联合目标试验模拟使用分布式观测数据来估计治疗效果
Haoyang Li1, Chengxi Zang1, Zhenxing Xu1
1Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
NPJ digital medicine
|July 1, 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治疗效果估计. 这种方法克服了数据共享的障碍,提供了比传统方法更普遍和更强大的见解.
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.
- 分布式TTE增强了通用性,但面临着隐私和数据共享的挑战.
- 现有的方法在不损害患者数据的情况下,难以进行跨站点分析.
研究的目的:
- 引入一个新的基于联邦学习的目标试验模拟 (FL-TTE) 框架.
- 在不共享患者级数据的情况下,在多个站点实现TTE.
- 为了促进隐私保护,联合治疗效果估计.
主要方法:
- 开发了结合联合协议设计的FL-TTE.
- 实施治疗权重的联合反向概率.
- 用一个联合的Cox比例危险模型来计算时间到事件的结果.
主要成果:
- 在败血症试验 (eICU,MIMIC-IV) 和阿尔茨海默氏症试验 (INSIGHT网络) 上验证的FL-TTE.
- 与传统的元分析相比,FL-TTE的估计偏差较小.
- 证明了对联合方法的理论支持.
结论:
- 在分布式,异质数据中,FL-TTE成功实现了联合治疗效果估计.
- 该框架保护数据隐私,克服了重大现实世界的数据挑战.
- FL-TTE为大规模的多站点临床试验模拟提供了强大的解决方案.
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