Statistical Methods for Multivariate Failure Time Data

项目摘要 该研究项目将开发用于分析事件发生时间或故障时间数据的统计方法。 主要应用领域包括随机对照试验和流行病学队列研究， 预防或治疗癌症或其他疾病。该项目旨在开发回归方法 同时分析与治疗或暴露相关的多个结局变量， 在研究随访期间不断发展。将开发的方法将基于半参数 回归模型，包括边际风险函数的考克斯模型和加性半参数 回归模型的成对和更高维的依赖函数。使用这些模型， 将使用Dabrowska幸存者函数表示的多变量版本表征时间数据。一 最大似然法，基于概率分布的演变故障时间历程，将是 用于参数估计。这项工作有可能加强对治疗效果的分析，或 更一般地，通过使用其他失效时间结局的数据， 提供信息审查信息。例如，在以死亡为主要结局的临床试验中， 方法将允许在研究受试者随访期间发生严重但非致死性事件， 加强主要疗效评价。新方法还将提供一种有效的手段， 评估各种结果类型的风险之间的依赖关系的大小，以及它们与 治疗或协变量。许多临床试验或队列研究应用涉及某种形式的队列 二次取样，从原材料中确定昂贵的生物标志物值（例如，基因组测量， 血液标本）仅用于在队列随访期间发生研究疾病的“病例”， “控制”，不。该研究项目的第二个目的是开发有效的治疗分析， 对于单变量和多变量失效时间数据，存在队列二次抽样时的协变量效应。 这里的方法开发也将依赖于半参数最大似然方法， 包括协变量历史增量的非参数似然分量， 队列随访。对于单变量失效时间数据，本文的工作将导致考克斯模型的估计函数 回归参数和迭代最大化的观测协变量历史参数，嵌套 病例对照、病例队列或更一般的抽样方案。多变量故障时间延长将 边际风险函数的联合收割机半参数模型和两两和高维半参数模型 依赖函数与观察协变量历史的完全非参数模型。渐近 新的估计程序的分布将使用经验过程理论开发， 适度的样本属性将使用计算机模拟进行评估，并使用妇女的应用程序 健康倡议和其他数据集。

PROJECT SUMMARY This research project will develop statistical methods for the analysis of time-to-event, or failure time, data. Major areas of application include randomized controlled trials and epidemiologic cohort studies for the prevention or treatment of cancer or other diseases. The project aims to develop regression methods for the simultaneous analysis of multiple outcome variables in relation to treatments or exposures that may be evolving over the study follow-up period. The methods to be developed will be based on semiparametric regression models that include Cox models for marginal hazard functions and additive semiparametric regression models for pairwise and higher dimensional dependency functions. Using these models the failure time data will be characterized using a multivariate version of Dabrowska’s survivor function representation. A maximum likelihood approach, based on the probability distribution of the evolving failure time histories, will be used for parameter estimation. The work has potential to strengthen analyses of treatment effects, or regression effects more generally, for specific clinical outcomes by using data on other failure time outcomes to provide information censoring information. For example in a clinical trial with death as primary outcome, these methods will allow the occurrence of serious, but non-fatal, events during the study subject follow-up period to strengthen primary outcome treatment evaluations. The novel methods also will provide an efficient means of assessing the magnitude of dependencies among the risks for various outcome types, and their relationship to treatments or covariates. Many clinical trials or cohort study applications involve some form of cohort subsampling, with expensive biomarker values determined from raw materials (e.g., genomic measures from blood specimens) only for ‘cases’ that develop study diseases during cohort follow-up and corresponding ‘controls’ that do not. A second aim of this research project is to develop efficient analyses of treatment or covariate effects in the presence of cohort subsampling, for both univariate and multivariate failure time data. The methods development here will also rely on semiparametric maximum likelihood methods, with the novel aspect of including a nonparametric likelihood component for covariate history increments as they evolve over cohort follow-up. With univariate failure time data this work will lead to estimating functions for Cox model regression parameters and for observed covariate history parameters for iterative maximization, under nested case-control, case-cohort, or more general sampling schemes. Multivariate failure time extensions will combine semiparametric models for marginal hazard functions and for pairwise and higher dimensional dependency functions with completely nonparametric models for observed covariate histories. Asymptotic distributions for the novel estimation procedures will be developed using empirical process theory, and moderate sample properties will be evaluated using computer simulations, and using applications to Women’s Health Initiative and other datasets.