Samawi Hani, Chen Ding-Geng, Yin Jingjing, Alsharman Marwan
Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA.
College of Health Solutions, Arizona State University, Phoenix, AZ, USA.
J Appl Stat. 2022 Oct 26;51(3):497-514. doi: 10.1080/02664763.2022.2137478. eCollection 2024.
In medical diagnostic research, it is customary to collect multiple continuous biomarker measures to improve the accuracy of diagnostic tests. A prevalent practice is to combine the measurements of these biomarkers into one single composite score. However, incorporating those biomarker measurements into a single score depends on the combination of methods and may lose vital information needed to make an effective and accurate decision. Furthermore, a diagnostic cut-off is required for such a combined score, and it is difficult to interpret in actual clinical practice. The paper extends the classical biomarkers' accuracy and predictive values from univariate to bivariate markers. Also, we will develop a novel pseudo-measures system to maximize the vital information from multiple biomarkers. We specified these pseudo-and-or classifiers for the true positive rate, true negative rate, false-positive rate, and false-negative rate. We used them to redefine classical measures such as the Youden index, diagnostics odds ratio, likelihood ratios, and predictive values. We provide optimal cut-off point selection based on the modified Youden index with numerical illustrations and real data analysis for this paper's newly developed pseudo measures.
在医学诊断研究中，习惯收集多个连续生物标志物测量值以提高诊断测试的准确性。一种普遍的做法是将这些生物标志物的测量值合并为一个单一的综合评分。然而，将这些生物标志物测量值纳入单一评分取决于组合方法，并且可能会丢失做出有效和准确决策所需的重要信息。此外，这样的综合评分需要一个诊断临界值，而在实际临床实践中很难解释。本文将经典生物标志物的准确性和预测值从单变量扩展到双变量标志物。此外，我们将开发一种新颖的伪测量系统，以最大限度地利用来自多个生物标志物的重要信息。我们为真阳性率、真阴性率、假阳性率和假阴性率指定了这些伪与或分类器。我们用它们重新定义了经典指标，如尤登指数、诊断比值比、似然比和预测值。我们基于修改后的尤登指数提供最佳临界值选择，并通过数值示例和实际数据分析对本文新开发的伪测量进行说明。
