title: Analysis of additive risk model with high-dimensional covariates using partial least squares.
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.
authors.affiliation: Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303, USA. dz2007@gmail.com
authors.forename: Yichuan
authors.initials: Y
authors.lastname: Zhao
authors.forename: Yue
authors.initials: Y
authors.lastname: Zhou
authors.forename: Meng
authors.initials: M
authors.lastname: Zhao
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics / Q000401:mortality; D015870:Gene Expression; D006801:Humans; D016403:Lymphoma, Large B-Cell, Diffuse / Q000235:genetics / Q000401:mortality; D015233:Models, Statistical; D011379:Prognosis; D012306:Risk; D016019:Survival Analysis