dGAMLSS:一个精确的,分布式的算法,以适应一般化附加模型的位置,规模和形状,用于保护隐私的人口参考图表
Fengling Hu1, Jiayi Tong2,3, Margaret Gardner4
1Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania.
Bioinformatics (Oxford, England)
|January 8, 2026
概括
我们开发了分布式的位置,尺度和形状 (GAMLSS) 的通用增材模型,以从多个站点的数据中创建人口参考图表,而无需共享患者信息. 这使得在不同的临床环境中能够进行准确的,保护隐私的分析.
科学领域:
- 生物统计学 生物统计学
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 估计跨年龄和性别的人口参考范围对于识别非典型测量至关重要.
- 推用于模拟非线性增长和人口异质性的位置,规模和形状 (GAMLSS) 的通用添加模型.
- 由于隐私和实际限制,GAMLSS模型适配的数据共享方面的挑战阻碍了多站点研究.
研究的目的:
- 引入一个保护隐私的分布式算法,用于在多个站点上安装GAMLSS模型.
- 为了使人口参考图表的构建,而无需共享患者级数据.
- 解决对GAMLSS适用的联合学习算法的需求.
主要方法:
- 拟议的分布式GAMLSS (dGAMLSS) 是一种新的分布式算法,用于以联合的方式将GAMLSS模型与GAMLSS相匹配.
- 整合了特定策略,以配合流的功能,具有不同的通信效率.
- 开发了一个R包 (dGAMLSS) 来实现算法和管理特定站点的参数.
主要成果:
- 证明了dGAMLSS在构建临床,基因组学和神经成像数据的人口参考图表中的有效性.
- 展示了dGAMLSS准确复制聚合参考图表和统计推理的能力.
- 在需要保护隐私的场景中验证了算法的性能.
结论:
- dGAMLSS成功地使用多站点数据创建人口参考图表,同时保持患者隐私.
- 该算法在分布式设置中提供了可靠的统计建模和推理,在分布式设置中,数据不能被聚合.
- dGAMLSS为需要敏感健康数据的协作研究提供了实用解决方案.
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