ABSTRACT

The amount of real-world data (RWD) available from sources other than randomized-controlled trials (RCTs) has grown ultra-rapidly in recent years. It provides the impetus for generating substantial evidence of effectiveness and safety from both RCTs and RWD to accelerate medical product development. Especially in the areas of unmet needs, the conduct of fully powered RCTs is generally infeasible because of their sizes, duration, cost, or ethical constraints. The unique challenges in such areas include a small patient population, heterogeneity in disease presentation, and a lack of established endpoints. However, merging information from disparate sources is an intricate task. The value of the Bayesian framework has gained more recognition due to its flexibility in calibrating uncertainty and handling data heterogeneity, and its inherent updating process ideal for synthesizing information. Meanwhile, propensity score, as a powerful tool in causal inference, can be used in various ways to adjust for confounders. As a newly emerging data borrowing strategy in a regulatory setting, integrating propensity scores in a Bayesian setting not only utilizes the strengths from Bayesian models but also minimizes bias from external data borrowing. These methods potentially allow information sharing among data sources, provide more reliable estimates when the sample size is small, and improve the efficiency of treatment effect estimation. In this paper, we will review the recent development of methods incorporating propensity score for evidence synthesis under the Bayesian framework, and discuss different examples of incorporating external data with or without RCTs, as well as the recommendations for reporting in clinical studies.

摘要

近年来，可从随机对照试验 (RCT) 以外的来源获得的真实世界数据 (RWD) 的数量增长得非常快。它为从 RCT 和 RWD 生成大量有效性和安全性证据提供了动力，以加速医疗产品的开发。特别是在需求未得到满足的领域，由于其规模、持续时间、成本或伦理限制，进行全功率 RCT 通常是不可行的。这些领域的独特挑战包括患者人数少、疾病表现的异质性以及缺乏既定的终点。然而，合并来自不同来源的信息是一项复杂的任务。贝叶斯框架的价值因其在校准不确定性和处理数据异质性方面的灵活性而获得了更多的认可，其固有的更新过程非常适合合成信息。同时，倾向评分作为因果推理的有力工具，可以以多种方式用于调整混杂因素。作为监管环境中新兴的数据借用策略，在贝叶斯环境中整合倾向得分不仅利用了贝叶斯模型的优势，而且还最大限度地减少了外部数据借用的偏差。这些方法可能允许数据源之间的信息共享，在样本量较小时提供更可靠的估计，并提高治疗效果估计的效率。在本文中，我们将回顾最近在贝叶斯框架下结合倾向得分进行证据合成的方法的发展，并讨论在有或没有 RCT 的情况下结合外部数据的不同示例，