Department of Biostatistics, University at Buffalo, Buffalo, New York, USA.
Stat Med. 2023 Dec 10;42(28):5207-5228. doi: 10.1002/sim.9908. Epub 2023 Oct 2.
"Compound multi-class classification" refers to the setting where three or more main classes are involved and at least one of the main classes have multiple subclasses. A common practice in evaluating biomarker performance under "compound multi-class classification" is "subclasses pooling." In this article, we first explore the downsides of accuracy metrics based on pooled data. Then we propose a new accuracy measure proper for "compound multi-class classification" with three ordinal main classes, namely "volume under compound surface ( )." The proposed evaluates the accuracy of a biomarker appropriately by identifying main classes without requiring specification of an ordering for marker values of subclasses within each main class. For confidence interval estimation of , both parametric and nonparametric methods are studied, and simulation studies are carried out to assess coverage probabilities. A subset of Alzheimer's Disease Neuroimaging Initiative study dataset is analyzed.
“复合多类分类”是指涉及三个或更多主类，且至少有一个主类具有多个子类的情况。在评估“复合多类分类”下的生物标志物性能时，常用的方法是“子类合并”。在本文中，我们首先探讨了基于合并数据的准确率度量的缺点。然后，我们提出了一种新的适合于三个有序主类的“复合多类分类”的准确率度量方法，即“复合曲面下的体积（ ）”。所提出的 方法通过识别主类，而无需指定每个主类中子类标记值的顺序，从而适当地评估生物标志物的准确性。对于 的置信区间估计，研究了参数和非参数方法，并进行了模拟研究以评估覆盖率概率。分析了阿尔茨海默病神经影像学倡议研究数据集的一个子集。
