Statistical Methodology and Consulting, Novartis, Basel, Switzerland.
Biometrics, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, 101 Orchard Ridge Dr., Gaithersburg, MD, 20878, USA.
Ther Innov Regul Sci. 2022 Sep;56(5):704-716. doi: 10.1007/s43441-022-00413-0. Epub 2022 Jun 8.
Real-world data (RWD) can contextualize findings from single-arm trials when randomized comparative trials are unethical or unfeasible. Findings from single-arm trials alone are difficult to interpret and a comparison, when feasible and meaningful, to patient-level information from RWD facilitates the evaluation. As such, there have been several recent regulatory applications including RWD or other external data to support the product's efficacy and safety. This paper summarizes some lessons learned from such contextualization from 20 notable new drug or biologic licensing applications in oncology and rare diseases.
This review focuses on 20 notable new drug or biologic licensing applications that included patient-level RWD or other external data for contextualization of trial results. Publicly available regulatory documents including clinical and statistical reviews, advisory committee briefing materials and minutes, and approved product labeling were retrieved for each application. The authors conducted independent assessments of these documents focusing on the regulatory evaluation, in each case. Three examples are presented in detail to illustrate the salient issues and themes identified across applications.
Regulatory decisions were strongly influenced by the quality and usability of the RWD. Comparability of cohort attributes such as endpoints, populations, follow-up, index and censoring criteria, as well as data completeness and accuracy of key variables appeared to be essential to ensure the quality and relevance of the RWD. Given adequate sample size of the clinical trials or external control, the use of appropriate analytic methods to properly account for confounding, such as regression or matching, and pre-specification of these methods while blinded to patient outcomes seemed good strategies to address baseline differences.
Contextualizing single-arm trials with patient-level RWD appears to be an advance in regulatory science; however, challenges remain. Statisticians and epidemiologists have long focused on analytical methods for comparative effectiveness but hurdles in use of RWD have often occurred upstream of the analyses. More specifically, we noted hurdles in evaluating data quality, justifying cohort selection or initiation of follow-up, and demonstrating comparability of cohorts and endpoints.
当随机对照试验不道德或不可行时，真实世界数据（RWD）可以为单臂试验的结果提供背景信息。仅单臂试验的结果很难解释，并且当可行且有意义时，与 RWD 中的患者水平信息进行比较有助于评估。因此，最近有几项监管申请包括 RWD 或其他外部数据，以支持产品的疗效和安全性。本文总结了从肿瘤学和罕见病 20 项显著的新药或生物制品许可申请中，通过这种背景化方法获得的一些经验教训。
本综述重点关注了 20 项显著的新药或生物制品许可申请，这些申请包括患者水平的 RWD 或其他外部数据，用于对试验结果进行背景化。每个申请都检索了公开的监管文件，包括临床和统计审查、顾问委员会简报材料和会议记录，以及批准的产品标签。作者对这些文件进行了独立评估，重点是每个案例的监管评估。详细介绍了三个例子，以说明在应用程序中确定的突出问题和主题。
监管决策受到 RWD 质量和可用性的强烈影响。队列属性的可比性，如终点、人群、随访、索引和删失标准，以及关键变量的数据完整性和准确性，似乎对于确保 RWD 的质量和相关性至关重要。鉴于临床试验或外部对照的足够样本量，使用适当的分析方法（如回归或匹配）正确考虑混杂因素，并在对患者结局保持盲态的情况下预先指定这些方法，似乎是解决基线差异的良好策略。
用患者水平的 RWD 对单臂试验进行背景化似乎是监管科学的一项进步；然而，挑战依然存在。统计学家和流行病学家长期以来一直专注于比较有效性的分析方法，但 RWD 的使用障碍往往出现在分析之前。更具体地说，我们注意到在评估数据质量、为队列选择或启动随访提供依据，以及证明队列和终点的可比性方面存在障碍。
