title: Using time-to-onset for detecting safety signals in spontaneous reports of adverse events following immunization: a proof of concept study.
abstract: OBJECTIVE
  Disproportionality analyses (DPA) are widely used in pharmacovigilance for detecting safety signals from spontaneous reports of adverse events. In these analyses, time-to-onset (TTO; the time between vaccination and the onset of the adverse event) is rarely considered. Our objective is to assess the potential use of TTO to improve signal detection (SD).
  
  
  METHODS
  Adverse events were defined as signals for a vaccine if the TTO distribution was significantly different from the distribution of other events for the same vaccine and from the distribution obtained with the same event for other vaccines. Distributions were compared within a time window of 30 days by using the two-sample Kolmogorov-Smirnov statistic. With the use of the product label as a proxy of true positive safety signals, TTO SD was compared with a standard DPA method (based on stratified empirical Bayesian geometric mean) for an oral live pediatric vaccine (Rotarix™) and an inactivated adult vaccine (Fluarix™).
  
  
  RESULTS
  With the use of the GlaxoSmithKline spontaneous reports database for Rotarix™, 10 Medical Dictionary for Regulatory Activities preferred terms were identified as signals, and among them, five were listed in the product label. The DPA method identified only three preferred terms from the label, that is, TTO SD showed higher sensitivity and specificity. For Fluarix™, TTO SD also showed higher sensitivity but lower specificity.
  
  
  CONCLUSIONS
  This TTO SD method is complementary, conceptually and practically, to more traditional DPA and does not share the major drawback of DPA known as the masking effect. Higher sensitivity and/or specificity can be achieved using TTO SD.
authors.affiliation: Safety and Observational Database Research, Epidemiology, GlaxoSmithKline Biologicals, Wavre, Belgium. lionel.vanholle@gmail.com
authors.forename: Lionel
authors.initials: L
authors.lastname: Van Holle
authors.forename: Ziad
authors.initials: Z
authors.lastname: Zeinoun
authors.forename: Vincent
authors.initials: V
authors.lastname: Bauchau
authors.forename: Thomas
authors.initials: T
authors.lastname: Verstraeten
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D003198:Computer Simulation; D005188:False Negative Reactions; D005189:False Positive Reactions; D006801:Humans; D007252:Influenza Vaccines / Q000009:adverse effects*; D015233:Models, Statistical; D022243:Rotavirus Vaccines / Q000009:adverse effects*; D012680:Sensitivity and Specificity; D014613:Vaccines, Attenuated / Q000009:adverse effects; D015164:Vaccines, Inactivated / Q000009:adverse effects
chemical_list: D007252:Influenza Vaccines; C492457:RIX4414 vaccine; D022243:Rotavirus Vaccines; D014613:Vaccines, Attenuated; D015164:Vaccines, Inactivated; C510903:fluarix