Identifying Safety Subgroups at Risk: Assessing the Agreement Between Statistical Alerting and Patient Subgroup Risk

Olivia Mahaux (), Greg Powell, François Haguinet, Paulina Sobczak, Namrata Saini, Allen Barry, Amira Mustafa and Andrew Bate
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Olivia Mahaux: GSK
Greg Powell: GSK
François Haguinet: GSK
Paulina Sobczak: GSK
Namrata Saini: GSK
Allen Barry: University of North Carolina
Amira Mustafa: University of North Carolina
Andrew Bate: GSK

Drug Safety, 2023, vol. 46, issue 6, No 8, 614 pages

Abstract: Abstract Introduction Identifying individual characteristics or underlying conditions linked to adverse drug reactions (ADRs) can help optimise the benefit–risk ratio for individuals. A systematic evaluation of statistical methods to identify subgroups potentially at risk using spontaneous ADR report datasets is lacking. Objectives In this study, we aimed to assess concordance between subgroup disproportionality scores and European Medicines Agency Pharmacovigilance Risk Assessment Committee (PRAC) discussions of potential subgroup risk. Methods The subgroup disproportionality method described by Sandberg et al., and variants, were applied to statistically screen for subgroups at potential increased risk of ADRs, using data from the US FDA Adverse Event Reporting System (FAERS) cumulative from 2004 to quarter 2 2021. The reference set used to assess concordance was manually extracted from PRAC minutes from 2015 to 2019. Mentions of subgroups presenting potential differentiated risk and overlapping with the Sandberg method were included. Results Twenty-seven PRAC subgroup examples representing 1719 subgroup drug–event combinations (DECs) in FAERS were included. Using the Sandberg methodology, 2 of the 27 could be detected (one for age and one for sex). No subgroup examples for pregnancy and underlying condition were detected. With a methodological variant, 14 of 27 examples could be detected. Conclusions We observed low concordance between subgroup disproportionality scores and PRAC discussions of potential subgroup risk. Subgroup analyses performed better for age and sex, while for covariates not well-captured in FAERS, such as underlying condition and pregnancy, additional data sources should be considered.

Date: 2023
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DOI: 10.1007/s40264-023-01306-3

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