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在N-of-1试验中估计个别因果效应的仪器变量方法:应用于ISTOP研究

Kexin Qu¹, Christopher H Schmid², Tao Liu¹

¹Department of Biostatistics, School of Public Health, Brown University, 121 South Main Street, Providence, RI 02903, United States.

Biostatistics (Oxford, England)

|December 13, 2025

概括

N-of-1试验允许使用可穿戴设备进行个性化治疗决策. 我们的贝叶斯仪器变量方法准确地估计了个别的因果关系,即使不完全遵守和复杂的数据,如I-STOP-AFib研究所示.

科学领域:

生物统计学生物统计学
个性化医疗是个性化的医疗.
可穿戴技术可穿戴技术

背景情况:

在个性化治疗决策中,N-of-1试验至关重要,尤其是在异质反应的情况下.
可穿戴设备提高了N-of-1试验的可行性,以确定最佳的个人治疗计划.
在I-STOP-AFib研究中,研究了心房动 (AF) 的触发因素.

研究的目的:

为N-of-1试验开发一个因果框架.
定义和估计个人因果效应,考虑连续和观察到的行为.
为了应对不完美的合规性,二进制结果和串行相关性等挑战.

主要方法:

利用一个因果框架与潜在的治疗和结果路径.
采用贝叶斯仪器变量 (IV) 方法,使用随机化作为IV.
开发了一套由两个参数贝叶斯模型构成的系统,用于估计个别因果关系,解决非合性和序列相关性问题.

主要成果:

拟议的贝叶斯IV方法在模拟中显示了偏差的减少和对估计的因果效应的更好的覆盖.
该方法有效地处理了不完美的合规性,二进制结果 (非合并性) 和纵向数据自相关性.
应用于I-STOP-AFib研究,它估计了酒精对AF发生的个体影响.

结论:

贝叶斯IV方法为N-of-1试验提供了一个强大的框架,增强了个性化医疗.
这种方法在复杂的场景中准确估计了个别的因果关系,超过了现有方法.
它为分析临床研究中的可穿戴设备数据提供了一个有价值的工具,比如I-STOP-AFib.

关键词:

贝叶斯语贝叶斯语贝叶斯语贝叶斯语有关因果推理的推理.混是一种混.这是一个仪器变量.移动健康移动健康移动健康移动健康时间序列时间序列可穿戴设备可以穿戴.N-of-1试验中的试验.

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