Type of Document Master's Thesis Author Zhou, Yue Author's Email Address yzhou11@student.gsu.edu URN etd-04082006-134858 Title Analysis of Additive Risk Model with High Dimensional Covariates Using Partial Least Squares Degree Master of Science Department Mathematics and Statistics Advisory Committee
Advisor Name Title Yichuan Zhao Committee Chair Gengsheng(jeff) Qin Committee Member Jiawei(Jay) Liu Committee Member Yu-Sheng Hsu Committee Member Keywords
- Additive risk model
- right censoring
- Partial Least Squares
- gene expression
Date of Defense 2006-03-17 Availability restricted Abstract In this thesis, we consider the problem of constructing an additive risk model based on the right censored survival data to predict the survival times of the cancer patients, especially when the dimension of the covariates is much larger than the sample size. For microarray Gene Expression data, the number of gene expression levels is far greater than the number of samples. Such ˇ°small n, large pˇ± problems have attracted researchers to investigate the association between cancer patient survival times and gene expression profiles for recent few years. We apply Partial Least Squares to reduce the dimension of the covariates and get the corresponding latent variables (components), and these components are used as new regressors to fit the extensional additive risk model. Also we employ the time dependent AUC curve (area under the Receiver Operating Characteristic (ROC) curve) to assess how well the model predicts the survival time. Finally, this approach is illustrated by re-analysis of the well known AML 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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