Ghosal Soutik
Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, Virginia, USA.
Biom J. 2025 Jun;67(3):e70053. doi: 10.1002/bimj.70053.
The receiver operating characteristic (ROC) curve stands as a cornerstone in assessing the efficacy of biomarkers for disease diagnosis. Beyond merely evaluating performance, it provides with an optimal cutoff for biomarker values, crucial for disease categorization. While diverse methodologies exist for cutoff estimation, less attention has been paid to integrating covariate impact into this process. Covariates can strongly impact diagnostic summaries, leading to variations across different covariate levels. Therefore, a tailored covariate-based framework is imperative for outlining covariate-specific optimal cutoffs. Moreover, recent investigations into cutoff estimators have overlooked the influence of ROC curve estimation methodologies. This study endeavors to bridge this gap by addressing the research void. Extensive simulation studies are conducted to scrutinize the performance of ROC curve estimation models in estimating different cutoffs in varying scenarios, encompassing diverse data-generating mechanisms and covariate effects. In addition, leveraging the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set, the research assesses the performance of different biomarkers in diagnosing Alzheimer's disease and determines the suitable optimal cutoffs.
受试者工作特征（ROC）曲线是评估生物标志物用于疾病诊断效能的基石。它不仅仅是评估性能，还能为生物标志物值提供一个最佳截断点，这对疾病分类至关重要。虽然存在多种截断点估计方法，但在将协变量影响纳入这一过程方面关注较少。协变量会强烈影响诊断总结，导致不同协变量水平之间存在差异。因此，一个基于协变量的定制框架对于勾勒特定于协变量的最佳截断点至关重要。此外，最近对截断点估计器的研究忽略了ROC曲线估计方法的影响。本研究旨在通过填补这一研究空白来弥合这一差距。进行了广泛的模拟研究，以审视ROC曲线估计模型在不同场景下估计不同截断点的性能，包括各种数据生成机制和协变量效应。此外，利用阿尔茨海默病神经影像学倡议（ADNI）数据集，该研究评估了不同生物标志物在诊断阿尔茨海默病方面的性能，并确定了合适的最佳截断点。
