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.
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机译：目的在开发用于儿童的新药时，已经充分认识到可以从成人数据中推断出减轻儿童实验负担的潜力。但是，要使推论具有生物学上的合理性，需要对成年人和儿童的相似性做出重要假设。我们回顾了文献，以确定可用于优化儿科药物开发计划中的推断的统计方法。方法Web of Science用于识别提出与使用“源人群”数据支持“目标人群”推论相关的方法的论文。方法开发的四个关键领域是针对性的：儿科临床试验，在族裔或地理区域外推论功效的试验，当代临床试验中使用历史数据以及使用短期终点来支持关于长期结果的推论。结果搜索确定了626篇论文，其中52篇符合我们的纳入标准。从这些中，我们确定了102种方法，其中包括58种贝叶斯方法和44种频繁方法。大多数贝叶斯方法（n = 54）试图使用源人群中的现有数据来为将来的临床试验创建信息丰富的先前分布。其中46个允许对源数据进行加权，以解决总体之间的潜在差异。发现了贝叶斯和频率论者版本的方法，用于评估源人群和目标人群的关键参数是否相称（n = 34）。十四种常用方法使用联合模型或加权检验统计量来综合来自不同人群的数据。结论确定了几种可能适用于儿科药物开发的方法。可以容纳异类目标人群并允许对源人群的数据进行加权的方法是首选。评估参数可比性的方法可用于确定是否适合汇总各个年龄段的数据以估计治疗效果。
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