Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Department of Biostatistics, University of Washington, Seattle, WA, USA.
Biom J. 2021 Aug;63(6):1223-1240. doi: 10.1002/bimj.202000210. Epub 2021 Apr 19.
Biomarkers abound in many areas of clinical research, and often investigators are interested in combining them for diagnosis, prognosis, or screening. In many applications, the true positive rate (TPR) for a biomarker combination at a prespecified, clinically acceptable false positive rate (FPR) is the most relevant measure of predictive capacity. We propose a distribution-free method for constructing biomarker combinations by maximizing the TPR while constraining the FPR. Theoretical results demonstrate desirable properties of biomarker combinations produced by the new method. In simulations, the biomarker combination provided by our method demonstrated improved operating characteristics in a variety of scenarios when compared with alternative methods for constructing biomarker combinations. Thus, use of our method could lead to the development of better biomarker combinations, increasing the likelihood of clinical adoption.
生物标志物在临床研究的许多领域都有广泛应用，研究人员通常有兴趣将它们组合起来用于诊断、预后或筛查。在许多应用中，在规定的可接受的假阳性率 (FPR) 下，生物标志物组合的真阳性率 (TPR) 是预测能力的最相关度量标准。我们提出了一种无分布的方法，通过最大化 TPR 同时约束 FPR 来构建生物标志物组合。理论结果证明了新方法产生的生物标志物组合的理想特性。在模拟中，与构建生物标志物组合的其他方法相比，我们的方法提供的生物标志物组合在各种情况下均表现出更好的操作特性。因此，使用我们的方法可以开发出更好的生物标志物组合，从而增加临床应用的可能性。
