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在复杂的采样设计中评估AUC估计:从COVID-19患者数据的见解

Amaia Iparragirre¹, José María Quintana-López^2,3,4, Irantzu Barrio^5,6

¹Department of Mathematics, University of the Basque Country, Leioa, 48940, Basque Country, Spain. amaia.iparragirre@ehu.eus.

BMC medical research methodology

|August 9, 2025

概括

对于复杂的调查数据,一个新的基于设计的ROC曲线下面积 (AUC) 估计器提供了公正的结果. 传统的AUC估计器可能会有偏见,特别是复杂的采样设计,使得基于设计的方法更适合准确的歧视能力估计.

科学领域:

生物统计学生物统计学
流行病学流行病学
健康调查方法方法卫生调查方法

背景情况:

医学研究经常使用复杂的采样设计 (例如分层,聚类) 的大规模健康调查.
传统的统计方法可能会在复杂的调查数据中产生无效的结果,需要专门的技术.
精确估计物流回归模型的歧视,使用接收器运行特征曲线 (AUC) 下的面积至关重要.

研究的目的:

为了比较传统和新提出的基于设计的AUC估计器的性能.
评估用于复杂采样设计健康数据的后勤回归模型的AUC估计.
确定在健康调查中评估模式歧视的最可靠方法.

主要方法:

一项模拟研究使用了来自巴斯克国家的COVID-19患者群体.
采用各种复杂的抽样设计,抽取多个样本.
配备了后勤回归模型,并使用传统和基于设计的方法估计了AUC,与真实人群AUC相比.

主要成果:

基于设计的AUC估计器产生了公正的结果.
传统的AUC估计表现出偏差,受取样设计及其变量的影响.
采样设计中的集群增加了估计器变异性;更强的变量-结果关系放大了传统估计器中的偏差.

结论:

对于复杂的调查数据,建议使用基于设计的AUC估计器.
使用基于设计的估计器有助于避免偏见的歧视能力估计.
这种方法可确保在健康研究中对物流回归模型进行更可靠的评估.

关键词:

它们的AUC AUC.在 COVID-19 疫情中,临床预测模型的临床预测模型.复杂的采样设计.

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