Performance of subgrouped proportional reporting ratios in the US Food and Drug Administration (FDA) adverse event reporting system

Daniel G Dauner, Rui Zhang, Terrence J Adam, Eleazar Leal, Viviene Heitlage, Joel F Farley

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: Many signal detection algorithms give the same weight to information from all products and patients, which may result in signals being masked or false positives being flagged as potential signals. Subgrouped analysis can be used to help correct for this. Research design and methods: The publicly available US Food and Drug Administration Adverse Event Reporting System quarterly data extract files from 1 January 2015 through 30 September 2017 were utilized. A proportional reporting ratio (PRR) analysis subgrouped by either age, sex, ADE report type, seriousness of ADE, or reporter was compared to the crude PRR analysis using sensitivity, specificity, precision, and c-statistic. Results: Subgrouping by age (n = 78, 34.5% increase), sex (n = 67, 15.5% increase), and reporter (n = 64, 10.3% increase) identified more signals than the crude analysis. Subgrouping by either age or sex increased both the sensitivity and precision. Subgrouping by report type or seriousness resulted in fewer signals (n = 50, −13.8% for both). Subgrouped analyses had higher c-statistic values, with age having the highest (0.468). Conclusions: Subgrouping by either age or sex produced more signals with higher sensitivity and precision than the crude PRR analysis. Subgrouping by these variables can unmask potentially important associations.

Original languageEnglish (US)
Pages (from-to)589-597
Number of pages9
JournalExpert Opinion on Drug Safety
Volume22
Issue number7
DOIs
StatePublished - 2023

Bibliographical note

Funding Information:
JF Farley reports receiving personal fees from Takeda for expert witness testimony and grant support from AstraZeneca to the University of Minnesota for an unrelated research project. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Publisher Copyright:
© 2023 Informa UK Limited, trading as Taylor & Francis Group.

Keywords

  • Proportional reporting ratio
  • adverse drug events
  • pharmacovigilance
  • signal detection
  • subgrouping

PubMed: MeSH publication types

  • Journal Article

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