Grouped penalization estimation of the osteoporosis data in the traditional Chinese medicine |
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Authors: | Yang Li Yichen Qin Feng Tian |
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Institution: | 1. School of Statistics , Renmin University of China , Beijing , 100872 , People's Republic of China;2. Center for Applied Statistics , Renmin University of China , Beijing , 100872 , People's Republic of China;3. School of Public Health , Yale University , New Haven , CT , 06511 , USA;4. Department of Applied Mathematics and Statistics , Johns Hopkins University , Baltimore , MD , 21218 , USA;5. Institute of Basic Research in Clinical Medicine , China Academy of Chinese Medicine Science , Beijing , 100700 , People's Republic of China |
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Abstract: | Both continuous and categorical covariates are common in traditional Chinese medicine (TCM) research, especially in the clinical syndrome identification and in the risk prediction research. For groups of dummy variables which are generated by the same categorical covariate, it is important to penalize them group-wise rather than individually. In this paper, we discuss the group lasso method for a risk prediction analysis in TCM osteoporosis research. It is the first time to apply such a group-wise variable selection method in this field. It may lead to new insights of using the grouped penalization method to select appropriate covariates in the TCM research. The introduced methodology can select categorical and continuous variables, and estimate their parameters simultaneously. In our application of the osteoporosis data, four covariates (including both categorical and continuous covariates) are selected out of 52 covariates. The accuracy of the prediction model is excellent. Compared with the prediction model with different covariates, the group lasso risk prediction model can significantly decrease the error rate and help TCM doctors to identify patients with a high risk of osteoporosis in clinical practice. Simulation results show that the application of the group lasso method is reasonable for the categorical covariates selection model in this TCM osteoporosis research. |
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Keywords: | variable selection categorical covariates group lasso traditional Chinese medicine osteoporosis |
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