title: Outlier removal to uncover patterns in adverse drug reaction surveillance - a simple unmasking strategy.
abstract: OBJECTIVE
  This study aimed to develop an algorithm for uncovering associations masked by extreme reporting rates, characterize the occurrence of masking by influential outliers in two spontaneous reporting databases and evaluate the impact of outlier removal on disproportionality analysis.
  
  
  METHODS
  We propose an algorithm that identifies influential outliers and carries out parallel analysis after their omission. It considers masking of drugs as well as of adverse drug reactions (ADRs), uses a direct measure of the masking effect and makes no assumptions regarding the number of outliers per drug or ADR. The occurrence of masking is characterized in the WHO Global Individual Case Safety Report database, VigiBase and a regional collection of reports from Shanghai, China.
  
  
  RESULTS
  For WHO-ART critical terms such as myocardial infarction, rhabdomyolysis and hypoglycaemia outlier removal led to a 25-50% increase in the number of Statistics of Disproportionate Reporting (SDR) and gains in time to detection of 1-2 years, while keeping the rate of spurious SDRs from the parallel analysis at 1%. Twenty-three per cent of VigiBase and 18% of Shanghai SRS reports listed an influential outlier. Twenty-seven per cent of the ADRs and 5% of the drugs in VigiBase, and 2% of the ADRs and 3% of the drugs in Shanghai SRS were involved in an outlier. The overall increase in the number of SDRs for both datasets was 3%.
  
  
  CONCLUSIONS
  Masking by outliers has substantial impact on specific ADRs including, in VigiBase, rhabdomyolysis, myocardial infarction and hypoglycaemia. It is a local phenomenon involving a fair number of reports but yielding a limited number of additional SDRs.
authors.affiliation: Uppsala Monitoring Centre, WHO Collaborating Centre for International Drug Monitoring, Uppsala, Sweden.
authors.forename: Kristina
authors.initials: K
authors.lastname: Juhlin
authors.forename: Xiaofei
authors.initials: X
authors.lastname: Ye
authors.forename: Kristina
authors.initials: K
authors.lastname: Star
authors.forename: G Niklas
authors.initials: GN
authors.lastname: Norén
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D000465:Algorithms; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D011358:Product Surveillance, Postmarketing; D012189:Retrospective Studies; D014944:World Health Organization
keywords: adverse drug reactions; competition bias; disproportionality analysis; masking; outliers; pharmacoepidemiology; statistical shrinkage