title: Extrapolation of efficacy and other data to support the development of new medicines for children: A systematic review of methods.
abstract: When developing new medicines for children, the potential to extrapolate from adult data to reduce the experimental burden in children is well recognised. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. We reviewed the literature to identify statistical methods that could be used to optimise extrapolations in paediatric drug development programmes.
  
  
  
  Web of Science was used to identify papers proposing methods relevant for using data from a 'source population' to support inferences for a 'target population'. Four key areas of methods development were targeted: paediatric clinical trials, trials extrapolating efficacy across ethnic groups or geographic regions, the use of historical data in contemporary clinical trials and using short-term endpoints to support inferences about long-term outcomes.
  
  
  
  Searches identified 626 papers of which 52 met our inclusion criteria. From these we identified 102 methods comprising 58 Bayesian and 44 frequentist approaches. Most Bayesian methods (n = 54) sought to use existing data in the source population to create an informative prior distribution for a future clinical trial. Of these, 46 allowed the source data to be down-weighted to account for potential differences between populations. Bayesian and frequentist versions of methods were found for assessing whether key parameters of source and target populations are commensurate (n = 34). Fourteen frequentist methods synthesised data from different populations using a joint model or a weighted test statistic.
  
  
  
  Several methods were identified as potentially applicable to paediatric drug development. Methods which can accommodate a heterogeneous target population and which allow data from a source population to be down-weighted are preferred. Methods assessing the commensurability of parameters may be used to determine whether it is appropriate to pool data across age groups to estimate treatment effects.
authors.affiliation: Department of Mathematics and Statistics, Fylde College, Lancaster University, Lancaster, UK.
authors.forename: Ian
authors.initials: I
authors.lastname: Wadsworth
authors.affiliation: Department of Mathematics and Statistics, Fylde College, Lancaster University, Lancaster, UK.
authors.forename: Lisa V
authors.initials: LV
authors.lastname: Hampson
authors.affiliation: Department of Mathematics and Statistics, Fylde College, Lancaster University, Lancaster, UK.
authors.forename: Thomas
authors.initials: T
authors.lastname: Jaki
mesh_terms: D000328:Adult; D000367:Age Factors; D001499:Bayes Theorem; D056808:Biostatistics; D002648:Child; D003627:Data Interpretation, Statistical; D003663:Decision Trees; D000076722:Drug Development / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D016896:Treatment Outcome
keywords: Adult; Bayesian; bridging study; commensurability; extrapolation; paediatric; prior distribution; random-effects meta-analysis