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Assessing the predictive power of newly added biomarkers
Author(s) -
Wang Zhanfeng,
Luo Xiangyu,
Chang Yuanchin I.
Publication year - 2015
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201400210
Subject(s) - receiver operating characteristic , predictive power , linear discriminant analysis , measure (data warehouse) , computer science , range (aeronautics) , machine learning , artificial intelligence , statistics , data mining , pattern recognition (psychology) , mathematics , engineering , philosophy , epistemology , aerospace engineering
As medical research and technology advance, there are always new biomarkers found and predictive models proposed for improving the diagnostic performance of diseases. Therefore, in addition to the existing biomarkers and predictive models, how to assess new biomarkers becomes an important research problem. Many classification performance measures, which are usually based on the performance on the whole cut‐off values, were applied directly to this type of problems. However, in a medical diagnosis, some cut‐off points are more important, such as those points within the range of high specificity. Thus, as the partial area under the ROC curve to the area under ROC curve, we study the partial integrated discriminant improvement (pIDI) for evaluating the predictive ability of a newly added marker at a prespecified range of cut‐offs. Theoretical property of estimate of the proposed measure is reported. The performance of this new measure is then compared with that of the partial area under an ROC curve. The numerical results use synthesized are presented, and a liver cancer dataset is used for demonstration purposes.