Literature DB >> 28933038

Methodological Considerations for Comparison of Brand Versus Generic Versus Authorized Generic Adverse Event Reports in the US Food and Drug Administration Adverse Event Reporting System (FAERS).

Md Motiur Rahman1, Yasser Alatawi1, Ning Cheng1, Jingjing Qian1, Peggy L Peissig2, Richard L Berg2, David C Page3, Richard A Hansen4.   

Abstract

BACKGROUND: The US Food and Drug Administration Adverse Event Reporting System (FAERS), a post-marketing safety database, can be used to differentiate brand versus generic safety signals.
OBJECTIVE: To explore the methods for identifying and analyzing brand versus generic adverse event (AE) reports.
METHODS: Public release FAERS data from January 2004 to March 2015 were analyzed using alendronate and carbamazepine as examples. Reports were classified as brand, generic, and authorized generic (AG). Disproportionality analyses compared reporting odds ratios (RORs) of selected known labeled serious adverse events stratifying by brand, generic, and AG. The homogeneity of these RORs was compared using the Breslow-Day test. The AG versus generic was the primary focus since the AG is identical to brand but marketed as a generic, therefore minimizing generic perception bias. Sensitivity analyses explored how methodological approach influenced results.
RESULTS: Based on 17,521 US event reports involving alendronate and 3733 US event reports involving carbamazepine (immediate and extended release), no consistently significant differences were observed across RORs for the AGs versus generics. Similar results were obtained when comparing reporting patterns over all time and just after generic entry. The most restrictive approach for classifying AE reports yielded smaller report counts but similar results.
CONCLUSION: Differentiation of FAERS reports as brand versus generic requires careful attention to risk of product misclassification, but the relative stability of findings across varying assumptions supports the utility of these approaches for potential signal detection.

Entities:  

Mesh:

Substances:

Year:  2017        PMID: 28933038      PMCID: PMC5842081          DOI: 10.1007/s40261-017-0574-4

Source DB:  PubMed          Journal:  Clin Drug Investig        ISSN: 1173-2563            Impact factor:   2.859


  16 in total

1.  Use of measures of disproportionality in pharmacovigilance: three Dutch examples.

Authors:  Antoine C G Egberts; Ronald H B Meyboom; Eugène P van Puijenbroek
Journal:  Drug Saf       Date:  2002       Impact factor: 5.606

2.  Adverse drug event monitoring at the Food and Drug Administration.

Authors:  Syed Rizwanuddin Ahmad
Journal:  J Gen Intern Med       Date:  2003-01       Impact factor: 5.128

Review 3.  Perspectives on the use of data mining in pharmaco-vigilance.

Authors:  June Almenoff; Joseph M Tonning; A Lawrence Gould; Ana Szarfman; Manfred Hauben; Rita Ouellet-Hellstrom; Robert Ball; Ken Hornbuckle; Louisa Walsh; Chuen Yee; Susan T Sacks; Nancy Yuen; Vaishali Patadia; Michael Blum; Mike Johnston; Charles Gerrits; Harry Seifert; Karol Lacroix
Journal:  Drug Saf       Date:  2005       Impact factor: 5.606

4.  Efficacy and tolerability of a generic and a branded formulation of atorvastatin 20 mg/d in hypercholesterolemic Korean adults at high risk for cardiovascular disease: a multicenter, prospective, randomized, double-blind, double-dummy clinical trial.

