title: Data mining spontaneous adverse drug event reports for safety signals in Singapore - a comparison of three different disproportionality measures.
abstract: OBJECTIVE
  Quantitative data mining methods can be used to identify potential signals of unexpected relationships between drug and adverse event (AE). This study aims to compare and explore the use of three data mining methods in our small spontaneous AE database.
  
  
  METHODS
  We consider reporting odds ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN) and Gamma Poisson Shrinker (GPS) assuming two different sets of criteria: (1) ROR-1.96SE>1, IC-1.96SD>0, EB05>1 (2) ROR-1.96SE>2, IC-1.96SD>1, EB05 >2. Count of drug-AE pairs ≥3 was considered for ROR and GPS.
  
  
  RESULTS
  The Health Sciences Authority, Singapore received 151,180 AE reports between 1993 and 2013. ROR, BCPNN and GPS identified 2,835, 2,311 and 2,374 significant drug-AE pairs using Criterion 1, and 1,899, 1,101 and 1,358 respectively using Criterion 2. The performance of the three methods with respect to specificity, positive predictive value and negative predictive value were similar, although ROR yielded a higher sensitivity and larger area under the receiver operating characteristic curve. ROR and GPS picked up some potential signals which BCPNN missed.
  
  
  CONCLUSIONS
  The defined threshold used for ROR (Criterion 1) is a useful screening tool for our small database. It may be used in conjunction with GPS to avoid missed signals.
authors.affiliation: a Vigilance & Compliance Branch , Health Products Regulation Group, Health Sciences Authority , Singapore.
authors.forename: Pei San
authors.initials: PS
authors.lastname: Ang
authors.affiliation: b Investigational Medicine Unit , National University Health System , Singapore.
authors.forename: Zhaojin
authors.initials: Z
authors.lastname: Chen
authors.affiliation: a Vigilance & Compliance Branch , Health Products Regulation Group, Health Sciences Authority , Singapore.
authors.forename: Cheng Leng
authors.initials: CL
authors.lastname: Chan
authors.affiliation: c Saw Swee Hock School of Public Health , National University of Singapore , Singapore.|d Yong Loo Lin School of Medicine , National University of Singapore and National University Health System , Singapore.
authors.forename: Bee Choo
authors.initials: BC
authors.lastname: Tai
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D001499:Bayes Theorem; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology*; D006801:Humans; D012846:Singapore / Q000453:epidemiology
keywords: Adverse drug event; data mining; disproportionality analysis; drug safety; pharmacovigilance; signal detection