In this paper, a propensity score-integrated composite likelihood (PSCL) approach is developed for cases in which the control arm of a two-arm randomized controlled trial (RCT) (treated vs control) is augmented with patients from real-world data (RWD) containing both clinical outcomes and covariates at the patient-level. RWD patients who were treated with the same therapy as the control arm of the RCT are considered for the augmentation. The PSCL approach first estimates the propensity score for every patient as the probability of the patient being in the RCT rather than the RWD, and then stratifies all patients into strata based on the estimated propensity scores. Within each propensity score stratum, a composite likelihood function is specified and utilized to down-weight the information contributed by the RWD source. Estimates of the stratum-specific parameters are 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 two-arm RCT and a hypothetical RWD source are used to illustrate the implementation of the proposed approach.

中文翻译：

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倾向评分综合复合似然方法，通过结合真实世界的数据来增强随机对照试验的控制臂。

在本文中，针对两臂随机对照试验（RCT）（治疗vs对照）的控制臂增加了来自真实世界数据的患者的情况，开发了一种倾向得分综合复合可能性（PSCL）方法（ RWD）包含临床水平和患者水平的协变量。接受与RCT对照组相同治疗的RWD患者应考虑进行增强。PSCL方法首先将每个患者的倾向得分估计为患者处于RCT而非RWD中的概率，然后根据估计的倾向得分将所有患者分层。在每个倾向得分层中，指定了复合似然函数并将其用于权重RWD源提供的信息。通过最大化复合似然函数，可以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。假设的两臂RCT和假设的RWD源用于说明所提出方法的实现。