使用分布式观测数据进行治疗效果估计的联合目标试验模拟
Haoyang Li1, Chengxi Zang1, Zhenxing Xu1
1Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
medRxiv : the preprint server for health sciences
|May 19, 2025
概括
基于联合学习的目标试验模拟 (FL-TTE) 能够在分布式数据集中进行隐私保护的治疗效果估计. 这种方法克服了数据共享障碍,提高了概括性,减少了现实世界的证据研究中的偏见.
科学领域:
- 医疗信息学 医疗信息学
- 流行病学 流行病学
- 机器学习 机器学习
背景情况:
- 目标试验仿真 (TTE) 使用现实世界的数据来模拟临床试验以估计治疗效果.
- 在分布式数据集中应用TTE受到隐私和数据共享约束的限制.
- 联合学习为协作分析提供了一个解决方案,而不需要集中敏感的患者数据.
研究的目的:
- 提出和验证一个基于联邦学习的目标试验模拟 (FL-TTE) 框架.
- 在多个,分布式和异质数据集中实现保护隐私的TTE.
- 在多个地点设置中克服传统TTE方法的局限性.
主要方法:
- 开发了FL-TTE,这是一个整合联合协议设计,联合治疗权重的逆概率和联合Cox比例危险模型的框架.
- 应用FL-TTE以模拟来自192家医院的eICU和MIMIC-IV数据的败血症试验.
- 在使用INSIGHT网络在纽约市五个卫生系统的阿尔茨海默病试验中验证了FL-TTE.
主要成果:
- 在没有共享患者级信息的情况下,FL-TTE成功地在分布式和异质数据中实现了TTE.
- 与传统的元分析方法相比,当与聚合结果进行验证时,该框架产生了较少偏见的治疗效应估计.
- FL-TTE在现实场景中展示了理论支持和实际适用性.
结论:
- FL-TTE提供了一种强大且保护隐私的方法,用于联合治疗效果估计.
- 该框架通过利用分布式数据来增强TTE的通用性和力量.
- 在多机构研究中,FL-TTE代表了现实世界证据生成的重大进步.
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