Date of Award
4-22-2008
Degree Type
Closed Thesis
Degree Name
Master of Science (MS)
Department
Mathematics and Statistics
First Advisor
Yichuan Zhao - Chair
Second Advisor
Yu-Sheng Hsu
Third Advisor
Jiawei(Jay) Liu
Fourth Advisor
Xu Zhang
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.
DOI
https://doi.org/10.57709/1059707
Recommended Citation
Wang, Guoshen, "Analysis of Additive Risk Model with High Dimensional Covariates Using Correlation Principal Component Regression." Thesis, Georgia State University, 2008.
doi: https://doi.org/10.57709/1059707