控制高维数据中调解器选择的错误发现率
Ran Dai1, Ruiyang Li2, Seonjoo Lee3
1Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE 68198, United States.
Biometrics
|July 29, 2024
概括
这项研究引入了一种新的方法来选择高维数据中的介质,如神经成像和遗传数据,同时控制错误发现率 (FDR). 该方法在一项大型研究中成功识别了与童年逆境和认知分数相关的大脑连接标记.
科学领域:
- 神经科学是一个神经科学.
- 遗传学 是一个遗传学.
- 生物统计学 生物统计学
背景情况:
- 在神经科学和遗传学中常见的高维数据集中,调解者选择至关重要.
- 现有的方法可能缺乏对复杂数据的强有力的错误发现率 (FDR) 控制.
研究的目的:
- 开发一种新的统计框架,用于从高维数据中选择调解员.
- 实施一种确保FDR控制中介者识别的方法.
主要方法:
- 制定了一个多假设测试框架,用于调解员的选择.
- 扩展基于淘汰的变量选择用于FDR控制的介质识别.
- 为实际应用开发了相应的算法.
主要成果:
- 拟议的方法和算法实现了有限样本的FDR控制.
- 与现有方法相比,模拟显示出更高的功率和有限样本性能.
- 成功应用于青少年大脑认知发展 (ABCD) 研究.
结论:
- 开发的方法提供了一个统计严格的方法,在高维设置中选择调解者.
- 确定了休息状态功能磁共振成像 (fMRI) 连接标记,作为不良童年经历和认知得分之间的调解者.
- 突出了该方法在像ABCD这样的大规模神经成像研究中的实用性.
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