When incorporating historical control data into the analysis of current randomized controlled trial data, it is critical to account for differences between the datasets. When the cause of the difference is an unmeasured factor and adjustment for observed covariates only is insufficient, it is desirable to use a dynamic borrowing method that reduces the impact of heterogeneous historical controls. We propose a nonparametric Bayesian approach for borrowing historical controls that are homogeneous with the current control. Additionally, to emphasize the resolution of conflicts between the historical controls and current control, we introduce a method based on the dependent Dirichlet process mixture. The proposed methods can be implemented using the same procedure, regardless of whether the outcome data comprise aggregated study-level data or individual participant data. We also develop a novel index of similarity between the historical and current control data, based on the posterior distribution of the parameter of interest. We conduct a simulation study and analyze clinical trial examples to evaluate the performance of the proposed methods compared to existing methods. The proposed method based on the dependent Dirichlet process mixture can more accurately borrow from homogeneous historical controls while reducing the impact of heterogeneous historical controls compared to the typical Dirichlet process mixture. The proposed methods outperform existing methods in scenarios with heterogeneous historical controls, in which the meta-analytic approach is ineffective.

翻译：将历史对照数据纳入当前随机对照试验数据分析时，必须考虑数据集间的差异。当差异源于未观测因素且仅对已观测协变量进行调整不足时，应采用能降低异质性历史对照影响的动态借用方法。本文提出一种非参数贝叶斯方法，用于借用与当前对照同质的历史对照数据。此外，为强调历史对照与当前对照间冲突的解决，我们引入基于相依狄利克雷过程混合模型的方法。无论结局数据是汇总研究水平数据还是个体参与者数据，所提方法均可通过相同流程实施。基于目标参数的后验分布，我们还构建了衡量历史与当前对照数据相似度的新指标。通过模拟研究和临床试验实例分析，我们将所提方法与现有方法进行性能比较。与典型狄利克雷过程混合模型相比，基于相依狄利克雷过程混合模型的提出方法能更精确地借用同质历史对照，同时降低异质性历史对照的影响。在存在异质性历史对照且元分析方法失效的场景中，所提方法均优于现有方法。