Dataset for: Estimation of smooth ROC curves for biomarkers with limits of detection

Protein biomarkers found in plasma are commonly used for cancer screening and early detection. Measurements obtained by such markers are often based on different assays that may not support detection of accurate measurements due to a limit of detection (LOD). The $ROC$ curve is the most popular statistical tool for the evaluation of a continuous biomarker. However, in situations where LODs exist, the empirical $ROC$ curve fails to provide a valid estimate for the whole spectrum of the false positive rate (FPR). Hence, crucial information regarding the performance of the marker in high sensitivity and/or high specificity values is not revealed. In this paper, we address this problem and propose methods for constructing $ROC$ curve estimates for all possible $FPR$ values. We explore flexible parametric methods, transformations to normality, and robust kernel-based and spline-based approaches. We evaluate our methods though simulations and illustrate them in colorectal and pancreatic cancer data.

血浆中检出的蛋白质生物标志物（Protein biomarkers）常被用于癌症筛查与早期检测。此类生物标志物的检测结果通常依托多种检测实验，但由于检测限（Limit of Detection, LOD）的限制，这些实验往往无法获取准确的测量值。受试者工作特征曲线（Receiver Operating Characteristic Curve, ROC）是评估连续型生物标志物最主流的统计工具。然而，当存在检测限时，经验ROC曲线无法针对假阳性率（False Positive Rate, FPR）的全范围给出有效的估计结果。因此，该生物标志物在高灵敏度及/或高特异性水平下的性能相关关键信息无法被有效揭示。针对该问题，本文提出了可针对所有可能假阳性率值构建ROC曲线估计的方法。本文探讨了灵活参数化方法、正态性转换方法，以及基于核函数与样条的稳健建模方法。我们通过模拟实验对所提方法进行了评估，并以结直肠癌与胰腺癌数据为例展示了方法的应用效果。