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

被引:34
作者
Kang, Le [1 ]
Liu, Aiyi [2 ]
Tian, Lili [3 ]
机构
[1] US FDA, Ctr Devices & Radiol Hlth, Silver Spring, MD USA
[2] Eunice Kennedy Shriver Natl Inst Child Hlth & Hum, Biostat & Bioinformat Branch, Bethesda, MD USA
[3] SUNY Buffalo, Dept Biostat, 706 Kimball Tower,3435 Main St, Buffalo, NY 14214 USA
基金
美国国家卫生研究院;
关键词
Multiple biomarkers; receiver operating characteristic curve; area under the receiver operating characteristic curve; linear combination; diagnostic; prognostic accuracy; ROC CURVE INFERENCE; LOGISTIC-REGRESSION; BIOMARKERS; CLASSIFICATION; PROBABILITY; EFFICIENCY; SUBJECT; AREA;
D O I
10.1177/0962280213481053
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
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页码:1359 / 1380
页数:22
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