Analysis of additive risk model with high-dimensional covariates using partial least squares |
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Authors: | Zhao Yichuan Zhou Yue Zhao Meng |
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Institution: | Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303, USA. dz2007@gmail.com |
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Abstract: | In this paper, we construct a partial additive regression (PAR) model to predict the survival times of cancer patients based on microarray gene expression data with right censoring. The area under time-dependent receiver operating characteristic curve is used as a model evaluation criterion. We conduct a simulation study to compare the proposed method with other methods, i.e. partial Cox regression and supervised principal component analysis. Two data sets of breast cancer and diffuse large B-cell lymphoma are analyzed to illustrate our procedure. The outcome indicates great predictive performance on both dimension reduction and predictive performance of the proposed PAR model. |
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Keywords: | partial least squares right censoring gene expression additive risk model |
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