| 摘要(英) |
To do diagnosis based on biomarkers is an important issue in medicine study. The true-positive rate (TPR) is the probability of a patient diagnosed to be ill and the false-positive rate (FPR) is the probability of a healthy person diagnosed to be ill. Under all possible cut off points of a biomarker, the curve of TPR vs. FPR is called the Receiver Operating Characteristic curve (ROC curve) of the biomarkers. Moreover, the maximum difference between TPR and FPR, called the Youden index, denote as J. The area under the ROC curve (AUC) and the Youden index are conventionally used to evaluate the efficiency of the biomarkers. To compare the efficiency of two biomarkers that are distributed according to a bivariate normal distribution, we construct a joint confidence region for the difference of AUC and that of Youden index. When the bivariate distribution of the two biomarkers is unknown, we suggest construct a nonparametric joint confidence region for AUC-difference and J-difference based on the bootstrapping method. In spite of studying the coverage probability of the confidence regions mentioned above based on a simulation method, we also use two biomarkers, CA19-9 and CA125, to diagnose the pancreatic cancer based on the proposed confidence regions. |
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