The receiver operating characteristic (ROC) curve and its summary measure, the Area Under the Curve (AUC), are well-established tools for evaluating the efficacy of biomarkers in biomedical studies. Compared to the traditional ROC curve, the covariate-adjusted ROC curve allows for individual evaluation of the biomarker. However, the use of machine learning models has rarely been explored in this context, despite their potential to develop more powerful and sophisticated approaches for biomarker evaluation. The goal of this paper is to propose a framework for neural network-based covariate-adjusted ROC modeling that allows flexible and nonlinear evaluation of the effectiveness of a biomarker to discriminate between two reference populations. The finite-sample performance of our method is investigated through extensive simulation tests under varying dependency structures between biomarkers, covariates, and referenced populations. The methodology is further illustrated in a clinically case study that assesses daily physical activity - measured as total activity time (TAC), a proxy for daily step count-as a biomarker to predict mortality at three, five and eight years. Analyzes stratified by sex and adjusted for age and BMI reveal distinct covariate effects on mortality outcomes. These results underscore the importance of covariate-adjusted modeling in biomarker evaluation and highlight TAC's potential as a functional capacity biomarker based on specific individual characteristics.

翻译：受试者工作特征（ROC）曲线及其概括性指标——曲线下面积（AUC），是生物医学研究中评估生物标志物效能的成熟工具。相较于传统ROC曲线，协变量调整ROC曲线允许对生物标志物进行个体化评估。然而，尽管机器学习模型有望为生物标志物评估提供更强大、更复杂的方法，但在此背景下的应用仍鲜有探索。本文旨在提出一个基于神经网络的协变量调整ROC建模框架，该框架能够灵活且非线性地评估生物标志物区分两个参照人群的有效性。我们通过在不同生物标志物、协变量与参照人群间依赖结构下的大量模拟实验，检验了所提方法在有限样本下的性能。该方法进一步在一个临床案例研究中得到应用，该研究评估每日体力活动——以总活动时间（TAC，作为每日步数的替代指标）衡量——作为预测三年、五年和八年死亡率的生物标志物。按性别分层并调整年龄和BMI的分析揭示了协变量对死亡率结果的不同影响。这些结果强调了协变量调整建模在生物标志物评估中的重要性，并凸显了TAC基于特定个体特征作为功能能力生物标志物的潜力。