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 threshold 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. Additionally, leveraging the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the research assesses the performance of different biomarkers in diagnosing Alzheimer's disease and determines the suitable optimal cutoffs.

翻译：受试者工作特征曲线是评估生物标志物在疾病诊断中效能的基石。除评估性能外，该曲线还为生物标志物值提供最优截断值，这对疾病分类至关重要。尽管已有多种阈值估计方法，但将协变量影响纳入该过程的关注尚不足。协变量可能显著影响诊断汇总指标，导致不同协变量水平间的估计结果存在差异。因此，为不同协变量量身定制基于协变量的框架，以确定其专属最优截断值具有必要性。此外，近期关于截断值估计量的研究忽略了ROC曲线估计方法的影响。本研究旨在填补这一空白。通过开展大规模模拟研究，系统评估不同情境下（涵盖多样化数据生成机制与协变量效应）ROC曲线估计模型在截断值估计中的表现。同时，基于阿尔茨海默病神经影像学倡议数据集，评估不同生物标志物在诊断阿尔茨海默病中的性能，并确定适用的最优截断值。