title: Using electronic health care records for drug safety signal detection: a comparative evaluation of statistical methods.
abstract: BACKGROUND
  Drug safety monitoring relies primarily on spontaneous reporting, but electronic health care record databases offer a possible alternative for the detection of adverse drug reactions (ADRs).
  
  
  OBJECTIVE
  To evaluate the relative performance of different statistical methods for detecting drug-adverse event associations in electronic health care record data representing potential ADRs.
  
  
  METHODS
  Data from 7 databases across 3 countries in Europe comprising over 20 million subjects were used to compute the relative risk estimates for drug-event pairs using 10 different methods, including those developed for spontaneous reporting systems, cohort methods such as the longitudinal gamma poisson shrinker, and case-based methods such as case-control. The newly developed method "longitudinal evaluation of observational profiles of adverse events related to drugs" (LEOPARD) was used to remove associations likely caused by protopathic bias. Data from the different databases were combined by pooling of data, and by meta-analysis for random effects. A reference standard of known ADRs and negative controls was created to evaluate the performance of the method.
  
  
  METHODS
  The area under the curve of the receiver operator characteristic curve was calculated for each method, both with and without LEOPARD filtering.
  
  
  RESULTS
  The highest area under the curve (0.83) was achieved by the combination of either longitudinal gamma poisson shrinker or case-control with LEOPARD filtering, but the performance between methods differed little. LEOPARD increased the overall performance, but flagged several known ADRs as caused by protopathic bias.
  
  
  CONCLUSIONS
  Combinations of methods demonstrate good performance in distinguishing known ADRs from negative controls, and we assume that these could also be used to detect new drug safety signals.
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands. m.schuemie@erasmusmc.nl
authors.forename: Martijn J
authors.initials: MJ
authors.lastname: Schuemie
authors.forename: Preciosa M
authors.initials: PM
authors.lastname: Coloma
authors.forename: Huub
authors.initials: H
authors.lastname: Straatman
authors.forename: Ron M C
authors.initials: RM
authors.lastname: Herings
authors.forename: Gianluca
authors.initials: G
authors.lastname: Trifirò
authors.forename: Justin Neil
authors.initials: JN
authors.lastname: Matthews
authors.forename: David
authors.initials: D
authors.lastname: Prieto-Merino
authors.forename: Mariam
authors.initials: M
authors.lastname: Molokhia
authors.forename: Lars
authors.initials: L
authors.lastname: Pedersen
authors.forename: Rosa
authors.initials: R
authors.lastname: Gini
authors.forename: Francesco
authors.initials: F
authors.lastname: Innocenti
authors.forename: Giampiero
authors.initials: G
authors.lastname: Mazzaglia
authors.forename: Gino
authors.initials: G
authors.lastname: Picelli
authors.forename: Lorenza
authors.initials: L
authors.lastname: Scotti
authors.forename: Johan
authors.initials: J
authors.lastname: van der Lei
authors.forename: Miriam C J M
authors.initials: MC
authors.lastname: Sturkenboom
mesh_terms: D057286:Electronic Health Records / Q000458:organization & administration*; D005060:Europe; D006801:Humans; D015233:Models, Statistical; D055553:Prescription Drugs / Q000009:adverse effects*; D011358:Product Surveillance, Postmarketing / Q000379:methods*; D012372:ROC Curve; D013223:Statistics as Topic / Q000379:methods*
chemical_list: D055553:Prescription Drugs