In medical product development, there has been an increased interest in utilizing real-world data which have become abundant with recent advances in biomedical science, information technology, and engineering. High-quality real-world data may be analyzed to generate real-world evidence that can be utilized in the regulatory and healthcare decision-making. In this paper, we consider the case in which a single-arm clinical study, viewed as the primary data source, is supplemented with patients from a real-world data source containing both clinical outcome and covariate data at the patient-level. Propensity score methodology is used to identify real-world data patients that are similar to those in the single-arm study in terms of the baseline characteristics, and to stratify these patients into strata based on the proximity of the propensity scores. In each stratum, a composite likelihood function of a parameter of interest is constructed by down-weighting the information from the real-world data source, and an estimate of the stratum-specific parameter is obtained by maximizing the composite likelihood function. These stratum-specific estimates are then combined to obtain an overall population-level estimate of the parameter of interest. The performance of the proposed approach is evaluated via a simulation study. A hypothetical example based on our experience is provided to illustrate the implementation of the proposed approach.

中文翻译：

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倾向评分综合复合可能性方法，用于将真实证据纳入单臂临床研究。

在医疗产品开发中，人们对利用现实世界的数据越来越感兴趣，随着生物医学，信息技术和工程学的最新发展，这些数据变得越来越丰富。可以对高质量的现实世界数据进行分析，以生成可以在监管和医疗保健决策中使用的现实世界证据。在本文中，我们考虑了单臂临床研究（被视为主要数据源）补充有来自真实世界数据源的患者的情况，该数据源同时包含患者水平的临床结果和协变量数据。倾向评分方法用于识别基线特征方面与单臂研究相似的真实数据患者，并根据倾向评分的接近程度将这些患者分层。在每个层中，通过对来自真实世界数据源的信息进行权重加权，可以构建感兴趣参数的复合似然函数，并通过最大化复合似然函数来获得特定于层的参数的估计值。然后将这些特定于层的估计组合起来，以获得感兴趣参数的总体总体水平估计。通过仿真研究评估了所提出方法的性能。提供了一个基于我们的经验的假设示例，以说明所提出方法的实现。然后将这些特定于层的估计组合起来，以获得感兴趣参数的总体总体水平估计。通过仿真研究评估了所提出方法的性能。提供了一个基于我们的经验的假设示例，以说明所提出方法的实现。然后将这些特定于层的估计组合起来，以获得感兴趣参数的总体总体水平估计。通过仿真研究评估了所提出方法的性能。提供了一个基于我们的经验的假设示例，以说明所提出方法的实现。