There is a lack of validated statistical methods that could help identify subgroups defined by characteristics such as age, sex and underlying conditions, and that might be at increased risk of adverse drug reactions.

We tested one of the few available first-pass screening subgroup methods using a large, diverse dataset of spontaneous adverse event reports (US FDA Adverse Event Reporting System [FAERS]).

Our study showed apparent low concordance between disproportionality scores calculated by subgroup analysis using FAERS and a reference set (European Medicines Agency Pharmacovigilance Risk Assessment Committee discussions of subgroup risk).

Age and sex were better captured within FAERS and showed relatively better concordance among the different covariates tested. Covariates such as pregnancy and underlying condition might benefit from enrichment with additional data sources, such as electronic healthcare records data.