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Clinical Drug Investigation

, Volume 37, Issue 12, pp 1143–1152 | Cite as

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 Rahman
  • Yasser Alatawi
  • Ning Cheng
  • Jingjing Qian
  • Peggy L. Peissig
  • Richard L. Berg
  • David C. Page
  • Richard A. Hansen
Original Research Article

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.

Notes

Acknowledgements

The authors thank Wenlei Jiang, PhD (FDA) and Saranrat Wittayanukorn, PhD (FDA) for their thoughtful contributions to the study design and data analysis.

Funding for this work was made possible by the FDA through grant 1U01FD005272. Views expressed do not necessarily reflect the official policies of the Department of Health and Human Services, nor does any mention of trade names, commercial practices, or organization imply endorsement by the US Government.

Compliance with Ethical Standards

Funding

This study was funded by the US Food and Drug Administration through Grant 1U01FD005272.

Conflict of interest

In the past 3 years, Richard Hansen has provided expert testimony for Boehringer Ingelheim and Daiichi Sankyo. No other authors declare a potential conflict of interest. The sponsor of this study (FDA) has provided suggestions for study design, interpretation of the results and development of the manuscripts. However, the ultimate decisions came from all the authors. Views expressed do not necessarily reflect the official policies of the Department of Health and Human Services; nor does any mention of trade names, commercial practices, or organization imply endorsement by the United States Government.

Supplementary material

40261_2017_574_MOESM1_ESM.docx (30 kb)
Supplementary material 1 (DOCX 30 kb)

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Md. Motiur Rahman
    • 1
  • Yasser Alatawi
    • 1
  • Ning Cheng
    • 1
  • Jingjing Qian
    • 1
  • Peggy L. Peissig
    • 2
  • Richard L. Berg
    • 2
  • David C. Page
    • 3
  • Richard A. Hansen
    • 1
  1. 1.Department of Health Outcomes Research and Policy, Harrison School of PharmacyAuburn UniversityAuburnUSA
  2. 2.Biomedical Informatics Research CenterMarshfield Clinic Research FoundationMarshfieldUSA
  3. 3.Department of Computer Science, Department of Biostatistics and Medical Informatics, School of Medicine and Public HealthUniversity of WisconsinMadisonUSA

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