Abstract：
Biomarkers are key components of personalized medicine. In this paper, we consider biomarkers taking continuous values that are associated with disease status, called case and control. The performance of such a biomarker is evaluated by the area under the curve (AUC) of its receiver operating characteristic curve. Oftentimes, two biomarkers are collected from each subject to test if one has a larger AUC than the other. We propose a simple non-parametric statistical test for comparing the performance of two biomarkers. We also present a simple sample size calculation method for this test statistic. Our sample size formula requires specification of AUC values (or the standardized effect size of each biomarker between cases and controls together with the correlation coefficient between two biomarkers), prevalence of cases in the study population, type I error rate, and power. Through simulations, we show that the testing on two biomarkers controls type I error rate accurately and the proposed sample size closely maintains specified statistical power.
摘要：
生物标志物是个性化医疗的关键组成部分。在本文中,我们认为生物标志物采用与疾病状态相关的连续值，调用案例和控制。通过其接受者工作特征曲线的曲线下面积(AUC)来评估这种生物标志物的性能。通常,从每个受试者中收集两种生物标志物以测试其中一个的AUC是否大于另一个。我们提出了一种简单的非参数统计检验来比较两种生物标志物的性能。我们还为该检验统计量提供了一种简单的样本量计算方法。我们的样本量公式要求规范AUC值（或病例和对照之间每种生物标志物的标准化效应大小以及两种生物标志物之间的相关系数），研究人群中的病例患病率，I型错误率，和权力。通过模拟,我们表明,对两种生物标志物的检测准确地控制了I型错误率,并且所提出的样本大小紧密地保持了指定的统计功效.
