一种用于估计协变量调整的受试者工作特征曲线下面积的半参数方法
A semiparametric approach for the estimation of covariate-adjusted area under the receiver operating characteristic curve
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中文总结 AI 辅助
本研究针对临床中生物标志物判别性能的协变量异质性问题，提出基于广义加性模型的半参数框架估计协变量调整的AUC，通过模拟和ADNI数据验证了方法的有效性。
中文摘要 AI 辅助
受试者工作特征（ROC）曲线及ROC曲线下面积（AUC）被广泛用于评估生物标志物的判别能力。然而在许多临床场景中，诊断准确性会随患者特征存在显著异质性，若未考虑这种异质性可能会得出误导性结论。我们提出一种基于广义加性模型的新型半参数框架，用于估计协变量特异及协变量调整的AUC，同时允许协变量对生物标志物性能产生非线性和交互效应。该方法可同时处理二分类和多分类疾病状态，我们还建立了所提估计量的渐近性质。模拟研究表明其具有良好的有限样本性能，我们采用阿尔茨海默病神经影像倡议（ADNI）的数据对该方法进行了示例分析，观察到生物标志物判别性能随协变量存在显著异质性。
英文摘要
Receiver operating characteristic (ROC) and the area under the ROC curve (AUC) are widely used to evaluate the discriminative ability of biomarkers. In many clinical settings, however, diagnostic accuracy varies substantially across patient characteristics, and failure to account for such heterogeneity can lead to misleading conclusions. We propose a new semiparametric framework based on generalized additive models to estimate covariate-specific and covariate-adjusted AUC while allowing for nonlinear and interaction effects of covariates on biomarker performance. Our method accommodates both binary and multicategory disease status. We also establish the asymptotic properties of the proposed estimators. Simulations demonstrate favorable finite-sample performance. We illustrate the method using data from the Alzheimer's Disease Neuroimaging Initiative, where substantial heterogeneity in biomarker discrimination across covariates is observed.
