title: Effect of competition bias in safety signal generation: analysis of a research database of spontaneous reports in France.
abstract: BACKGROUND
  Automated disproportionality analysis of spontaneous reporting is increasingly used routinely. It can theoretically be influenced by a competition bias for signal detection owing to the presence of reports related to well-established drug-event associations.
  
  
  OBJECTIVE
  The aim of the study was to explore the effects of competition bias on safety signals generated from a large spontaneous reporting research database.
  
  
  METHODS
  Using the case/non-case approach in the French spontaneous reporting research database, which includes data of reporting in France from January 1986 to December 2001, the effects of the competition bias were explored by generating safety signals associated with six events of interest (gastric and oesophageal haemorrhages, central nervous system haemorrhage and cerebrovascular accidents, ischaemic coronary disorders, migraine headaches, muscle pains, and hepatic enzymes and function abnormalities) before and after removing from the database reports relating to drugs known to be strongly associated with these events, whether they constituted cases or non-cases. As this study was performed on a closed database (last data entered 31 December 2001), potential signals unmasked by removal were considered as real signals if no or only incomplete knowledge about the association was available from the literature before 1 January 2002.
  
  
  RESULTS
  For gastric and oesophageal haemorrhages, after removing reports involving antithrombotic agents or NSAIDs, three potential signals were unmasked (prednisone, rivastigmine and isotretinoin). For central nervous system haemorrhage and cerebrovascular accidents, after removing reports involving antithrombotic agents, three potential signals were unmasked (ethinylestradiol, interferon-α-2B and methylprednisolone). For ischaemic coronary disorders, after removing reports involving anthracyclines, bleomycine, anti-HIV drugs or triptans, one potential signal was unmasked (ondansetron). For migraine headaches, after removing reports involving nitrates, calcium channel blockers, opioid analgesics or intravenous immunoglobulins, six potential signals were unmasked (ammonium chloride, leflunomide, milnacipran, montelukast, proguanil and pyridostigmine). For muscle pains, after removing reports involving statins or fibrates, seven potential signals were unmasked (hydroxychloroquine, lactulose, levodopa in combination with dopadecarboxylase inhibitor, nevirapine, nomegestrol, ritonavir and stavudine). Finally, for hepatic enzymes and function abnormalities, after removing reports involving NSAIDs, anilides, antituberculosis drugs, antiepileptics, ketoconazole, tacrine, or amineptine, two potential signals were unmasked (caffeine, metformin). Of all these unmasked potential signals, ten appeared non/incompletely documented as at 1 January 2002 and were considered as real signals, with three of these later being confirmed by the literature and finally considered as true positives (isotretinoin, methylprednisolone and milnacipran).
  
  
  CONCLUSIONS
  This study confirms that a competition bias can occur when performing safety signal generation in spontaneous reporting databases. The minimization of this bias could lead to previously masked signals being revealed.
authors.affiliation: Département de Pharmacologie, INSERM U657, Université Bordeaux Segalen, Bordeaux, France. antoine.pariente@u-bordeaux2.fr
authors.forename: Antoine
authors.initials: A
authors.lastname: Pariente
authors.forename: Paul
authors.initials: P
authors.lastname: Avillach
authors.forename: Francesco
authors.initials: F
authors.lastname: Salvo
authors.forename: Frantz
authors.initials: F
authors.lastname: Thiessard
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: Bernard
authors.initials: B
authors.lastname: Bégaud
authors.forename: Nicholas
authors.initials: N
authors.lastname: Moore
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000592:standards / Q000706:statistics & numerical data*; D015982:Bias; D057225:Data Mining / Q000379:methods / Q000592:standards / Q000706:statistics & numerical data*; D016208:Databases, Factual / Q000706:statistics & numerical data*; D064420:Drug-Related Side Effects and Adverse Reactions; D005602:France; D006801:Humans