An Algorithm to Identify Generic Drugs in the FDA Adverse Event Reporting System

An Algorithm to Identify Generic Drugs in the FDA Adverse Event Reporting System Drug Saf (2017) 40:799–808 DOI 10.1007/s40264-017-0550-1 OR IGINAL RESEARCH ARTIC L E An Algorithm to Identify Generic Drugs in the FDA Adverse Event Reporting System 1 2,3 2,4,5 6 • • • Geetha Iyer Sathiya Priya Marimuthu Jodi B. Segal Sonal Singh Published online: 7 June 2017 Springer International Publishing Switzerland 2017 Abstract number/abbreviated NDA (ANDA), and specific use of the Introduction Although generic drugs constitute approxi- term ‘generic’ or ‘brand’ to classify the focal drug of each mately 88% of drugs prescribed in the US, there are no case report as definitely generic (two of three criteria), reliable methods to identify generic drugs in the US FDA probably generic (one of three criteria), brand, and cannot Adverse Event Reporting System (FAERS). be assessed. Inter-rater reliability was estimated using Objective The aim of this study was to develop an algo- kappa coefficients, and internal consistency was estimated rithm for identifying generic drugs in the FAERS. using Cronbach’s alpha. We compared the classification of Data Source We used 1237 adverse event reports for the drugs as generic versus non-generic in publicly avail- tamsulosin, levothyroxine, and amphetamine/dextroam- able FAERS compared with the original case reports phetamine from the publicly available FAERS Drug Safety Springer Journals

An Algorithm to Identify Generic Drugs in the FDA Adverse Event Reporting System

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Springer International Publishing
Copyright © 2017 by Springer International Publishing Switzerland
Medicine & Public Health; Drug Safety and Pharmacovigilance; Pharmacology/Toxicology
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