title: A potential competition bias in the detection of safety signals from spontaneous reporting databases.
abstract: OBJECTIVE
  To study whether reports related to known drug-event associations could hinder the detection of new signals by increasing the detection thresholds when using disporportionality analyses in spontaneous reporting (SR) databases.
  
  
  METHODS
  The French SR database (2005-2006 data) was used to test this hypothesis for the following events: bleeding, headache, hepatitis, myalgia, myocardial infarction, stroke, and toxic epidermal necrolysis (TEN). For each of these, using the Proportional Reporting Ratio (PRR) and the Reporting Odds Ratio (ROR), the number of cases needed to trigger a signal out of 50, 100, and 200 reports for a hypothetical newly introduced drug were computed before and after removing from the database reports involving drugs known to be associated with the event.
  
  
  RESULTS
  For bleeding and stroke, removing potentially competitive data resulted in a decrease of the number of cases needed to trigger a signal for a newly introduced drug for both PRR and ROR (e.g., from 9 to 4, and 5 to 3 cases out of 50 reports for bleeding and stroke, respectively using the PRR). They were not or only slightly modified for the other studied events.
  
  
  CONCLUSIONS
  Removing reports related to known drug-event associations could increase the sensitivity of signal detection in SR databases. This should be considered when using SR databases for signal detection as it could result in earlier identification of new drug-event associations.
authors.affiliation: Département de Pharmacologie, Université Victor Segalen Bordeaux 2, Bordeaux, France. antoine.pariente@pharmaco.u-bordeaux2.fr
authors.forename: Antoine
authors.initials: A
authors.lastname: Pariente
authors.forename: Marie
authors.initials: M
authors.lastname: Didailler
authors.forename: Paul
authors.initials: P
authors.lastname: Avillach
authors.forename: Ghada
authors.initials: G
authors.lastname: Miremont-Salamé
authors.forename: Annie
authors.initials: A
authors.lastname: Fourrier-Reglat
authors.forename: Françoise
authors.initials: F
authors.lastname: Haramburu
authors.forename: Nicholas
authors.initials: N
authors.lastname: Moore
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D015982:Bias; D003627:Data Interpretation, Statistical; D016208:Databases, Factual / Q000592:standards* / Q000706:statistics & numerical data*; D064420:Drug-Related Side Effects and Adverse Reactions; D005602:France / Q000453:epidemiology; D006801:Humans; D016017:Odds Ratio