Perspectives on the Use of Data Mining in Pharmacovigilance

Abstract

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.

Signal Management of Disproportionate Reporting in Moroccan Pharmacovigilance: The Lower Limb Edema Induced by Anti-Tuberculosis Drugs

“…Only some considerations on the evaluation of the performance of different methods have been discussed [12,13].…”

Quantitative signal detection for vaccines: Effects of stratification, background and masking on GlaxoSmithKline’s spontaneous reports database

“…13 Our study did not aim to validate whether MGPS disproportionality analysis is better or worse than other methodologies used in drug/vaccine safety. Our study intended to elucidate the importance and contribution of several settings within the MGPS methodology.…”

“…Recent articles researched the impact of stratification in spontaneous reports databases, in particular the WHO 11 and the Vaccine Adverse Event Reporting System VAERS. 12 Almenoff et al 13 recommend that the decision of whether to use disproportionality analyses to supplement traditional safety signal detection methods should be evaluated by individual institutions. If and when such a decision is made, however, a number of choices need to be made with respect to the parameters that may determine the outcome of the analyses.…”

Mining Unexpected Temporal Associations: Applications in Detecting Adverse Drug Reactions

“…In the medical domain, current postmarket ADR signaling techniques like proportional reporting ratios (PRRs) [9], multiitem Gamma Poisson Shrinker (MGPS) [14], and Bayesian confidence propagation neural network (BCPNN) [33] perform fruitfully on spontaneous ADR case reports [9]. Each ADR case report describes the suspected causality between drugs and conditions for one patient.…”