Data Mining Techniques in Pharmacovigilance: Analysis of the Publicly Accessible FDA Adverse Event Reporting System (AERS) 267 tools makes them valuable sources for data mining aimed to address clinical pharmacology questions, both in terms of new effects of drugs (especially on primary endpoints, to confirm premarketing evidence) and of assessment of appropriate drug use (closer to the main purpose of the registries).  Claim databases originate for administrative purposes: for instance, in

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... y European countries, health care costs are provided by the National Health Service to Local Health Authorities on the basis of the costs of the medical interventions provided to citizens. Claim databases include all data useful to this purpose (e.g., diagnoses of hospital admissions, reimbursed prescriptions of drugs, diagnostic procedures in ambulatory care) and, as a secondary aim, they represent an important source for epidemiological questions. These data can be equally useful for both aims (1 and 2 above), provided that their intrinsic limitations are duly acknowledged, in particular it should be recognized that information on outcomes are not strictly related to drug use (namely, adverse drug reactions) and patho-physiological plausibility supporting drug-reaction associations should be more stringently verified. complements disproportionality; and (5) the most appropriate selection of pharmacovigilance tools needs to be tailored to each situation, being mindful of the numerous biases and confounders that may influence performance and incremental utility of DMAs. We support the implementation of DMAs, although one should not automatically assume that greater complexity is synonymous of greater precision and accuracy. Overall, data derived from DMAs should be considered with caution and guided by appropriate clinical evaluation. This clinical perspective should always be considered to support really appropriate drug use, balancing drug effectiveness, safety and, above all, actual patients' needs.