Statistical Methods for Multivariate Failure Time Data

多变量故障时间数据的统计方法

• 批准号: 9206644
• 负责人: Ross L Prentice
• 金额: $ 16.46万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-12-16 至 2018-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9206644
• 关键词: Area; Biological Markers; Blood specimen; Cessation of life; Clinical; Clinical Trials; Cohort Studies; Computer Simulation; Cox Models; Data; Data Analyses; Data Set; Dependency; Development; Dimensions; Disease; Epidemiology; Equation; Evaluation; Event; Failure; Follow-Up Studies; Genomics; Individual; Joints; Likelihood Functions; Maximum Likelihood Estimate; Measures; Methodology; Methods; Modeling; Multivariate Analysis; Outcome; Population; Prevention; Preventive Intervention; Probability; Procedures; Process; Property; Randomized Controlled Trials; Recording of previous events; Reporting; Research; Research Project Grants; Research Project Summaries; Risk; Sampling; Scheme; Science; Statistical Methods; Study Subject; Survivors; Testing; Therapeutic Intervention; Time; Treatment outcome; Women' s Health; Work; analytical tool; base; biomarker evaluation; cancer therapy; case control; cohort; comparison group; design; flexibility; follow-up; hazard; high dimensionality; method development; novel; primary outcome; research study; semiparametric; theories; tool; treatment effect

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.

项目总结 这项研究项目将开发统计方法来分析事件发生时间或故障时间数据。 主要应用领域包括随机对照试验和流行病学队列研究 癌症或其他疾病的预防或治疗。该项目的目的是开发回归方法，用于 同时分析与治疗或暴露有关的多个结果变量 在研究随访期内不断演变。要开发的方法将基于半参数 包含边际风险函数和加性半参数的COX模型的回归模型 两两相关函数和高维相关函数的回归模型。使用这些模型会导致失败 时间数据将使用Dabrowska的幸存者函数表示的多变量版本来表征。一个 基于演化故障时间历史的概率分布的最大似然方法将是 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，通过使用关于其他失败时间结果的数据来对特定的临床结果进行回归效果 提供信息审查信息。例如，在以死亡为主要结果的临床试验中，这些 方法将允许在研究对象随访期内发生严重但非致命的事件 加强初步疗效评估。新的方法也将提供一种有效的手段 评估各种结果类型的风险之间的相关性大小，以及它们与 处理或协变量。许多临床试验或队列研究应用涉及某种形式的队列 二次抽样，昂贵的生物标记物价值由原材料确定(例如，来自 血液样本)仅适用于在队列随访期间发生研究疾病和相应 不需要的“控制”。这项研究项目的第二个目标是开发有效的治疗或 对于单变量和多变量失效时间数据，在存在队列次抽样的情况下的协变量效应。 这里的方法发展也将依赖于半参数最大似然方法，与新的 包括协变量历史增量的非参数似然分量的方面 队列随访。对于单变量失效时间数据，这项工作将导致估计COX模型的函数 回归参数和用于迭代最大化的观测协变量历史参数，在嵌套情况下 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、成对和高维的组合半参数模型 对观察到的协变量历史采用完全非参数模型的相关性函数。渐近的 将使用经验过程理论开发新的估计程序的分布，以及 适度的样本特性将使用计算机模拟和应用于女性的 健康倡议和其他数据集。