University of Florida, Gainesville, FL, USA.
University of Pennsylvania, Philadelphia, PA.
AMIA Annu Symp Proc. 2022 Feb 21;2021:716-725. eCollection 2021.
Recently, there has been a growing interest in using real-world data (RWD) to generate real-world evidence that complements clinical trials. To quantify treatment effects, it is important to develop meaningful RWD-based endpoints. In cancer trials, two real-world endpoints are of particular interest: real-world overall survival (rwOS) and real-world time to next treatment (rwTTNT). In this work, we identified ways to calculate these real-world endpoints with structured electronic health record (EHR) data and validate these endpoints against the gold-standard measurements of these endpoints derived from linked EHR and tumor registry (TR) data. In addition, we examined and reported data quality issues, especially inconsistencies between the EHR and TR data. Using a survival model, we show that the presence of next treatment was not significantly associated with rwOS, but patients who had longer rwTTNT had longer rwOS, validating the use of rwTTNT as a real-world surrogate marker for measuring cancer endpoints.
最近，人们越来越感兴趣地使用真实世界数据（RWD）来生成补充临床试验的真实世界证据。为了量化治疗效果，开发有意义的基于 RWD 的终点非常重要。在癌症试验中，有两个真实世界的终点特别引人关注：真实世界总生存期（rwOS）和真实世界下一次治疗时间（rwTTNT）。在这项工作中，我们确定了使用结构化电子健康记录（EHR）数据计算这些真实世界终点的方法，并根据从 EHR 和肿瘤登记处（TR）数据链接中获得的这些终点的金标准测量值对这些终点进行验证。此外，我们还检查并报告了数据质量问题，特别是 EHR 和 TR 数据之间的不一致性。使用生存模型，我们表明下一次治疗的存在与 rwOS 没有显著关联，但 rwTTNT 较长的患者 rwOS 也较长，验证了 rwTTNT 作为测量癌症终点的真实世界替代标志物的使用。
