Du Zhiyuan, Du Pang, Liu Aiyi
Department of Statistics, Virginia Tech, Blacksburg, Virginia, USA.
Biostatistics and Bioinformatics Branch, Division of Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland, USA.
Stat Med. 2024 Mar 30;43(7):1372-1383. doi: 10.1002/sim.10026. Epub 2024 Jan 30.
The diagnostic accuracy of multiple biomarkers in medical research is crucial for detecting diseases and predicting patient outcomes. An optimal method for combining these biomarkers is essential to maximize the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Although the optimality of the likelihood ratio has been proven by Neyman and Pearson, challenges persist in estimating the likelihood ratio, primarily due to the estimation of multivariate density functions. In this study, we propose a non-parametric approach for estimating multivariate density functions by utilizing Smoothing Spline density estimation to approximate the full likelihood function for both diseased and non-diseased groups, which compose the likelihood ratio. Simulation results demonstrate the efficiency of our method compared to other biomarker combination techniques under various settings for generated biomarker values. Additionally, we apply the proposed method to a real-world study aimed at detecting childhood autism spectrum disorder (ASD), showcasing its practical relevance and potential for future applications in medical research.
医学研究中多种生物标志物的诊断准确性对于疾病检测和患者预后预测至关重要。组合这些生物标志物的最佳方法对于最大化受试者工作特征曲线（ROC）下面积（AUC）至关重要。尽管奈曼和皮尔逊已经证明了似然比的最优性，但在估计似然比时仍存在挑战，主要是由于多元密度函数的估计。在本研究中，我们提出了一种非参数方法，通过利用平滑样条密度估计来估计多元密度函数，以近似构成似然比的患病组和非患病组的全似然函数。模拟结果表明，在生成生物标志物值的各种设置下，我们的方法与其他生物标志物组合技术相比具有更高的效率。此外，我们将所提出的方法应用于一项旨在检测儿童自闭症谱系障碍（ASD）的实际研究中，展示了其实际相关性以及在医学研究中未来应用的潜力。
