Department of Biostatistics, University at Buffalo, Buffalo, New York, USA.
Pharm Stat. 2024 Nov-Dec;23(6):984-1030. doi: 10.1002/pst.2413. Epub 2024 Jul 7.
In practice, we often encounter binary classification problems where both main classes consist of multiple subclasses. For example, in an ovarian cancer study where biomarkers were evaluated for their accuracy of distinguishing noncancer cases from cancer cases, the noncancer class consists of healthy subjects and benign cases, while the cancer class consists of subjects at both early and late stages. This article aims to provide a large number of optimal cut-point selection methods for such setting. Furthermore, we also study confidence interval estimation of the optimal cut-points. Simulation studies are carried out to explore the performance of the proposed cut-point selection methods as well as confidence interval estimation methods. A real ovarian cancer data set is analyzed using the proposed methods.
在实践中，我们经常遇到这样的二分类问题，即两个主类都包含多个子类。例如，在一项卵巢癌研究中，评估了生物标志物对区分非癌症病例和癌症病例的准确性，其中非癌症类包括健康受试者和良性病例，而癌症类包括早期和晚期病例。本文旨在为这种情况提供大量最佳切点选择方法。此外，我们还研究了最优切点的置信区间估计。通过模拟研究来探讨所提出的切点选择方法以及置信区间估计方法的性能。使用提出的方法分析了一个真实的卵巢癌数据集。
