Performance of Stratified and Subgrouped Disproportionality Analyses in Spontaneous Databases
- 537 Downloads
Disproportionality analyses are used in many organisations to identify adverse drug reactions (ADRs) from spontaneous report data. Reporting patterns vary over time, with patient demographics, and between different geographical regions, and therefore subgroup analyses or adjustment by stratification may be beneficial.
The objective of this study was to evaluate the performance of subgroup and stratified disproportionality analyses for a number of key covariates within spontaneous report databases of differing sizes and characteristics.
Using a reference set of established ADRs, signal detection performance (sensitivity and precision) was compared for stratified, subgroup and crude (unadjusted) analyses within five spontaneous report databases (two company, one national and two international databases). Analyses were repeated for a range of covariates: age, sex, country/region of origin, calendar time period, event seriousness, vaccine/non-vaccine, reporter qualification and report source.
Subgroup analyses consistently performed better than stratified analyses in all databases. Subgroup analyses also showed benefits in both sensitivity and precision over crude analyses for the larger international databases, whilst for the smaller databases a gain in precision tended to result in some loss of sensitivity. Additionally, stratified analyses did not increase sensitivity or precision beyond that associated with analytical artefacts of the analysis. The most promising subgroup covariates were age and region/country of origin, although this varied between databases.
Subgroup analyses perform better than stratified analyses and should be considered over the latter in routine first-pass signal detection. Subgroup analyses are also clearly beneficial over crude analyses for larger databases, but further validation is required for smaller databases.
KeywordsStratify Analysis Crude Analysis Reporting Odds Ratio Disproportionality Analysis Spontaneous Report Database
The views expressed in this paper are those of the authors only and not of their respective organisations and do not reflect the official policy or position of the Innovative Medicines Initiative Joint Undertaking (IMI JU).
Compliance with Ethical Standards
The research leading to these results was conducted as part of PROTECT (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium; www.imi-protect.eu), which is a public–private partnership coordinated by the European Medicines Agency (EMA).
The PROTECT project has received support from the Innovative Medicines Initiative Joint Undertaking (IMI JU; www.imi.europa.eu) under Grant Agreement No 115004, resources of which are composed of financial contribution from the European Union’s Seventh Framework Program (FP7/2007-2013) and companies of the European Federation of Pharmaceutical Industries and Associations (EFPIA) in-kind contribution.
Conflict of interest
Naashika Quarcoo and Jeffery Painter are employees of and hold shares in GlaxoSmithKline. Ramin Arani and Antoni Wisniewski are employees of and hold shares in AstraZeneca. Products from these companies were among those used to test the methodologies in this research. Suzie Seabroke, Gianmario Candore, Kristina Juhlin, Naashika Quarcoo, Antoni Wisniewski, Ramin Arani, Jeffery Painter, Philip Tregunno, Niklas Norén and Jim Slattery have no financial interest in any commercial signal detection software.
- 11.Hopstadius J, Norén GN. Robust discovery of local patterns: subsets and stratification in adverse drug reaction surveillance. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium. New York: ACM; 2012. p. 265–73 (Abstract).Google Scholar
- 17.Grundmark B, Holmberg L, Garmo H, Zethelius B. Reducing the noise in signal detection of adverse drug reactions by standardizing the background: a pilot study on analyses of proportional reporting ratios-by-therapeutic area. Eur J Clin Pharmacol. 2014;70(5):627–35.CrossRefPubMedPubMedCentralGoogle Scholar
- 22.DuMouchel W. Bayesian data mining in large frequency tables with an application to the FDA spontaneous reporting system. Am Stat. 1999;53(3):177–90.Google Scholar