Department of Biostatistics, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, USA.
Stat Methods Med Res. 2021 Apr;30(4):1101-1118. doi: 10.1177/0962280220987587. Epub 2021 Feb 1.
Either in clinical study or biomedical research, it is a common practice to combine multiple biomarkers to improve the overall diagnostic performance. Despite the fact there exist a large number of statistical methods for biomarker combination under binary classification, research on this topic under multi-class setting is sparse. The overall diagnostic accuracy, i.e. the sum of correct classification rates, directly measures the classification accuracy of the combined biomarkers. Hence the overall accuracy can serve as an important objective function for biomarker combination, especially when the combined biomarkers are used for the purpose of making medical diagnosis. In this paper, we address the problem of combining multiple biomarkers to directly maximize the overall diagnostic accuracy by presenting several grid search methods and derivation-based methods. A comprehensive simulation study was conducted to compare the performances of these methods. An ovarian cancer data set is analyzed in the end.
无论是在临床研究还是生物医学研究中，将多个生物标志物结合起来以提高整体诊断性能都是一种常见的做法。尽管在二进制分类下存在大量用于生物标志物组合的统计方法，但在多类设置下对该主题的研究却很少。整体诊断准确性，即正确分类率之和，直接衡量组合生物标志物的分类准确性。因此，整体准确性可以作为生物标志物组合的一个重要目标函数，特别是当组合生物标志物用于医疗诊断目的时。在本文中，我们通过提出几种网格搜索方法和基于推导的方法来解决通过直接最大化整体诊断准确性来组合多个生物标志物的问题。最后，进行了全面的模拟研究来比较这些方法的性能。分析了一个卵巢癌数据集。
