Biomarker selection for medical diagnosis using the partial area under the ROC curve

被引：29
|
作者：
Hsu M.-J. [1 ]
Chang Y.-C.I. [1 ,2 ]
Hsueh H.-M. [2 ]
机构：
[1] Institute of Statistical Science, Academia Sinica
[2] Department of Statistics, National ChengChi University
来源：
BMC Research Notes | / 7卷 / 1期
关键词：
Discriminatory power; Hypothesis testing; Optimal linear combination; Partial area under ROC curve; Stepwise biomarker selection;
D O I：
10.1186/1756-0500-7-25
中图分类号：
学科分类号：
摘要：
Background: A biomarker is usually used as a diagnostic or assessment tool in medical research. Finding an ideal biomarker is not easy and combining multiple biomarkers provides a promising alternative. Moreover, some biomarkers based on the optimal linear combination do not have enough discriminatory power. As a result, the aim of this study was to find the significant biomarkers based on the optimal linear combination maximizing the pAUC for assessment of the biomarkers. Methods. Under the binormality assumption we obtain the optimal linear combination of biomarkers maximizing the partial area under the receiver operating characteristic curve (pAUC). Related statistical tests are developed for assessment of a biomarker set and of an individual biomarker. Stepwise biomarker selections are introduced to identify those biomarkers of statistical significance. Results: The results of simulation study and three real examples, Duchenne Muscular Dystrophy disease, heart disease, and breast tissue example are used to show that our methods are most suitable biomarker selection for the data sets of a moderate number of biomarkers. Conclusions: Our proposed biomarker selection approaches can be used to find the significant biomarkers based on hypothesis testing. © 2014 Hsu et al.; licensee BioMed Central Ltd.
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