Authors:  Sang-Hyun Kim; Kyungil Park; Soon-Joon Hong; Young-Seok Cho; Ji-Dong Sung; Geon-Woong Moon; Myung-Ho Yoon; Moo-Yong Lee; Min-Su Hyon; Dong-Woon Kim; Hyo-Soo Kim
Journal:  Clin Ther       Date:  2010-10       Impact factor: 3.393

5.  Prevalence and Predictors of Generic Drug Skepticism Among Physicians: Results of a National Survey.

Authors:  Aaron S Kesselheim; Joshua J Gagne; Wesley Eddings; Jessica M Franklin; Kathryn M Ross; Lisa A Fulchino; Eric G Campbell
Journal:  JAMA Intern Med       Date:  2016-06-01       Impact factor: 21.873

6.  Clinical and pharmacy utilization outcomes with brand to generic antiepileptic switches in patients with epilepsy.

Authors:  Sara C Erickson; Lisa Le; Scott D Ramsey; Brian K Solow; Armen Zakharyan; Karen M Stockl; Ann S M Harada; Bradford Curtis
Journal:  Epilepsia       Date:  2011-06-21       Impact factor: 5.864

7.  Comparison of brand versus generic antiepileptic drug adverse event reporting rates in the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS).

Authors:  Md Motiur Rahman; Yasser Alatawi; Ning Cheng; Jingjing Qian; Annya V Plotkina; Peggy L Peissig; Richard L Berg; David Page; Richard A Hansen
Journal:  Epilepsy Res       Date:  2017-06-13       Impact factor: 3.045

Review 8.  The bioequivalence and therapeutic efficacy of generic versus brand-name psychoactive drugs.

Authors:  Giuseppe Borgheini
Journal:  Clin Ther       Date:  2003-06       Impact factor: 3.393

Review 9.  Data mining of the public version of the FDA Adverse Event Reporting System.

Authors:  Toshiyuki Sakaeda; Akiko Tamon; Kaori Kadoyama; Yasushi Okuno
Journal:  Int J Med Sci       Date:  2013-04-25       Impact factor: 3.738

10.  Off-patent generic medicines vs. off-patent brand medicines for six reference drugs: a retrospective claims data study from five local healthcare units in the Lombardy Region of Italy.

Authors:  Giorgio L Colombo; Enrico Agabiti-Rosei; Alberto Margonato; Claudio Mencacci; Carlo Maurizio Montecucco; Roberto Trevisan
Journal:  PLoS One       Date:  2013-12-18       Impact factor: 3.240

View more
  4 in total

1.  Safety of Marketed Cancer Supportive Care Biosimilars in the US: A Disproportionality Analysis Using the Food and Drug Administration Adverse Event Reporting System (FAERS) Database.

Authors:  Kaniz Afroz Tanni; Cong Bang Truong; Sura Almahasis; Jingjing Qian
Journal:  BioDrugs       Date:  2021-01-13       Impact factor: 5.807

2.  Analyzing adverse drug reaction using statistical and machine learning methods: A systematic review.

Authors:  Hae Reong Kim; MinDong Sung; Ji Ae Park; Kyeongseob Jeong; Ho Heon Kim; Suehyun Lee; Yu Rang Park
Journal:  Medicine (Baltimore)       Date:  2022-06-24       Impact factor: 1.817

3.  Adverse event profiles of solvent-based and nanoparticle albumin-bound paclitaxel formulations using the Food and Drug Administration Adverse Event Reporting System.

Authors:  Misa Naganuma; Kohei Tahara; Shiori Hasegawa; Akiho Fukuda; Sayaka Sasaoka; Haruna Hatahira; Yumi Motooka; Satoshi Nakao; Ririka Mukai; Kouseki Hirade; Tomoaki Yoshimura; Takeshi Kato; Hirofumi Takeuchi; Mitsuhiro Nakamura
Journal:  SAGE Open Med       Date:  2019-03-11

4.  Detecting and Filtering Immune-Related Adverse Events Signal Based on Text Mining and Observational Health Data Sciences and Informatics Common Data Model: Framework Development Study.

Authors:  Yue Yu; Kathryn Ruddy; Aaron Mansfield; Nansu Zong; Andrew Wen; Shintaro Tsuji; Ming Huang; Hongfang Liu; Nilay Shah; Guoqian Jiang
Journal:  JMIR Med Inform       Date:  2020-06-12
  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.