Biostatistics and Bioinformatics Branch, Division of Epidemiology, Statistics & Prevention Research, NIH/NICHD, 6100 Executive Blvd., Bethesda, MD 20892, USA.
Acad Radiol. 2013 Jul;20(7):874-82. doi: 10.1016/j.acra.2013.03.009.
Receiver operating characteristic (ROC) analysis is often used to find the optimal combination of biomarkers. When the subject level covariates affect the magnitude and/or accuracy of the biomarkers, the combination rule should take into account of the covariate adjustment. The authors propose two new biomarker combination methods that make use of the covariate information.
The first method is to maximize the area under the covariate-adjusted ROC curve (AAUC). To overcome the limitations of the AAUC measure, the authors further proposed the area under covariate-standardized ROC curve (SAUC), which is an extension of the covariate-specific ROC curve. With a series of simulation studies, the proposed optimal AAUC and SAUC methods are compared with the optimal AUC method that ignores the covariates. The biomarker combination methods are illustrated by an example from Alzheimer's disease research.
The simulation results indicate that the optimal AAUC combination performs well in the current study population. The optimal SAUC method is flexible to choose any reference populations, and allows the results to be generalized to different populations.
The proposed optimal AAUC and SAUC approaches successfully address the covariate adjustment problem in estimating the optimal marker combination. The optimal SAUC method is preferred for practical use, because the biomarker combination rule can be easily evaluated for different population of interest.
受试者工作特征（ROC）分析常用于寻找生物标志物的最佳组合。当个体水平的协变量影响生物标志物的幅度和/或准确性时，组合规则应考虑协变量调整。作者提出了两种新的利用协变量信息的生物标志物组合方法。
第一种方法是最大化协变量调整的 ROC 曲线下面积（AAUC）。为了克服 AAUC 度量的局限性，作者进一步提出了协变量标准化 ROC 曲线下面积（SAUC），这是协变量特异性 ROC 曲线的扩展。通过一系列模拟研究，将提出的最优 AAUC 和 SAUC 方法与忽略协变量的最优 AUC 方法进行了比较。通过阿尔茨海默病研究的一个例子说明了生物标志物组合方法。
模拟结果表明，最优 AAUC 组合在当前研究人群中表现良好。最优 SAUC 方法灵活，可选择任何参考人群，并允许将结果推广到不同的人群。
提出的最优 AAUC 和 SAUC 方法成功地解决了在估计最优标记组合时的协变量调整问题。最优 SAUC 方法更适合实际使用，因为可以轻松评估不同感兴趣人群的生物标志物组合规则。
