Analysis of high dimensional gene data combining correlation principal component regression and additive risk model

One problem of interest is to relate genes to survival outcomes of patients for the purpose of building regression models to predict future patientspsila survival based on their gene expression data. Applying semiparametric additive risk model of survival analysis, we propose a new approach to conduct the analysis of gene expression data with the focus on modelpsilas predictive ability. The method modifies the correlation principal component regression to handle the censoring problem of survival data. In addition, 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 that are related to the disease. Finally, this proposed approach is illustrated by the diffuse large B-cell lymphoma (DLBCL) data set. The results show that the model fits the data set very well.