Statistical and Quantitative Sciences, Takeda Pharmaceuticals, 300 Massachusetts Ave, Cambridge, MA 02139, United States.
Statistical and Quantitative Sciences, Takeda Pharmaceuticals, 300 Massachusetts Ave, Cambridge, MA 02139, United States.
Contemp Clin Trials. 2020 Oct;97:106123. doi: 10.1016/j.cct.2020.106123. Epub 2020 Aug 24.
In recent years, with the rapid increase in the volume and accessibility of Real-World-Data (RWD) and Real-World-Evidence (RWE), we have seen the unprecedented opportunities for their use in drug clinical development and life-cycle management. RWD and RWE have demonstrated the significant potential to improve the design, planning, and execution of clinical development. Furthermore, they can feature in the designs as either a substitute or compliment to traditional clinical trials. However, to utilize RWD and RWE appropriately and wisely, it is critical to apply rigorous statistical methodologies that enable the robustness of results to be characterized and ascertained. Several statistical methodologies including exact matching, propensity score methods, matching-adjusted indirect comparisons and meta-analysis have been proposed for analyzing RWD. Among them, propensity score method is one of the most commonly used methods for non-randomized trials with indirect comparison. Although massive methodologies and examples have been published and discussed since propensity score methods were introduced, systematic review and discussion of how to rigorously use propensity score methods in the practical clinical development is still deficient. This paper introduces commonly used and emerging propensity score methods with detailed discussions of their pros and cons. Three different case studies are presented to illustrate the practical considerations of utilizing propensity score methods in the study design and evaluation using real-world and historical data. Additional considerations including selection of patient populations, endpoints, baseline covariates, propensity score methods, sensitivity analysis and practical implementation flow in clinical development will be discussed.
近年来，随着真实世界数据（RWD）和真实世界证据（RWE）的数量和可及性的快速增加，我们看到了前所未有的机会，可以将其用于药物临床开发和生命周期管理。RWD 和 RWE 已显示出极大的潜力，可以改进临床开发的设计、规划和执行。此外，它们可以作为传统临床试验的替代或补充纳入设计中。然而，要想合理、明智地利用 RWD 和 RWE，就必须应用严格的统计方法，以确定结果的稳健性。已经提出了几种统计方法，包括精确匹配、倾向评分方法、匹配调整间接比较和荟萃分析，用于分析 RWD。其中，倾向评分方法是最常用于非随机试验和间接比较的方法之一。尽管自引入倾向评分方法以来已经发表和讨论了大量的方法和实例，但仍缺乏关于如何在实际临床开发中严格使用倾向评分方法的系统综述和讨论。本文介绍了常用和新兴的倾向评分方法，并详细讨论了它们的优缺点。通过三个不同的案例研究，说明了在使用真实世界和历史数据进行研究设计和评估时，利用倾向评分方法的实际考虑因素。还将讨论其他考虑因素，包括患者人群选择、终点、基线协变量、倾向评分方法、敏感性分析和临床开发中的实际实施流程。
