title: Pilot evaluation of an automated method to decrease false-positive signals induced by co-prescriptions in spontaneous reporting databases.
abstract: OBJECTIVE
  To test an automated method to decrease the number of false-positive (FP) signals of disproportionate reportings (SDRs) generated by co-prescription.
  
  
  METHODS
  Automated backward stepwise removal of reports concerning the drug associated with the highest ranked SDR for an event was tested for gastric and oesophageal haemorrhages (GOH), central nervous system haemorrhages and cerebrovascular accidents (CNSH), ischaemic coronary artery disorders and muscle pains (MP) using the reporting odds ratio in the French spontaneous reporting research database. After ranking SDRs detected in the complete dataset on the lower limit of the reporting odds ratio 95% confidence interval, reports concerning the drug with the highest ranked SDR were removed. In the dataset thus generated, SDRs were again identified, ranked and reports related to the drug involved in the newly highest ranked SDR removed. The process was repeated until no signal was detected. Initially detected SDRs eliminated using this technique were assessed regarding the summary of products characteristics and the literature to determine their FP nature.
  
  
  RESULTS
  Seventeen SDRs were successively eliminated for GOH, 37 for CNSH, 15 for ischaemic coronary artery disorders, and 36 for MP. Four were FP for GOH, 29 for CNSH, 7 for ACI and none were FP for MP. The positive predictive value of the backward stepwise removal procedure in identifying FP SDRs ranged from 0% (MP) to 78.4% (CNSH).
  
  
  CONCLUSIONS
  Although further adjustment is needed to improve the method presented herein, our results suggest that numerous FP signals because of co-prescription bias could be eliminated using an automated method.
authors.affiliation: Université de Bordeaux, Bordeaux, France.
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
authors.forename: Antoine
authors.initials: A
authors.lastname: Pariente
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D001331:Automation; D015982:Bias; D016208:Databases, Factual / Q000706:statistics & numerical data*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology*; D005189:False Positive Reactions; D005602:France; D006801:Humans; D057230:Limit of Detection; D060735:Pharmacovigilance; D010865:Pilot Projects; D055553:Prescription Drugs / Q000008:administration & dosage / Q000009:adverse effects
chemical_list: D055553:Prescription Drugs
keywords: adverse reaction; bias; methods; pharmacoepidemiology; pharmacovigilance; signal detection; spontaneous reporting