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：当前研究重点在于能够利用历史试验信息以加速药物发现、开发及交付的统计方法。通过构建贝叶斯方法，可实现"动态"借用——即数据相似性决定信息使用程度。在单一历史数据集的生存分析场景中，针对多种基线风险函数的常用模型为分段指数模型，该模型固定时间节点并施加借用结构。尽管便于实施，但这种做法影响了模型的借用能力。我们提出一种贝叶斯模型，允许时间节点动态变化，并在基线风险函数间建立依赖关系。这有助于平滑后验基线风险函数，从而改善模型估计与借用特性。我们在所提模型中探索了多种借用先验结构，并评估其相较于既有方法的性能表现。研究表明，该方法在存在先验数据冲突时能提升I类错误控制能力，同时增强统计功效。我们同步开发了免费配套软件，便于该方法便捷实施。