A Review of Statistical Methods for Safety Surveillance

The data-mining statistical methods used for disproportionality analysis of drug–adverse event combinations from large drug safety databases such as the FDA’s Adverse Event Reporting System (FAERS), consisting of spontaneous reports on adverse events for postmarket drugs, are called passive surveill...

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Veröffentlicht in:Therapeutic innovation & regulatory science 2014-01, Vol.48 (1), p.98-108
Hauptverfasser: Huang, Lan, Guo, Ted, Zalkikar, Jyoti N., Tiwari, Ram C.
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container_title Therapeutic innovation & regulatory science
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creator Huang, Lan
Guo, Ted
Zalkikar, Jyoti N.
Tiwari, Ram C.
description The data-mining statistical methods used for disproportionality analysis of drug–adverse event combinations from large drug safety databases such as the FDA’s Adverse Event Reporting System (FAERS), consisting of spontaneous reports on adverse events for postmarket drugs, are called passive surveillance methods. However, the statistical signal detection methods for longitudinal data, as the data accrue in time, are called active surveillance methods. A review of the most commonly used passive surveillance statistical methods and the relationships among them is presented with unified notations. These methods are applied to the 2006-2012 FAERS data; the number of drug signals of disproportionate rates (SDRs) detected by each of these methods with the common SDRs from all of these methods, for the adverse event myocardial infarction, are given. Finally, there is a brief discussion on the recently developed active surveillance methods.
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subjects Drug Safety and Pharmacovigilance
Neural networks
Pharmaceutical industry
Pharmacotherapy
Pharmacy
Probability
Product safety
Public private partnerships
Special Issue on Statistics
title A Review of Statistical Methods for Safety Surveillance
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