Evaluation of diagnostic biomarkers: A comparative analysis by area under the receiver operating characteristic curve

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference Pub Date : 2025-08-27 DOI:10.1016/j.jspi.2025.106336

Pengfei Liu , Kai Lou , Yangchun Zhang , Peng Zhao , Wang Zhou

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引用次数: 0

Abstract

In recent years, a substantial of biomarkers have surfaced to facilitate the prompt diagnosis and intervention of chronic kidney disease. However, the lack of a reliable approach to compare biomarker efficacy poses a significant challenge in clinical practice and biomedical research. The inability to accurately assess biomarkers’ performance limits their utility in disease diagnosis. In this article, we study the efficiency of different diagnostic markers by comparing the areas under the receiver operating characteristic curves of markers, which are estimated via the Wilcoxon–Mann–Whitney statistics. Furthermore, the precision of interval estimation was enhanced through the implementation of the Edgeworth expansion and bootstrap approximation on the statistics. By performing numerical simulations, we have demonstrated that our improved methods exhibit superior accuracy in constructing confidence intervals when compared to the traditional normal approximation method.

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诊断性生物标志物的评价：通过受试者工作特征曲线下面积的比较分析

近年来，大量的生物标志物已经浮出水面，以促进慢性肾脏疾病的及时诊断和干预。然而，缺乏一种可靠的方法来比较生物标志物的疗效，这对临床实践和生物医学研究构成了重大挑战。无法准确评估生物标志物的性能限制了它们在疾病诊断中的应用。在本文中，我们通过比较标记的接受者工作特征曲线下的面积来研究不同诊断标记的效率，这些标记是通过Wilcoxon-Mann-Whitney统计估计的。此外，通过对统计量进行Edgeworth展开和自举逼近，提高了区间估计的精度。通过进行数值模拟，我们已经证明，与传统的正态近似方法相比，我们改进的方法在构建置信区间方面具有更高的准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Statistical Planning and Inference 数学-统计学与概率论

CiteScore

2.10

自引率

11.10%

发文量

审稿时长

3-6 weeks

期刊介绍： The Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical aspects of statistics and probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing the subject. While we maintain our traditional strength in statistical inference, design, classical probability, and large sample methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians, and scientists. We publish high quality articles in all branches of statistics, probability, discrete mathematics, machine learning, and bioinformatics. We also especially welcome well written and up to date review articles on fundamental themes of statistics, probability, machine learning, and general biostatistics. Thoughtful letters to the editors, interesting problems in need of a solution, and short notes carrying an element of elegance or beauty are equally welcome.