在嵌套案例控制采样下对时间到事件数据进行隐私保护分析
Lamin Juwara1,2, Yi Archer Yang1,3, Ana M Velly2,4
1Quantitative Life Sciences, McGill University, Montreal, Canada.
Statistical methods in medical research
|December 14, 2023
概括
本研究引入了使用聚合数据的罕见疾病研究的隐私保护方法. 该技术可以实现准确的考克斯回归分析,而不会损害参与者的隐私,这对于分布式网络至关重要.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 数据 隐私 数据 隐私 数据
背景情况:
- 针对罕见疾病的分布式数据网络分析面临隐私和道德障碍.
- 从招聘网站共享个人参与者记录往往是不可行的.
- 现有的时间到事件数据的隐私保护方法是计算密集的或不可访问的.
研究的目的:
- 开发一个易于实施的,保护隐私的技术,用于分布式网络中的Cox比例危险回归.
- 为了能够准确分析罕见疾病数据,同时保护参与者的保密性.
- 克服当前隐私保护方法在应用研究中的局限性.
主要方法:
- 根据嵌套的病例控制抽样框架,在招聘地点提出将个别的共同变量记录组合在一起.
- 开发了一种用于为聚合的嵌套病例控制子样本生成伪事件时间的方法.
- 通过广泛的模拟和来自国家肺部查试验的真实世界数据验证了这一方法.
主要成果:
- 聚合危险比率估计器是最大概率估计器,并提供完整队列危险比率的一致估计.
- 拟议的聚合技术实现了接近最佳的性能,与完整的队列分析和合成数据可比.
- 在罕见事件设置中观察到效率增长,控制匹配增加.
结论:
- 拟议的聚合方法为在分布式罕见病数据网络中保护隐私的Cox回归提供了实用和有效的解决方案.
- 这种方法提高了数据共享的可行性和分析准确性,解决了关键的伦理和后勤挑战.
- 该方法显示出显著的实用性和效率,特别是在有限数据的罕见疾病研究中.
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