing safety databases with which these tools can be deployed; (iii) analytical considerations for using safety data-mining methods and interpreting the results; and (iv) points to consider in integration of safety data mining with traditional pharmaco-vigilance methods. Perspectives from both the FDA and the industry are provided. Data mining is a potentially useful adjunct to traditional pharmaco-vigilance methods. The results of data mining should be viewed as hypothesis generating and should be evaluated in the context of other relevant data. The availability of a publicly accessible global safety database, which is updated on a frequent basis, would further enhance detection and communication about safety issues.
Adverse Drug Reaction Reporting Systems↗