Statistical Problems in Multivariate Survival Analysis

描述（由申请人提供）：该项目将开发和调查 分析心血管疾病复发事件数据的新方法， 哮喘研究和其他生物医学研究。该提案描述了四个 项目 第一个项目涉及多类型经常项目的统计推断 事件数据。参数估计将基于估计方程 approach.为了提高效率，加权估计方程将是 权重与受试者内协方差成反比; 将开发参数和非参数相关估计量。 推理将基于多元中心极限定理和现代 经验过程理论渐近和有限样本性质将是 考察所提出的方法将用于分析数据，从临床 左心室功能不全患者的回顾性队列研究 儿童哮喘 第二个项目考虑加速失效时间边际均值模型 分析截尾递归的条件乘均值模型 允许终止事件的事件数据。参数估计将是 基于估计方程方法。的优势和劣势 提出的方法将通过理论研究进行严格审查， 仿真研究来自肾移植患者的研究数据将被 使用所提出的方法进行分析。 第三个项目涉及边际和条件手段模型， 删失的复发事件数据，包括终止事件和 相关删失参数估计将通过 估计方程方法，基于多元中心 极限定理和经验过程。渐近和有限样本性质 将被审查。所提出的方法将被应用于分析数据从一个 透析患者的临床研究。 第四个项目研究了删失常返的可加均值模型 事件数据。我们将提出适用于以下情况的估计方法： 仅独立删失的复发性事件，终末事件， 独立删失，以及终末事件和相关删失。 渐近和有限样本性质将被检查。数据集从 哮喘、透析和肾移植研究将使用 建议的方法。

DESCRIPTION (provided by applicant): This project will develop and investigate new methodology for analyzing recurrent event data from cardiovascular disease, asthma study and other biomedical research. The proposal describes four projects. The first project concerns statistical inferences for multiple-type recurrent events data. Parameter estimation will be based on an estimating equations approach. To increase efficiency, weighted estimating equations will be developed with weights inversely proportional to intra-subject covariance; parametric and non-parametric correlation estimators will be developed. Inference will be based on the multivariate central limit theorem and modern empirical processes theory. Asymptotic and finite sample properties will be examined. The proposed methods will be used to analyze data from a clinical study of left ventricular dysfunction patients and a retrospective cohort study of childhood asthma. The second project considers an accelerated failure time marginal means model and conditional multiplicative means model for analyzing censored recurrent event data which allow for terminating events. Parameter estimation will be based on an estimating equation approach. The strengths and weaknesses of the proposed method will be critically examined via theoretical investigations and simulation studies. Data from a study of kidney transplant patients will be analyzed using the proposed methods. The third project concerns marginal and conditional means models for analyzing censored recurrent event data, which accommodate both terminating events and dependent censoring. Parameter estimation will be conducted through an estimating equation approach, with inference based on the multivariate central limit theorem and empirical processes. Asymptotic and finite sample properties will be examined. Methods proposed will be applied to analyze data from a clinical study of dialysis patients. The fourth project investigates additive means models for censored recurrent event data. We will propose methods of estimation, which are applicable for recurrent events with independent censoring only, with terminal events and independent censoring, and with both terminal events and dependent censoring. Asymptotic and finite sample properties will be examined. Data sets from asthma, dialysis and renal transplant studies will be analyzed using the proposed methods.