title: Simulating adverse event spontaneous reporting systems as preferential attachment networks: application to the Vaccine Adverse Event Reporting System.
abstract: BACKGROUND
  Spontaneous Reporting Systems [SRS] are critical tools in the post-licensure evaluation of medical product safety. Regulatory authorities use a variety of data mining techniques to detect potential safety signals in SRS databases. Assessing the performance of such signal detection procedures requires simulated SRS databases, but simulation strategies proposed to date each have limitations.
  
  
  OBJECTIVE
  We sought to develop a novel SRS simulation strategy based on plausible mechanisms for the growth of databases over time.
  
  
  METHODS
  We developed a simulation strategy based on the network principle of preferential attachment. We demonstrated how this strategy can be used to create simulations based on specific databases of interest, and provided an example of using such simulations to compare signal detection thresholds for a popular data mining algorithm.
  
  
  RESULTS
  The preferential attachment simulations were generally structurally similar to our targeted SRS database, although they had fewer nodes of very high degree. The approach was able to generate signal-free SRS simulations, as well as mimicking specific known true signals. Explorations of different reporting thresholds for the FDA Vaccine Adverse Event Reporting System suggested that using proportional reporting ratio [PRR] > 3.0 may yield better signal detection operating characteristics than the more commonly used PRR > 2.0 threshold.
  
  
  CONCLUSIONS
  The network analytic approach to SRS simulation based on the principle of preferential attachment provides an attractive framework for exploring the performance of safety signal detection algorithms. This approach is potentially more principled and versatile than existing simulation approaches.
  
  
  CONCLUSIONS
  The utility of network-based SRS simulations needs to be further explored by evaluating other types of simulated signals with a broader range of data mining approaches, and comparing network-based simulations with other simulation strategies where applicable.
authors.affiliation: Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, U. S. Food and Drug Administration.
authors.forename: J
authors.initials: J
authors.lastname: Scott
authors.forename: T
authors.initials: T
authors.lastname: Botsis
authors.affiliation: Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, U. S. Food and Drug Administration.
authors.forename: R
authors.initials: R
authors.lastname: Ball
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D000465:Algorithms; D003198:Computer Simulation; D057225:Data Mining; D016208:Databases, Factual; D006801:Humans; D012984:Software; D014612:Vaccines / Q000009:adverse effects*
chemical_list: D014612:Vaccines
keywords: Simulation; data mining; network analysis; safety; vaccines
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