There is currently a focus on statistical methods which can use historical trial information to help accelerate the discovery, development and delivery of medicine. Bayesian methods can be constructed so that the borrowing is "dynamic" in the sense that the similarity of the data helps to determine how much information is used. In the time to event setting with one historical data set, a popular model for a range of baseline hazards is the piecewise exponential model where the time points are fixed and a borrowing structure is imposed on the model. Although convenient for implementation this approach effects the borrowing capability of the model. We propose a Bayesian model which allows the time points to vary and a dependency to be placed between the baseline hazards. This serves to smooth the posterior baseline hazard improving both model estimation and borrowing characteristics. We explore a variety of prior structures for the borrowing within our proposed model and assess their performance against established approaches. We demonstrate that this leads to improved type I error in the presence of prior data conflict and increased power. We have developed accompanying software which is freely available and enables easy implementation of the approach.

翻译：当前研究重点关注能够利用历史试验信息加速药物发现、开发及交付的统计方法。贝叶斯方法可构建为"动态"借用机制，即通过数据相似性决定信息使用量。在单一历史数据集的时间-事件建模场景中，分段指数模型（固定时间节点并施加借用结构）是处理多种基线风险的常用模型。尽管该模型便于实现，但其借用能力受到约束。我们提出一种贝叶斯模型，允许时间节点变化并在基线风险间建立依赖关系。该模型可平滑后验基线风险，显著改善模型估计与借用特性。我们在所提模型中探索多种借用先验结构，并与现有方法展开性能比较。结果表明，该方法在先验数据冲突时能降低第一类错误率并提升统计功效。我们已开发配套免费软件，便于该方法的实践应用。