Statistical Methods for HIV/AIDS Research

HIV/艾滋病研究的统计方法

• 批准号: 7554697
• 负责人: Mei Cheng Wang
• 金额: $ 44.21万
• 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/7554697
• 关键词: AIDS prevention; 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; Disease regression; Drug resistance; Effectiveness of Interventions; End Point; Event; Failure; HIV; Intention; Intervention; Longitudinal Studies; Measurement; Medicine; Methods; Modeling; Monitor; Nature; Observational Study; Outcome; Patients; Prevention; Procedures; Public Health; Randomized; Randomized Controlled Clinical Trials; Recurrence; Research; Safety; Statistical Methods; Statistical Models; Techniques; Therapeutic; Therapeutic Studies; Time; United States Food and Drug Administration; Work; base; computerized data processing; conditioning; design; follow-up; hazard; improved; insight; interest; 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)：本项目的目标是开发和应用艾滋病研究中的统计学方法，以提高采用纵向随访设计时的科学推断。本文的研究内容包括以下几个方面：(1)开发终端事件信息量较大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，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测量：重复标记过程数据和在故障事件发生时评估的处理后标记数据。通常通过行政审查或诸如死亡的终端事件的发生来终止标记数据的收集，其中该终端事件可能与感兴趣的标记测量相关联。目标1下的工作将包括制定用于分析经常性标记过程数据的推断、建模和估计方法，以及用于从治疗后标记数据估计因果治疗效果的分析程序。(2)开发新的分组序贯方法，用于监测HIV/AIDS长期临床试验中经审查的事件发生时间终点。数据和安全监测委员会(数据和安全监测委员会)通常要求进行中期分析，以监测长期艾滋病毒/艾滋病临床试验中预防方案或治疗方案的有效性和安全性。对于这种收集经过审查的事件发生时间终点的长期艾滋病毒/艾滋病试验，传统统计方法的幼稚应用，如比例风险模型(COX，1972)，往往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。在这一目标下，我们将发展新的基于带变点的危险函数的分组序贯方法，用于监测截尾时间-事件结局的时变治疗效果。(3)发展回归方法以适应复发事件数据的演变协变量效应。艾滋病毒/艾滋病干预措施很少有持久的效果。无论是艾滋病毒预防试验还是艾滋病治疗研究，期望干预措施在随机后立即完全见效是不现实的。此外，耐药性可能会随着时间的推移而发展，这会侵蚀干预效果。刻画和量化时变干预效应将为研究干预机制提供有价值的科学见解。然而，大多数可用的方法只适用于恒定的效果。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重复事件数据。(4)开发增强型敏感性分析程序，用于分析过早失去随访的HIV随机研究，特别是由于终止治疗。在FDA批准的许多艾滋病毒药物注册试验中，患者在治疗终止后没有得到跟踪。在FDA要求患者在治疗终止后继续随访之前，这项研究的目的是为FDA临床审查员和更广泛的科学界提供有关治疗效果的信息，否则将无法获得这些信息。我们将开发的方法将普遍适用于随机试验和可能失去后续信息的观察性研究。公众 健康相关性：在HIV/AIDS临床试验和队列研究中，提出了新的统计模型和方法来研究存活率、复发事件和标记过程数据。统计工具和技术的发展是为了处理艾滋病研究中出现的一些复杂和重要的纵向问题。