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Charting and Sidestepping the Pitfalls of Disproportionality Analysis
Michele Fusaroli1, Daniele Sartori2, Eugène P van Puijenbroek3,4
1Uppsala Monitoring Centre, Uppsala, Sweden. michele.fusaroli@who-umc.org.
Drug Safety
|September 24, 2025
Summary
Disproportionality analysis in pharmacovigilance is often misused, leading to incorrect conclusions about adverse drug reactions. Careful interpretation and advanced methods are crucial to avoid biases and ensure reliable signal detection.
Area of Science:
- Pharmacovigilance
- Drug Safety
- Biostatistics
Background:
- Disproportionality analysis is a common tool in pharmacovigilance for detecting adverse drug reaction signals.
- Misapplication and misunderstanding of its assumptions can lead to erroneous conclusions.
Purpose of the Study:
- To illustrate the pitfalls of simplistic disproportionality analysis.
- To highlight common errors and biases in signal detection using this method.
Main Methods:
- Analysis of VigiBase, the WHO global database of adverse event reports.
- Application of the Information Component disproportionality metric.
- Illustration of biases including confounding, effect modification, notoriety bias, masking, and misclassification.
Main Results:
- Simplistic disproportionality analysis can yield spurious signals or miss important ones.
- Biases such as confounding, effect modification, notoriety bias, masking, and misclassification were identified.
- Sophisticated analyses may introduce or amplify biases like collider bias.
Conclusions:
- Disproportionality analysis should serve a supportive role in signal detection, not a decisive one.
- Careful design, interpretation, subgrouping, and clinical assessment are essential.
- Further development of bias detection and mitigation tools for disproportionality analysis is needed.
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