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Drug Safety

, Volume 39, Issue 4, pp 355–364 | Cite as

Performance of Stratified and Subgrouped Disproportionality Analyses in Spontaneous Databases

  • Suzie SeabrokeEmail author
  • Gianmario Candore
  • Kristina Juhlin
  • Naashika Quarcoo
  • Antoni Wisniewski
  • Ramin Arani
  • Jeffery Painter
  • Philip Tregunno
  • G. Niklas Norén
  • Jim Slattery
Original Research Article

Abstract

Introduction

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.

Objective

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.

Methods

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.

Results

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.

Conclusions

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.

Keywords

Stratify Analysis Crude Analysis Reporting Odds Ratio Disproportionality Analysis Spontaneous Report Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

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

Funding

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.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Suzie Seabroke
    • 1
    Email author
  • Gianmario Candore
    • 2
  • Kristina Juhlin
    • 3
  • Naashika Quarcoo
    • 4
  • Antoni Wisniewski
    • 5
  • Ramin Arani
    • 5
  • Jeffery Painter
    • 4
  • Philip Tregunno
    • 1
  • G. Niklas Norén
    • 3
  • Jim Slattery
    • 2
  1. 1.UK Medicines and Healthcare Products Regulatory Agency (MHRA)LondonUK
  2. 2.European Medicines AgencyLondonUK
  3. 3.Uppsala Monitoring CentreUppsalaSweden
  4. 4.GlaxoSmithKlineLondonUK
  5. 5.AstraZenecaAlderley ParkUK

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