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Comparing data mining methods on the VAERS database.

David Banks1, Emily Jane Woo, Dale R Burwen

  • 1The Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Rockville, MD 20852, USA.

Pharmacoepidemiology and Drug Safety
|June 15, 2005
PubMed
Summary
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Four data mining methods were compared for vaccine adverse event surveillance using the Vaccine Adverse Event Reporting System (VAERS). Results showed significant variation in identifying vaccine-event associations, highlighting the need for further analysis to determine optimal methods for public health.

Area of Science:

  • Pharmacovigilance
  • Data Mining
  • Biostatistics

Background:

  • Traditional vaccine adverse event surveillance relies on reporting systems.
  • Data mining offers potential to enhance signal detection by analyzing relative reporting proportions across vaccines.
  • The Vaccine Adverse Event Reporting System (VAERS) is a large database of potential vaccine-associated adverse events.

Purpose of the Study:

  • To evaluate and compare four data mining techniques for identifying potential vaccine-adverse event associations.
  • To assess the agreement and performance properties of these methods using the VAERS database.

Main Methods:

  • Four data mining techniques were applied: empirical Bayes geometric mean (EBGM), EBGM's 90% confidence interval lower bound (EB05), proportional reporting ratio (PRR), and screened PRR (SPRR).

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  • These methods were applied to the VAERS database, focusing on vaccine-event combinations with high numerical scores.

    • Main Results:

    • The ranking of vaccine-event combinations varied significantly across the four data mining methods.
    • Known vaccine-event associations did not consistently appear in the top identified pairs for all methods.

    Conclusions:

    • The evaluated data mining methods exhibit differing performance in ranking vaccine-event pairs.
    • The optimal method for public health surveillance depends on specific situations and requires further analysis of true and false alarm rates.
    • Evaluating these methods is crucial for understanding their value in vaccine safety surveillance.