Genentech, South San Francisco, California, USA.
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
Clin Pharmacol Ther. 2020 Feb;107(2):369-377. doi: 10.1002/cpt.1586. Epub 2019 Oct 11.
Oncology drug development increasingly relies on single-arm clinical trials. External controls (ECs) derived from electronic health record (EHR) databases may provide additional context. Patients from a US-based oncology EHR database were aligned with patients from randomized controlled trials (RCTs) and trial-specific eligibility criteria were applied to the EHR dataset. Overall survival (OS) in the EC-derived control arm was compared with OS in the RCT experimental arm. The primary outcome was OS, defined as time from randomization or treatment initiation (EHR) to death. Cox regression models were used to obtain effect estimates using EHR data. EC-derived hazard ratio estimates aligned closely with those from the corresponding RCT with one exception. Comparing log HRs among all RCT and EC results gave a Pearson correlation coefficient of 0.86. Properly selected control arms from contemporaneous EHR data could be used to put single-arm trials of OS in advanced non-small cell lung cancer into context.
肿瘤药物研发越来越依赖于单臂临床试验。来自电子健康记录 (EHR) 数据库的外部对照 (EC) 可能提供额外的背景信息。将来自美国肿瘤 EHR 数据库的患者与随机对照试验 (RCT) 中的患者相匹配,并将特定于试验的入选标准应用于 EHR 数据集。EC 衍生对照臂中的总生存期 (OS) 与 RCT 实验臂中的 OS 进行比较。主要结局是 OS,定义为从随机分组或治疗开始 (EHR) 到死亡的时间。使用 Cox 回归模型使用 EHR 数据获得效应估计。除了一个例外,EC 衍生的风险比估计值与相应 RCT 的结果非常吻合。比较所有 RCT 和 EC 结果的对数 HRs 给出了 0.86 的 Pearson 相关系数。从同期 EHR 数据中正确选择对照臂,可以将晚期非小细胞肺癌的单臂 OS 试验置于适当的背景下。