The detection of unknown and unexpected connections between drug exposure and adverse events is one of the major challenges of pharmacovigilance. For the identification of possible connections in large databases, automated statistical systems have been introduced with promising results. From the large numbers of associations so produced, the human mind has to identify signals that are likely to be important, in need of further assessment and follow-up and that may require regulatory action. Such decisions are based on a variety of clinical, epidemiological, pharmacological and regulatory criteria. Likewise, there are a number of criteria that underlie the subsequent evaluation of such signals. A good understanding of the logic underlying these processes fosters rational pharmacovigilance and efficient drug regulation. In the future a combination of quantitative and qualitative criteria may be incorporated in automated signal detection.
Agranulocytosis Erythema Multiforme Proportional Reporting Ratio Individual Case Report Pharmacoepidemiological Study
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We gratefully acknowledge the valuable discussions regarding signal detection during recent meetings of the Uppsala Monitoring Centre Signal Review Panel.
Olsson S. The role of the WHO programme on international drug monitoring in coordinating worldwide drug safety efforts. Drug Saf 1998; 19: 1–10PubMedCrossRefGoogle Scholar
Bate A, Lindquist M, Edwards IR, et al. A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol 1998; 54: 315–21PubMedCrossRefGoogle Scholar
Meyboom RHB, Egberts ACG, Edwards IR, et al. Principles of signal detection in pharmacovigilance. Drug Saf 1997; 16: 355–65PubMedCrossRefGoogle Scholar
Bate A, Lindquist M, Orre R, et al. Data mining analyses of pharmacovigilance signals in relation to relevant comparison drugs. Eur J Clin Pharmacol. In pressGoogle Scholar
Egberts ACG, Meyboom RHB, De Koning FHP, et al. Non-puerperal lactation associated with antidepressant drug use. Br J Clin Pharmacol 1997; 44: 277–81PubMedCrossRefGoogle Scholar
Natsch S, Vinks MHAM, Voogt AK, et al. Anaphylactic reactions to proton-pump inhibitors. Ann Pharmacother 2000; 34: 474–6PubMedCrossRefGoogle Scholar
Lindquist M, Ståhl M, Bate A, et al. A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database. Drug Saf 2000; 23: 533–42PubMedCrossRefGoogle Scholar
Meyboom RHB, Egberts ACG, Gribnau FWJ, et al. Causal or casual?. The role of causality assessment in pharmacovigilance. Drug Saf 1997; 16: 374–89CrossRefGoogle Scholar
Meyboom RHB, Hekster YA, Egberts ACG, et al. Pharmacovigilance in perspective. Drug Saf 1997; 21: 429–47CrossRefGoogle Scholar