Statistical Problems in Multivariate Survival Analysis

描述(由申请人提供)：该项目将进行开发和调查 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。该提案描述了四个方面 项目。 第一个项目涉及多种类型经常性的统计推断。 事件数据。参数估计将基于估计方程 接近。为了提高效率，加权估计方程将被 权重与受试者内部协方差成反比； 将开发参数和非参数相关估计器。 推理将基于多元中心极限定理和现代 经验过程理论。渐近和有限样本属性将是 检查过了。建议的方法将被用于分析来自临床的数据 左心功能不全患者的研究和一项回顾性队列研究 儿童哮喘的症状。 第二个项目考虑了加速失效时间边际均值模型。 删失回归分析的条件乘法均值模型 允许终止事件的事件数据。参数估计将是 基于估计方程式的方法。的优势和劣势 建议的方法将通过理论研究和 模拟研究。来自肾移植患者的研究数据将是 利用提出的方法进行了分析。 第三个项目涉及用于分析的边际均值模型和条件均值模型 被审查的重复事件数据，既包括终止事件，也包括 从属审查。参数估计将通过一个 基于多元中心的推论估计方程方法 极限定理和经验过程。渐近和有限样本性质 将会被检查。提出的方法将被应用于分析来自 透析患者的临床研究。 第四个项目研究删失回归的加性均值模型。 事件数据。我们将提出适用于以下情况的估算方法 只有独立审查的重复性事件，具有终结性事件和 独立审查，既有终端事件，也有从属审查。 我们将研究渐近和有限样本的性质。数据集来自 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.