Statistical Methods for HIV/AIDS Research

HIV/艾滋病研究的统计方法

基本信息

批准号：
8078823
负责人：
Mei Cheng Wang
金额：
$ 39.83万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-06-05 至 2013-05-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): The objective of this project is to develop and apply statistical methods in AIDS research to improve scientific inferences when longitudinal follow-up designs are employed. The proposed research includes the following aims: (1) To develop statistical methods for marker data with informative terminal events. In longitudinal studies of HIV/AIDS research, marker measurements are frequently collected or observed conditioning on the occurrence of recurrent events. Two types of marker measurements will be considered under Aim 1: recurrent- marker process data, and post-treatment marker data evaluated at time of failure event. The collection of marker data is typically terminated by administrative censoring or occurrence of a terminal event such as death, where the terminal event is possibly correlated with the marker measurements of interest. The work under Aim 1 will include the development of inference, modeling and estimation methods for analyzing recurrent marker process data, and analytical procedures for estimation of causal treatment effects from post-treatment marker data. (2) To develop new group sequential methods for monitoring censored time-to-event endpoints in long-term HIV/AIDS clinical trials. Interim analyses are usually required by the Data and Safety Monitoring Board (DSMB) to monitor the efficacy and safety of a prevention regiment or therapeutic treatment in long-term HIV/AIDS clinical trials. For such long-term HIV/AIDS trials that collect censored time-to-event endpoints, naive applications of the conventional statistical methods, such as the proportional hazards model (Cox, 1972), are often insufficient to characterize the time-varying nature of treatment effect between different treatment regiments during long-term follow-up. Under this aim, we will develop new group sequential methods based on the hazard functions with change points in monitoring time-varying treatment effect for censored time-to-event outcomes. (3) To develop regression methods to accommodate evolving covariate effects for recurrent events data. HIV/AIDS interventions rarely have constant effects. Whether it is an HIV prevention trial or AIDS therapeutic study, it is unrealistic to expect the intervention to take full effect instantaneously after randomization. Furthermore, drug resistance might develop over time, which erodes the intervention effect. Characterizing and quantifying time- varying intervention effect would provide valuable scientific insight to the mechanism of the intervention. However, most available methods only accommodate constant effects. We plan to develop statistical models and inference procedures to address this issue, with a focus on generalizing the accelerated failure time model and on recurrent events data. (4) To develop enhanced sensitivity analysis procedures for analyzing HIV randomized studies with premature loss of follow-up, especially due to termination of treatment. In many registration trials for FDA approval of HIV medicines, patients are not followed after treatment termination. Until the FDA mandates continued follow-up of patients after treatment termination, the aim of this research is to provide FDA clinical reviewers and the broader scientific community information about intention to treat effects that would otherwise be unavailable. The methods we will develop will be generally applicable to randomized trials and observational studies with potentially informative loss of follow-up. PUBLIC HEALTH RELEVANCE: New statistical models and methods are proposed to study survival, recurrent events and marker process data in HIV/AIDS clinical trials and cohort studies. Statistical tools and techniques are developed to deal with some of the sophisticated and important problems arising in AIDS studies with longitudinal nature.

描述（由申请人提供）：该项目的目的是在采用纵向后续设计时开发和应用统计方法，以改善科学推断。拟议的研究包括以下目的：（1）开发具有信息终端事件的标记数据的统计方法。在艾滋病毒/艾滋病研究的纵向研究中，经常收集或观察到有关复发事件发生的标志物测量值。将在AIM 1中考虑两种类型的标记测量值：在失败事件时评估的经常性标记过程数据以及处理后标记数据。标记数据的收集通常是通过审查或发生诸如死亡等终端事件的终端事件的终止事件终止的，其中终端事件可能与感兴趣的标记测量值相关。 AIM 1下的工作将包括用于分析经常性标记过程数据的推理，建模和估计方法的开发，以及用于估计治疗后标记数据因果治疗效应的分析程序。（2）开发新的组顺序方法，用于监测长期HIV/AIDS临床试验中的事件时间终点。数据和安全监测委员会（DSMB）通常需要进行临时分析，以监测长期HIV/AIDS临床试验中预防团或治疗治疗的疗效和安全性。对于此类长期的HIV/AIDS试验，这些试验收集了审查的事件终点，诸如比例危害模型（Cox，1972）之类的常规统计方法的幼稚应用通常不足以表征长期随访期间不同治疗方案之间治疗效应的时间变化性质。在此目标下，我们将根据危害函数开发新的组顺序方法，并在监测审查时间变化的时间变化治疗效果方面具有变化点。（3）开发回归方法以适应反复事件数据的不断发展的协变量效应。艾滋病毒/艾滋病干预措施很少有持续的影响。无论是预防HIV试验还是AIDS治疗研究，期望干预措施在随机分组后立即采取全部作用是不现实的。此外，耐药性可能会随着时间的流逝而发展，从而侵蚀了干预效果。表征和量化时间变化的干预效果将为干预机制提供宝贵的科学见解。但是，大多数可用方法仅适应恒定效果。我们计划开发统计模型和推理程序来解决此问题，重点是概括加速的失败时间模型和经常性事件数据。（4）开发增强的灵敏度分析程序，用于分析过早随访的艾滋病毒随机研究，尤其是由于治疗终止。在许多用于FDA批准HIV药物的注册试验中，治疗终止后未遵循患者。在FDA命令治疗终止后继续对患者进行随访之前，这项研究的目的是为FDA临床审查员提供有关治疗效果的意图的更广泛的科学社区信息，这些信息本来是无法获得的。我们将开发的方法通常适用于随机试验和观察性研究，并具有潜在的随访信息。民众健康相关性：提出了新的统计模型和方法来研究HIV/AIDS临床试验和队列研究中的生存，经常性事件和标记过程数据。开发了统计工具和技术来处理具有纵向性质的艾滋病研究中引起的一些复杂而重要的问题。