Statistical Issues in AIDS Research

艾滋病研究中的统计问题

• 批准号: 8816027
• 负责人: JAMES P HUGHES
• 金额: $ 57.49万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 1989
• 资助国家: 美国
• 起止时间: 1989-09-30 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8816027
• 关键词: AIDS prevention; AIDS/HIV problem; Accounting; Acquired Immunodeficiency Syndrome; Address; Adherence; Algorithms; Anti-Retroviral Agents; Area; Biological Assay; Biological Markers; Biometry; Complex; Data; Data Analyses; Developing Countries; Development; Disease; Documentation; Epidemic; Epitope Mapping; Failure; HIV; HIV Infections; HIV prevention trial; HIV vaccine; Health; Healthcare; Individual; Infection; Lead; Measures; Meta-Analysis; Methods; Monitor; Oral; Pattern; Performance; Pharmaceutical Preparations; Phase; Prevention; Procedures; Process; Property; Prophylactic treatment; Research; Research Personnel; Resources; Risk; Sampling; Scheme; Solutions; Source; Staging; Statistical Methods; Testing; Vaccines; Work; causal model; cost; design; drug efficacy; improved; innovation; microbicide; novel; prevent; simulation; theories; tool; user friendly software; vaccine development

DESCRIPTION: Thirty years after identification of HIV, the AIDS epidemic continues with an estimated 34 million individuals currently infected worldwide and 2.5 million new infections each year. Research to prevent and treat HIV infection has grown increasingly sophisticated and the analytic challenges have become correspondingly complex. This application is intended to address timely and important statistical issues in HIV/AIDS research. In particular, novel statistical methods will be developed for (i) analysis of data from studies of pre-exposure prophylaxis (PrEP), (ii) optimal design and analysis of studies with complex sampling and observation schemes, and (iii) optimal design of pooling studies. First, statistical methods are proposed that address key challenges in PrEP studies, namely, inferring drug efficacy in the face of incomplete adherence, combining disparate sources of information to estimate adherence and summarizing adherence patterns. The approaches include causal modeling, Bayesian synthesis of information and functional data analysis. The proposed methods have the potential to provide clarity in a field which is hindered by disparate trial results. Second, becaue HIV studies often utilize expensive tests and assays, the proposed research will explore optimal sampling designs and efficient semiparametric and nonparametric methods for studies that use such two-phase or multi-phase sampling. The problems of multi-phase sampling and competing risks in the context of interval censored data will also be studied. These methods will provide critical guidance for optimizing the use of resources and minimizing costs in studies that utilize expensive assays. Finally, the proposed research will investigate statistical methods for the analysis of pooled data with specific applications to epitope mapping and monitoring for virologic failure in resource constrained settings. The former is a critical component of HIV vaccine development and the latter has the potential to greatly improve health care of HIV-infected individuals in developing countries. The proposed research reflects the extensive and ongoing involvement of the investigators in the field of HIV/AIDS research. The statistical problems addressed are motivated by applications in key areas of HIV research. The solutions outlined are highly innovative and directly applicable to current scientific research in vaccine development, trials of pre-exposure prophylaxis, and other HIV/AIDS trials and studies.

描述：在发现艾滋病毒30年后，艾滋病的流行仍在继续，目前全世界估计有3400万人感染，每年有250万人新感染。预防和治疗艾滋病毒感染的研究变得越来越复杂，分析的挑战也相应地变得复杂。此应用程序旨在解决艾滋病毒/艾滋病研究中及时和重要的统计问题。特别是，将开发新的统计方法来(i)分析暴露前预防（PrEP）研究的数据，（ii）具有复杂抽样和观察方案的研究的优化设计和分析，以及（iii）池化研究的优化设计。首先，提出了统计方法来解决PrEP研究中的关键挑战，即在不完全依从性的情况下推断药物疗效，结合不同的信息来源来估计依从性，并总结依从性模式。方法包括因果建模、贝叶斯信息综合和功能数据分析。所提出的方法有可能在一个受到不同试验结果阻碍的领域提供清晰度。其次，由于HIV研究通常使用昂贵的测试和分析，因此拟议的研究将探索最佳采样设计和有效的半参数和非参数方法，用于使用这种两阶段或多阶段采样的研究。本文还研究了区间截尾数据下的多相采样和竞争风险问题。这些方法将为在使用昂贵检测方法的研究中优化资源使用和最小化成本提供关键指导。最后，该研究将研究统计方法，用于分析汇集的数据，具体应用于表位定位和资源受限环境下的病毒学失败监测。前者是艾滋病毒疫苗开发的关键组成部分，后者有可能大大改善发展中国家艾滋病毒感染者的保健。拟议的研究反映了研究人员在艾滋病毒/艾滋病研究领域的广泛和持续参与。解决的统计问题的动机是在艾滋病毒研究的关键领域的应用。所概述的解决办法具有高度创新性，可直接适用于目前在疫苗开发、接触前预防试验以及其他艾滋病毒/艾滋病试验和研究方面的科学研究。