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

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.

KEYWORDS:

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

Acknowledgments

This research was presented as a poster at the International Conference on Pharmacoepidemiology and Therapeutic Risk Management in Copenhagen, Denmark on 28 August 2022.

Declaration of interests

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.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

DG Dauner designed the study, conducted the statistical analysis, and drafted the manuscript. V Heitlage contributed to development of the reference set, interpretation of results, and review and editing of the manuscript. JF Farley, R Zhang, TJ Adam, and E Leal contributed to the conception of the research project, interpretation of results, and review and editing of the manuscript. All authors approved the final version of the manuscript.

Ethics

This study used a secondary data source that is publicly available, and ethical approval was not needed.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14740338.2023.2182289

Additional information

Funding

This paper was not funded.