Semi-parametric Methods for Multivariate Survival Data

描述（由申请人提供）：该项目将开发和调查 分析多变量截尾失效时间数据的新方法 心血管疾病、癌症和其他生物医学研究。 该提案描述了四个项目。第一个项目涉及 具有多变量失效时间的协变量效应的非参数建模 数据半参数方法的推论将制定为 部分线性模型和变系数模型。渐近有限 将研究拟议统计方法的样本特性。数据 铀矿工癌症风险和围产期合作项目将是 使用所提出的方法进行分析。 第二个项目考虑了非线性假设检验问题 协变量效应与广义伪偏似然比程序 用于检验参数与非参数以及线性与非线性 部分线性模型和变系数模型的假设 将被开发。建议程序的统计特性将是 研究了所提出的方法将应用于来自协作的数据 铀矿工人的围产期计划、癌症危险性和 合作脂质研究临床计划（LRC）。 第三个项目涉及多变量失效的模型选择技术 时间数据一个惩罚的伪偏似然方法将被开发为 模型和变量选择。渐近和有限样本性质将是 研究了协作血脂家系研究资料分析 临床研究计划（LRC）和心脏病研究将在 执行。 第四个项目是关于多类型经常项目的统计推断 事件数据。提出了一种估计方程的方法来估计 多类复发事件边际均值模型中的均值比参数 数据.非线性协变量推断的半参数法 将研究边际均值模型中的效应; 似然比检验和惩罚伪偏似然方法将是 调查这些数据。渐近和有限样本性质将是 研究了左室功能不全研究资料分析 将被执行。

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.