Treatment Comparison and Estimation of Relative Risks with Failure Time Data

使用故障时间数据进行治疗比较和相对风险估计

• 批准号: 8081217
• 负责人: Jianguo TONY Sun
• 金额: $ 22.13万
• 依托单位: UNIVERSITY OF MISSOURI-COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2014-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8081217
• 关键词: Address; Affect; Attention; Beryllium; Clinical Trials; Data; Development; Disease; Drug Industry; Face; Failure; Goals; Government Agencies; Literature; Medical; Methods; Pharmacologic Substance; Play; Procedures; Progression-Free Survivals; Randomized Clinical Trials; Regression Analysis; Relative Risks; Research; Research Design; Research Personnel; Right-On; Role; Sample Size; Statistical Methods; Study Subject; Testing; The Sun; Time; base; design; interest; oncology; public health relevance; response; tool

DESCRIPTION (provided by applicant): The statistical analysis of failure time data has been the cornerstone and plays an essential role in the design and analysis of various medical studies such as randomized clinical trials. A key feature of failure time data that separates them from all other types of data is censoring, which makes the analysis of failure time data unique and difficult and can occur in different forms. This proposal will investigate two fundamental problems in medical studies, treatment comparison and estimation of relative risks, with respect to several types of failure time data that involve interval-censoring or interval-censored failure time data. The analysis of such data has been attracting more and more attention among medical investigators and statistician including these in government agencies and pharmaceutical companies. Specifically, the proposed research consists of three aims and they are the development of appropriate and/or efficient statistical procedures for 1) treatment comparison and sample size calculation, 2) estimation of relative risks and regression analysis I, and 3) estimation of relative risks and regression analysis II. It is well-known that treatment comparison is perhaps the most basic and commonly required task in medical studies and for the design of such studies, a key element is the determination of the required sample size. In the case of interval-censored data, some comparison procedures have been proposed. However, most of them are limited in applications and more importantly, none of them can be used for sample size calculation. Aim 1 will develop new comparison procedures that allow and thus give formulas for the sample size calculation. Estimation of relative risks or more generally covariate effects is another common task in medical studies such as progression-free survival oncology studies. The proposed research will develop appropriate or efficient approaches to it when one faces various types of interval-censored data including clustered data. Aim 2 will focus on situations where the censoring can be regarded to be independent of the failure time variable under study, while aim 3 will deal with situations where the independence is not true. The statistical procedures or tools that will be developed in this proposed research will make the design of the concerned studies possible and help one to conduct correct and/or efficient analysis of them. PUBLIC HEALTH RELEVANCE: The main goal of this proposal is to address and investigate several important issues on treatment comparison and relative risk estimation in medical studies such as randomized clinical trials when one faces various types of interval-censored failure time data, and develop appropriate and/or efficient statistical procedures for them. It will consist of three aims and they are; 1) treatment comparison and sample size calculation, 2) estimation of relative risks and regression analysis I, and 3) estimation of relative risks and regression analysis II. A major difference between aims 2 and 3 is that the former will focus on situations where censoring mechanism is independent of response variables, while the latter will deal with situations when the censoring mechanism may depend on response variables.

描述（由申请人提供）：失效时间数据的统计分析一直是各种医学研究（如随机临床试验）设计和分析的基石，并发挥着重要作用。将故障时间数据与所有其他类型数据区分开来的一个关键特征是删失，这使得故障时间数据的分析独特而困难，并且可以以不同的形式发生。本提案将探讨医学研究中的两个基本问题，治疗比较和相对风险的估计，关于几种类型的故障时间数据，涉及区间删失或区间删失故障时间数据。这些数据的分析已经吸引了越来越多的关注，包括在政府机构和制药公司的医学调查人员和统计学家。具体而言，拟议的研究包括三个目标，它们是开发适当和/或有效的统计程序，用于1）治疗比较和样本量计算，2）相对风险估计和回归分析I，以及3）相对风险估计和回归分析II。众所周知，治疗比较可能是医学研究中最基本和最常见的任务，对于此类研究的设计，一个关键因素是确定所需的样本量。在区间删失数据的情况下，已经提出了一些比较程序。然而，它们大多数在应用中受到限制，更重要的是，它们都不能用于样本量计算。目标1将开发新的比较程序，允许并因此给出样本量计算的公式。估计相对风险或更一般的协变量效应是医学研究中的另一项常见任务，如无进展生存肿瘤学研究。该研究将在面对包括聚类数据在内的各种类型的区间删失数据时，开发适当或有效的方法。目标2将侧重于删失可以被认为是独立于所研究的故障时间变量的情况，而目标3将处理独立性不成立的情况。在这项拟议的研究中开发的统计程序或工具将使相关研究的设计成为可能，并帮助人们对它们进行正确和/或有效的分析。 公共卫生相关性：本提案的主要目的是解决和研究医学研究中的治疗比较和相对风险估计的几个重要问题，如随机临床试验，当一个人面对各种类型的区间删失失效时间数据，并制定适当的和/或有效的统计程序。它将包括三个目标，它们是：1）治疗比较和样本量计算，2）相对风险估计和回归分析I，以及3）相对风险估计和回归分析II。目标2和目标3之间的一个主要区别是，前者将侧重于审查机制独立于反应变量的情况，而后者将处理审查机制可能取决于反应变量的情况。