STATISTICAL PROBLEMS IN MULTIVARIATE SURVIVAL ANALYSIS

描述：这项拨款申请的目的是开发新的 多变量截尾生存分析的统计方法 数据，并将这些方法应用于心血管疾病的数据 研究。该提案描述了四个项目。第一个项目成交 具有随机簇(即，将簇视为随机样本 来自这种集群的底层人口)。潜变量模型是 为这类数据提出的。提出了一种伪似然方法。 估计主要研究变量的平均效应。渐近的 我们将研究所建议的统计方法的性质。软件 将会被开发出来。左心功能不全研究资料综述 (SOLVD)将使用所提出的方法进行分析。 第二个项目考虑了代理/缺失协变量的问题 生存数据。提出了一种平均分的方法来处理这些问题。 单变量和多变量生存数据的替代/缺失协变量。 将研究拟议程序的统计特性。软件 将会被开发出来。拟议的方法将应用于以下数据 美苏合作的左心功能不全研究 在区域1(动脉粥样硬化的发病机制)和家庭心脏研究中。 还将与现有程序进行比较。 第三个项目涉及具有多个级别的集群的数据。 提出了边际风险模型来分析这类数据。一种估算 提出了一种估算边际危险比的方程式方法。 参数。我们将研究其渐近性质。软件将是 来自家庭协作研究的数据的开发和分析 脂质研究诊所计划(LRC)和弗雷明翰心脏研究将 已执行。 第四个项目涉及对错误指定的 多元生存边际风险模型中的基线风险 数据。偏置和效率损失将在大范围和有限范围内进行评估 样本。协作性血脂家族研究的数据分析 研究诊所计划(LRC)和弗雷明翰心脏研究将 已执行。 最后，提出的方法的优点和缺点如下 通过模拟和理论研究进行了严格的检验。

DESCRIPTION: The purpose of this grant application is to develop new statistical methodologies for analyzing multi-variate censored survival data, and to apply these methodologies to data from cardio vascular disease research. The proposal describes four projects. The first project deals with random clusters (i.e., the clusters are considered as a random sample from an underlying population of such clusters). A latent variable model is proposed for such data. A pseudo-likelihood approach is proposed for estimating the average effects of the main study variables. Asymptotic properties of the proposed statistical methods will be studied. Software will be developed. Data from the Studies of Left Ventricular Dysfunction (SOLVD) will be analyzed using the proposed method. The second project considers the problem of surrogate/missing covariates in survival data. A mean score method is proposed to deal with these surrogate/ missing covariates for univariate and multivariate survival data. Statistical properties of the proposed procedures will be studied. Software will be developed. The proposed methodologies will be applied to data from the Studies of Left Ventricular Dysfunction, the U.S.-U.S.S.R. Collaboration in Area 1 (Pathogenesis of Atherosclerosis), and the Family Heart Study. Comparisons with existing procedures will also be made. The third project concerns data with multiple levels of clustering. Marginal hazard models are proposed for analyzing such data. An estimating equation approach is proposed for estimating the marginal hazard ratio parameters. Asymptotic properties will be investigated. Software will be developed, and 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 an evaluation of the effect of a misspecified baseline hazard in the marginal hazard models for multivariate survival data. Bias and efficiency loss will be evaluated in both large and finite samples. Analysis of data from the Family Study of Collaborative Lipid Research Clinics Program (LRC) and the Framingham Heart Study will be performed. Finally, the strengths and weaknesses of the proposed methodologies will be critically examined via simulations and theoretical investigations.