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April 8, 2009Pharmacoepidemiology and Drug Safety896 citations

Quantitative signal detection using spontaneous ADR reporting

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ABAndrew BateLondon School of Hygiene & Tropical MedicineSEStephen EvansLondon School of Hygiene & Tropical Medicine

Key Points

  • To review the mathematical principles, temporal properties, and core methodological challenges of quantitative signal detection algorithms used in spontaneous adverse drug reaction reporting databases.
  • Examined core quantitative disproportionality metrics, including the proportional reporting ratio (PRR), reporting odds ratio (ROR), information component (IC), and empirical Bayes geometric mean (EBGM).
  • Analyzed the utility of Bayesian shrinkage, longitudinal behavior of screening measures, and methodological factors including data stratification, validation frameworks, and real-world system implementation.
  • Identifies Bayesian shrinkage as a crucial mechanism to stabilize disproportionality estimates and prevent spurious signals when report counts are small.
  • Highlights unresolved controversies regarding optimal data stratification methods, standardized algorithm performance evaluation, and practical implementation within pharmacovigilance workflows.

Abstract

Quantitative methods are increasingly used to analyse spontaneous reports. We describe the core concepts behind the most common methods, the proportional reporting ratio (PRR), reporting odds ratio (ROR), information component (IC) and empirical Bayes geometric mean (EBGM). We discuss the role of Bayesian shrinkage in screening spontaneous reports, the importance of changes over time in screening the properties of the measures. Additionally we discuss three major areas of controversy and ongoing research: stratification, method evaluation and implementation. Finally we give some suggestions as to where emerging research is likely to lead.

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Cite This Study

Bate et al. (2009) studied this question.

synapsesocial.com/papers/69c5e6dc7090fce65e4ad854https://doi.org/10.1002/pds.1742
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