External control arms (ECA) can inform the early clinical development of experimental drugs and provide efficacy evidence for regulatory approval in non-randomized settings. However, the main challenge of implementing ECA lies in accessing real-world data or historical clinical trials. Indeed, data sharing is often not feasible due to privacy considerations related to data leaving the original collection centers, along with pharmaceutical companies' competitive motives. In this paper, we leverage a privacy-enhancing technology called federated learning (FL) to remove some of the barriers to data sharing. We introduce a federated learning inverse probability of treatment weighted (IPTW) method for time-to-event outcomes called FedECA which eases the implementation of ECA by limiting patients' data exposure. We show with extensive experiments that FedECA outperforms its closest competitor, matching-adjusted indirect comparison (MAIC), in terms of statistical power and ability to balance the treatment and control groups. To encourage the use of such methods, we publicly release our code which relies on Substra, an open-source FL software with proven experience in privacy-sensitive contexts.

翻译：外部对照臂（ECA）可为实验性药物的早期临床开发提供信息，并在非随机化环境下为监管审批提供疗效证据。然而，实施ECA的主要挑战在于获取真实世界数据或历史临床试验数据。实际上，由于数据离开原始收集中心涉及隐私问题，加之制药公司的竞争动机，数据共享往往难以实现。本文利用一种称为联邦学习（FL）的隐私增强技术，消除了数据共享的部分障碍。我们提出了一种面向时间-事件结果的联邦学习逆概率治疗加权（IPTW）方法——FedECA，该方法通过限制患者数据暴露来简化ECA的实施。通过大量实验表明，FedECA在统计效能及治疗组与对照组均衡能力方面均优于其最接近的竞争者——匹配调整间接比较（MAIC）。为促进此类方法的应用，我们公开了代码，该代码基于Substra（一款经隐私敏感场景验证的开源FL软件）开发。