OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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: Caroline Brard; Lisa V Hampson; Nathalie Gaspar; Marie-Cécile Le Deley; Gwénaël Le Teuff Journal: BMC Med Res Methodol Date: 2019-04-24 Impact factor: 4.615
Authors: Deborah Ford; Ian R White; Rebecca M Turner; Anna Turkova; Cecilia L Moore; Alasdair Bamford; Moherndran Archary; Linda N Barlow-Mosha; Mark F Cotton; Tim R Cressey; Elizabeth Kaudha; Abbas Lugemwa; Hermione Lyall; Hilda A Mujuru; Veronica Mulenga; Victor Musiime; Pablo Rojo; Gareth Tudor-Williams; Steven B Welch; Diana M Gibb Journal: BMC Med Res Methodol Date: 2022-02-20 Impact factor: 4.615
Authors: Lauren E Kelly; Michele P Dyson; Nancy J Butcher; Robert Balshaw; Alex John London; Christine J Neilson; Anne Junker; Salaheddin M Mahmud; S Michelle Driedger; Xikui Wang Journal: Trials Date: 2018-10-19 Impact factor: 2.279