Jung Sin-Ho
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27705, USA.
Diagnostics (Basel). 2024 Aug 20;14(16):1813. doi: 10.3390/diagnostics14161813.
Diagnostic biomarkers are key components of diagnostics. In this paper, we consider diagnostic biomarkers taking continuous values that are associated with a dichotomous disease status, called malignant or benign. The performance of such a biomarker is evaluated by the area under the curve (AUC) of its receiver operating characteristic curve. We assume that, together with the disease status, one control and multiple experimental biomarkers are collected from each subject to test if any of the experimental biomarkers have a larger AUC than the control. In this case, each experimental biomarker will be compared with the control so that a multiple testing issue is involved in the comparisons. In this paper, we propose a simple non-parametric statistical testing procedure to compare K(≥2) experimental biomarkers with a control, adjusting for the multiplicity and its sample size calculation method. Our sample size formula requires the specification of the AUC values (or the standardized effect size of each biomarker between the benign and malignant groups) together with the correlation coefficients between the biomarkers, the prevalence of the malignant group in the study population, the type I error rate, and the power. Through simulations, we show that the statistical test controls the overall type I error rate accurately and the proposed sample size closely maintains the specified statistical power.
诊断生物标志物是诊断的关键组成部分。在本文中，我们考虑取值为连续型且与二分疾病状态（称为恶性或良性）相关的诊断生物标志物。此类生物标志物的性能通过其接收者操作特征曲线的曲线下面积（AUC）来评估。我们假设，除了疾病状态外，还从每个受试者收集了一个对照生物标志物和多个实验生物标志物，以检验是否有任何实验生物标志物的AUC大于对照生物标志物。在这种情况下，每个实验生物标志物都将与对照进行比较，因此在比较中涉及多重检验问题。在本文中，我们提出了一种简单的非参数统计检验程序，用于将K（≥2）个实验生物标志物与一个对照进行比较，并对多重性及其样本量计算方法进行调整。我们的样本量公式需要指定AUC值（或良性和恶性组之间每个生物标志物的标准化效应大小），以及生物标志物之间的相关系数、研究人群中恶性组的患病率、I型错误率和检验效能。通过模拟，我们表明该统计检验能够准确控制总体I型错误率，并且所提出的样本量能紧密维持指定的统计检验效能。
