title: Signalling paediatric side effects using an ensemble of simple study designs.
abstract: BACKGROUND
  Children are frequently prescribed medication 'off-label', meaning there has not been sufficient testing of the medication to determine its safety or effectiveness. The main reason this safety knowledge is lacking is due to ethical restrictions that prevent children from being included in the majority of clinical trials.
  
  
  OBJECTIVE
  The objective of this paper is to investigate whether an ensemble of simple study designs can be implemented to signal acutely occurring side effects effectively within the paediatric population by using historical longitudinal data. The majority of pharmacovigilance techniques are unsupervised, but this research presents a supervised framework.
  
  
  METHODS
  Multiple measures of association are calculated for each drug and medical event pair and these are used as features that are fed into a classifier to determine the likelihood of the drug and medical event pair corresponding to an adverse drug reaction. The classifier is trained using known adverse drug reactions or known non-adverse drug reaction relationships.
  
  
  RESULTS
  The novel ensemble framework obtained a false positive rate of 0.149, a sensitivity of 0.547 and a specificity of 0.851 when implemented on a reference set of drug and medical event pairs. The novel framework consistently outperformed each individual simple study design.
  
  
  CONCLUSIONS
  This research shows that it is possible to exploit the mechanism of causality and presents a framework for signalling adverse drug reactions effectively.
authors.affiliation: IMA, The University of Nottingham, Nottingham, NG8 1BB, UK, psxjr1@nottingham.ac.uk.
authors.forename: Jenna M
authors.initials: JM
authors.lastname: Reps
authors.forename: Jonathan M
authors.initials: JM
authors.lastname: Garibaldi
authors.forename: Uwe
authors.initials: U
authors.lastname: Aickelin
authors.forename: Daniele
authors.initials: D
authors.lastname: Soria
authors.forename: Jack E
authors.initials: JE
authors.lastname: Gibson
authors.forename: Richard B
authors.initials: RB
authors.lastname: Hubbard
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D015984:Causality; D002648:Child; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D015233:Models, Statistical; D056687:Off-Label Use; D012107:Research Design