We are now at an amazing time for medical product development in drugs, biological products and medical devices. As a result of dramatic recent advances in biomedical science, information technology and engineering, ``big data’’ from health care in the real-world have become available. Although big data may not necessarily be attuned to provide the preponderance of evidence to a clinical study, high-quality real-world data can be transformed into scientific evidence for regulatory and healthcare decision-making using proven analytical methods and techniques, such as propensity score methodology and Bayesian inference. In this paper, we extend the Bayesian power prior approach for a single-arm study (the current study) to leverage external real-world data. We use propensity score methodology to pre-select a subset of real-world data containing patients that are similar to those in the current study in terms of covariates, and to stratify the selected patients together with those in the current study into more homogeneous strata. The power prior approach is then applied in each stratum to obtain stratum-specific posterior distributions, which are combined to complete the Bayesian inference for the parameters of interest. We evaluate the performance of the proposed method as compared to that of the ordinary power prior approach by simulation and illustrate its implementation using a hypothetical example, based on our regulatory review experience.

我们现在正处于药物，生物制品和医疗设备的医疗产品开发的绝佳时机。由于生物医学科学，信息技术和工程学方面最近的巨大进步，现实世界中来自医疗保健的``大数据''已经可用。尽管不一定需要对大数据进行调优以为临床研究提供大量证据，但可以使用久经考验的分析方法和技术（例如倾向评分）将高质量的真实世界数据转换为科学依据，以进行监管和医疗保健决策方法和贝叶斯推断。在本文中，我们将贝叶斯幂先验方法扩展到单臂研究（当前研究）中，以利用外部实际数据。我们使用倾向评分方法预先选择了一组真实世界数据，其中包含与协变量有关的与当前研究相似的患者，并将所选患者与当前研究中的患者分层为更均一的分层。然后将幂先验方法应用于每个层中以获得特定于层的后验分布，将其组合以完成对感兴趣参数的贝叶斯推断。我们通过仿真评估了该方法与普通电力先行方法相比的性能，并基于我们的监管审查经验，使用一个假设的例子说明了其实现方法。并将所选患者与本研究中的患者分层为更均一的分层。然后将幂先验方法应用于每个层中以获得特定于层的后验分布，将其组合以完成对感兴趣参数的贝叶斯推断。我们通过仿真评估了该方法与普通电力先行方法相比的性能，并基于我们的监管审查经验，使用一个假设的例子说明了其实现方法。并将所选患者与本研究中的患者分层为更均一的分层。然后将幂先验方法应用于每个层中以获得特定于层的后验分布，将其组合以完成对感兴趣参数的贝叶斯推断。我们通过仿真评估了该方法与普通电力先行方法相比的性能，并基于我们的监管审查经验，使用一个假设的例子说明了其实现方法。