Semi-parametric Methods for Multivariate Survival Data

点击翻译按钮获取中文摘要

DESCRIPTION (provided by applicant): This project will develop and investigate new methodology for analyzing multivariate censored failure time data from cardiovascular disease, cancer and other biomedical research. The proposal describes four projects. The first project deals with nonparametric modeling of covariate effects with multivariate failure time data. Semi-parametric methods for inferences will be developed for the partially linear model and varying-coefficients model. Asymptotic and finite sample properties of the proposed statistical methods will be studied. Data from Cancer Risk in Uranium Miners and Collaborative Perinatal Project will be analyzed using the proposed methods. The second project considers the problem of hypothesis testing for non-linear covariate effects with Generalized pseudo-partial likelihood ratio procedure for testing parametric versus non-parametric and linear versus non-linear hypothesis for the partially linear model and the varying-coefficients model will be developed. Statistical properties of the proposed procedures will be studied. The proposed methodologies will be applied to data from Collaborative Perinatal Project, Cancer Risk in Uranium Miners, and the Family Study of the Collaborative Lipid Research Clinics Program (LRC). The third project concerns model selection techniques for multivariate failure time data. A penalized pseudo-partial likelihood method will be developed for model and variable selection. Asymptotic and finite sample properties will be investigated. Analysis of data from the Family Study of the Collaborative Lipid Research Clinics Program (LRC) and the Framingham Heart Study will be performed. The fourth project concerns statistical inferences for multi-type recurrent events data. An estimating equation approach is proposed for estimating the mean ratio parameters in marginal means models for multi-type recurrent events data; a semi-parametric method for inferences about non-linear covariates effects in the marginal means model will be studied; and generalized partial likelihood ratio tests and penalized pseudo-partial likelihood method will be investigated for such data. Asymptotic and finite sample properties will be investigated. Analysis of data from the Studies of Left Ventricular Dysfunction will be performed.