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用于提高对未来观察结果的诊断/预后准确性的线性组合方法。

Linear combination methods to improve diagnostic/prognostic accuracy on future observations.

作者信息

Kang Le, Liu Aiyi, Tian Lili

机构信息

Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA.

Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, USA.

出版信息

Stat Methods Med Res. 2016 Aug;25(4):1359-80. doi: 10.1177/0962280213481053. Epub 2013 Apr 16.

Abstract

Multiple diagnostic tests or biomarkers can be combined to improve diagnostic accuracy. The problem of finding the optimal linear combinations of biomarkers to maximise the area under the receiver operating characteristic curve has been extensively addressed in the literature. The purpose of this article is threefold: (1) to provide an extensive review of the existing methods for biomarker combination; (2) to propose a new combination method, namely, the nonparametric stepwise approach; (3) to use leave-one-pair-out cross-validation method, instead of re-substitution method, which is overoptimistic and hence might lead to wrong conclusion, to empirically evaluate and compare the performance of different linear combination methods in yielding the largest area under receiver operating characteristic curve. A data set of Duchenne muscular dystrophy was analysed to illustrate the applications of the discussed combination methods.

摘要

多种诊断测试或生物标志物可以结合起来以提高诊断准确性。在文献中,寻找生物标志物的最佳线性组合以最大化受试者工作特征曲线下面积的问题已得到广泛探讨。本文的目的有三个:(1)对现有的生物标志物组合方法进行全面综述;(2)提出一种新的组合方法,即非参数逐步法;(3)使用留一配对交叉验证法,而不是过度乐观且可能导致错误结论的重新代入法,来实证评估和比较不同线性组合方法在获得最大受试者工作特征曲线下面积方面的性能。分析了一组杜氏肌营养不良症数据集以说明所讨论的组合方法的应用。

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