Type of Document Master's Thesis Author Wang, Guoshen Author's Email Address gwang5@student.gsu.edu URN etd-04212008-130541 Title Analysis of Additive Risk Model with High Dimensional Covariates Using Correlation Principal Component Regression Degree Master of Science Department Mathematics and Statistics Advisory Committee
Advisor Name Title Yichuan Zhao Committee Chair Jiawei(Jay) Liu Committee Member Xu Zhang Committee Member Yu-Sheng Hsu Committee Member Keywords
- Correlation principal component regression
- Additive risk model
- Gene expression data
- Right censoring
Date of Defense 2008-04-17 Availability restricted Abstract One problem of interest is to relate genes to survival outcomes of patients for the purpose of building regression models to predict future patients¡¯ survival based on their gene expression data. Applying semeparametric additive risk model of survival analysis, this thesis proposes a new approach to conduct the analysis of gene expression data with the focus on model¡¯s predictive ability. The method modifies the correlation principal component regression to handle the censoring problem of survival data. Also, we employ the time dependent AUC and RMSEP to assess how well the model predicts the survival time. Furthermore, the proposed method is able to identify significant genes which are related to the disease. Finally, this proposed approach is illustrated by simulation data set, the diffuse large B-cell lymphoma (DLBCL) data set, and breast cancer data set. The results show that the model fits both of the data sets very well.Files
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