Variable selection using the optimal ROC curve: An application to a traditional Chinese medicine study on osteoporosis disease

Authors

  • X. H. Zhou,

    1. School of Statistics, Renmin University, Beijing 100872, People's Republic of China
    2. HSR&D Center of Excellence, VA Puget Sound Health Care System, Seattle, WA 98101, U.S.A.
    3. Department of Biostatistics, University of Washington, Seattle, WA 98195, U.S.A.
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  • B. Chen,

    1. Department of Biostatistics, University of Washington, Seattle, WA 98195, U.S.A.
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  • Y. M. Xie,

    Corresponding author
    1. Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, People's Republic of China
    • Y. M. Xie, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, People's Republic of China.

      F. Tian, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, People's Republic of China.

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  • F. Tian,

    Corresponding author
    1. Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, People's Republic of China
    • Y. M. Xie, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, People's Republic of China.

      F. Tian, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, People's Republic of China.

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  • H. Liu,

    1. Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, People's Republic of China
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  • X. Liang

    1. School of Statistics, Renmin University, Beijing 100872, People's Republic of China
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Abstract

In biomedical studies, there are multiple sources of information available of which only a small number of them are associated with the diseases. It is of importance to select and combine these factors that are associated with the disease in order to predict the disease status of a new subject. The receiving operating characteristic (ROC) technique has been widely used in disease classification, and the classification accuracy can be measured with area under the ROC curve (AUC). In this article, we combine recent variable selection methods with AUC methods to optimize diagnostic accuracy of multiple risk factors. We first describe one new and some recent AUC-based methods for effectively combining multiple risk factors for disease classification. We then apply them to analyze the data from a new clinical study, investigating whether a combination of traditional Chinese medicine symptoms and standard Western medicine risk factors can increase discriminative accuracy in diagnosing osteoporosis (OP). Based on the results, we conclude that we can make a better diagnosis of primary OP by combining traditional Chinese medicine symptoms with Western medicine risk factors. Copyright © 2011 John Wiley & Sons, Ltd.

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