title: Association rule mining in the US Vaccine Adverse Event Reporting System (VAERS).
abstract: OBJECTIVE
  Spontaneous adverse event reporting systems are critical tools for monitoring the safety of licensed medical products. Commonly used signal detection algorithms identify disproportionate product-adverse event pairs and may not be sensitive to more complex potential signals. We sought to develop a computationally tractable multivariate data-mining approach to identify product-multiple adverse event associations.
  
  
  METHODS
  We describe an application of stepwise association rule mining (Step-ARM) to detect potential vaccine-symptom group associations in the US Vaccine Adverse Event Reporting System. Step-ARM identifies strong associations between one vaccine and one or more adverse events. To reduce the number of redundant association rules found by Step-ARM, we also propose a clustering method for the post-processing of association rules.
  
  
  RESULTS
  In sample applications to a trivalent intradermal inactivated influenza virus vaccine and to measles, mumps, rubella, and varicella (MMRV) vaccine and in simulation studies, we find that Step-ARM can detect a variety of medically coherent potential vaccine-symptom group signals efficiently. In the MMRV example, Step-ARM appears to outperform univariate methods in detecting a known safety signal.
  
  
  CONCLUSIONS
  Our approach is sensitive to potentially complex signals, which may be particularly important when monitoring novel medical countermeasure products such as pandemic influenza vaccines. The post-processing clustering algorithm improves the applicability of the approach as a screening method to identify patterns that may merit further investigation.
authors.affiliation: Division of Biostatistics, Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Lai
authors.initials: L
authors.lastname: Wei
authors.affiliation: Division of Biostatistics, Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: John
authors.initials: J
authors.lastname: Scott
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D019433:Chickenpox Vaccine / Q000009:adverse effects*; D057225:Data Mining / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000175:diagnosis / Q000453:epidemiology*; D006801:Humans; D007252:Influenza Vaccines / Q000009:adverse effects*; D022542:Measles-Mumps-Rubella Vaccine / Q000009:adverse effects*; D015999:Multivariate Analysis; D014481:United States / Q000453:epidemiology; D017778:Vaccines, Combined / Q000009:adverse effects
chemical_list: D019433:Chickenpox Vaccine; D007252:Influenza Vaccines; D022542:Measles-Mumps-Rubella Vaccine; D017778:Vaccines, Combined; C050102:measles, mumps, rubella, varicella vaccine
keywords: association rule discovery; data mining; pharmacoepidemiology; signal detection; spontaneous reporting system; vaccine safety