Statistical Methods for HIV/AIDS Research

HIV/艾滋病研究的统计方法

• 批准号: 7630548
• 负责人: Mei Cheng Wang
• 金额: $ 40.43万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-06-05 至 2012-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7630548
• 关键词: AIDS/HIV problem; Acquired Immunodeficiency Syndrome; Address; Aftercare; Cessation of life; Clinical; Clinical Trials; Clinical Trials Data Monitoring Committees; Cohort Studies; Collection; Communities; Cox Proportional Hazards Models; Data; Development; Drug resistance; Event; Failure; HIV; HIV prevention trial; Intention; Intervention; Longitudinal Studies; Measurement; Medicine; Methods; Modeling; Monitor; Nature; Observational Study; Outcome; Patients; Prevention; Procedures; Randomized; Randomized Controlled Clinical Trials; Recurrence; Research; Safety; Statistical Methods; Statistical Models; Techniques; Therapeutic; Therapeutic Studies; Time; Work; base; computerized data processing; conditioning; design; follow-up; hazard; improved; insight; interest; intervention effect; premature; public health relevance; tool; treatment effect

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）：本项目的目的是开发和应用统计方法在艾滋病研究，以改善科学推论时，纵向随访设计。本研究的主要目的是：（1）建立具有信息性终点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，经常收集或观察标志物测量值，条件是复发事件的发生。在目标1下将考虑两种类型的标记物测量：复发标记物过程数据和失效事件时评价的处理后标记物数据。标志物数据的收集通常因行政审查或终末事件（如死亡）的发生而终止，其中终末事件可能与感兴趣的标志物测量相关。目标1下的工作将包括开发用于分析复发性标记物过程数据的推理、建模和估计方法，以及用于从治疗后标记物数据估计因果治疗效应的分析程序。(2)开发新的成组序贯方法，用于监测长期HIV/AIDS临床试验中删失的至事件时间终点。数据和安全监测委员会（DSMB）通常要求进行中期分析，以监测长期HIV/AIDS临床试验中预防方案或治疗性治疗的有效性和安全性。对于此类收集删失至事件时间终点的长期HIV/AIDS试验，传统统计方法（如比例风险模型（考克斯，1972））的简单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果的时变性质。在这一目标下，我们将开发新的基于风险函数的组序贯方法，用于监测删失时间至事件结局的时变治疗效果。(3)开发回归方法，以适应复发事件数据的协变量效应。艾滋病毒/艾滋病干预措施很少能产生持续的效果。无论是艾滋病预防试验还是艾滋病治疗研究，期望干预措施在随机化后立即完全生效都是不现实的。此外，耐药性可能随着时间的推移而发展，这会削弱干预效果。表征和量化时变干预效果将为干预机制提供有价值的科学见解。然而，大多数可用的方法仅适用于常数效应。我们计划开发统计模型和推理程序来解决这个问题，重点是推广加速故障时间模型和经常性事件数据。(4)为分析提前失访（尤其是由于终止治疗）的HIV随机研究开发增强的敏感性分析程序。在许多FDA批准的HIV药物注册试验中，患者在治疗终止后没有随访。在FDA要求对治疗终止后的患者进行持续随访之前，本研究的目的是为FDA临床审查人员和更广泛的科学界提供有关治疗效果的信息，否则这些信息将无法获得。我们将开发的方法将普遍适用于随机试验和观察性研究，具有潜在的失访信息。公共 健康相关性：提出了新的统计模型和方法，用于研究HIV/AIDS临床试验和队列研究中的生存、复发事件和标记过程数据。统计工具和技术的发展，以处理一些复杂的和重要的问题，在艾滋病的纵向性质的研究。