With the advancement of precision medicine there is an increasing need for design and analysis methods in clinical trials with the objective of investigating effect heterogeneity and estimating subgroup effects. As this requires precise estimation of interaction effects, borrowing information from external data sources including retrospective studies and early phase clinical trials to enrich the trial in sparse subgroups is pertinent. Motivated by a trial in gastric cancer we consider a practical design and analysis framework for borrowing from external data sources that only partially inform the inference. As the analysis model we propose an individually weighted model where the external data are weighted based on their fit with the target population based on the distribution of a set of covariates. In a simulation study we assess the performance of the model under various scenarios and make comparisons to dynamic borrowing. In addition, we provide a Bayesian design framework where design priors are extracted from the external data to determine decision boundaries and sample sizes. The design procedure is demonstrated within the context of our motivating example.

翻译：随着精准医学的发展，临床试验中对效应异质性研究和亚组效应估计的需求日益增长，这催生了对相应设计与分析方法的需求。由于此类研究需要精确估计交互效应，从回顾性研究和早期临床试验等外部数据源借用信息以丰富稀疏亚组的试验数据显得尤为重要。基于一项胃癌临床试验的启发，我们提出了一种实用的设计与分析框架，用于从仅部分支持推断的外部数据源借用信息。作为分析模型，我们提出了一种个体加权模型，该模型根据一组协变量的分布，基于外部数据与目标人群的拟合程度对其进行加权。在一项模拟研究中，我们评估了该模型在不同情境下的性能，并与动态借用方法进行了比较。此外，我们提供了一个贝叶斯设计框架，其中从外部数据提取设计先验以确定决策边界和样本量。该设计流程在我们启发性案例的背景下进行了演示。