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Statistical Issues in AIDS Research

艾滋病研究中的统计问题

基本信息

批准号：
10613454
负责人：
JAMES P HUGHES
金额：
$ 72.02万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
1989
资助国家：
美国
起止时间：
1989-09-30 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10613454
关键词：
AIDS prevention Acceleration Acquired Immunodeficiency Syndrome Address Adherence Area Biological Assay Biometry Cells Clinical Research Clinical Treatment Clinical Trials Complex Data Data Analyses Data Set Development Differential Equation Disease Documentation Epidemic Flow Cytometry HIV HIV Infections HIV prevention trial HIV vaccine HIV/AIDS Incidence Individual Infection Information Networks Laboratories Maps Mathematics Measures Methodology Methods Modeling Modernization Outcome Pathway Analysis Performance Policy Maker Population Prevalence Prevention Procedures Process Production Productivity Property Reporting Reproducibility Research Research Personnel Resolution Resources Running Safety Spatial Distribution Specific qualifier value Stains Statistical Methods Structure Survival Analysis Time Time trend Uncertainty Vaccine Research Viral Load result Work causal model cytokine design dynamic system flexibility human subject implementation research implementation study implementation trial innovation latent HIV reservoir multiple data sources novel novel strategies pre-exposure prophylaxis prevent semiparametric simulation surveillance study theories tool transmission process treatment trial trial design user friendly software vaccine development vaccine trial

项目摘要

Thirty years after identification of HIV, the AIDS epidemic continues with an estimated 37 million individuals currently infected worldwide and 1.8 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 HIV clinical trials, including methods for implementation trials, trials of pre-exposure prophylaxis (PrEP), and vaccine studies (ii) optimal design and analysis of surveillance studies that are necessary to characterize the HIV epidemic, and (iii) analysis of data from key laboratory assays used in HIV cure and vaccine research. First, statistical methods are proposed that address key challenges in current HIV prevention, treatment and implementation trials. The approaches include causal modeling and model-free/model- agnostic methods that remove the need for complex modeling assumptions. The proposed methods have broad applicability to clinical and implementation studies of HIV/AIDS as well as other fields. Second, statistical methods are proposed to help characterize and describe key features of the HIV epidemic. These include new approaches to spatial-temporal modeling that can provide fine-scale maps (with uncertainty information) of HIV prevalence, incidence, percent suppressed, and other measures, as well as a novel approach to combining the (stochastic) counting process approach to survival analysis with (deterministic) differential equations to analyze interactive and dynamic systems, such as socio-sexual networks. These methods have the potential to provide critical guidance for optimizing the distribution of treatment and prevention resources. Finally, the proposed research will investigate statistical methods for the analysis of key laboratory assays used in cure and vaccine studies, including the quantitative viral load outgrowth assay used to quantify the size of the latent HIV reservoir, and the intra-cellular staining flow-cytometry-based assay used in vaccine research to quantify cytokine production and accumulation after cell stimulation. 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, HIV prevention trials, implementation research, and other HIV/AIDS related studies.

艾滋病毒被发现30年后，艾滋病仍在流行，估计有3700万人感染艾滋病毒。目前全世界有100万人感染，每年有180万人新感染。研究以预防治疗艾滋病病毒感染变得越来越复杂，分析挑战已经成为相对复杂。此应用程序旨在解决及时和重要的统计问题在艾滋病研究中。特别是，将开发新的统计方法，用于（i）数据分析艾滋病毒临床试验，包括实施试验的方法，暴露前预防试验，（ii）必要的监测研究的最佳设计和分析描述艾滋病毒流行的特征，以及（iii）分析用于艾滋病毒治疗的关键实验室检测的数据疫苗研究。首先，提出了应对当前艾滋病毒预防中的关键挑战的统计方法，治疗和实施试验。这些方法包括因果建模和无模型/模型- 不可知的方法，消除了对复杂建模假设的需要。所提出的方法对HIV/AIDS的临床和实施研究以及其他领域具有广泛的适用性。其次，提出了统计方法来帮助描述和描述艾滋病毒的关键特征，疫情其中包括新的时空建模方法，可以提供精细的尺度艾滋病毒流行率、发病率、抑制百分比和其他信息的地图（含不确定性信息）措施，以及一种新的方法，结合（随机）计数过程的方法，使用（确定性）微分方程进行生存分析，以分析交互式动态系统，例如社会性网络。这些方法有可能为以下方面提供重要指导：优化防治资源配置。最后，拟议的研究将研究用于治疗和疫苗的关键实验室检测分析的统计方法研究，包括用于定量潜伏HIV大小的定量病毒载量生长测定疫苗研究中使用的基于细胞内染色流式细胞术的测定，定量细胞刺激后细胞因子的产生和积累。拟议的研究反映了研究人员广泛和持续的参与，艾滋病毒/艾滋病研究领域。所处理的统计问题的动机是应用关键艾滋病研究领域。概述的解决方案具有高度创新性，可直接适用于当前的疫苗开发、艾滋病毒预防试验、实施研究等方面的科学研究艾滋病毒/艾滋病相关研究。

项目成果

期刊论文数量（195）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

An introduction to the Marburg virus vaccine consortium, MARVAC.

DOI：
10.1371/journal.ppat.1010805
发表时间：
2022-10
期刊：
PLoS pathogens
影响因子：
6.7
作者：
通讯作者：

Harmonizing child mortality data at disparate geographic levels.

DOI：
10.1177/0962280220988742
发表时间：
2021-05
期刊：
Statistical methods in medical research
影响因子：
2.3
作者：
Marquez N;Wakefield J
通讯作者：
Wakefield J

Joint modeling of longitudinal and survival data with the Cox model and two-phase sampling.

DOI：
10.1007/s10985-016-9364-1
发表时间：
2017-01
期刊：
LIFETIME DATA ANALYSIS
影响因子：
1.3
作者：
Fu, Rong;Gilbert, Peter B.
通讯作者：
Gilbert, Peter B.

Parametric likelihood inference for interval censored competing risks data.

DOI：
10.1111/biom.12109
发表时间：
2014-03
期刊：
Biometrics
影响因子：
1.9
作者：
Hudgens MG;Li C;Fine JP
通讯作者：
Fine JP

A small area model to assess temporal trends and sub-national disparities in healthcare quality.

DOI：
10.1038/s41467-023-40234-9
发表时间：
2023-07-28
期刊：
NATURE COMMUNICATIONS
影响因子：
16.6
作者：
Allorant, Adrien;Fullman, Nancy;Leslie, Hannah H. H.;Sarr, Moussa;Gueye, Daouda;Eliakimu, Eliudi;Wakefield, Jon;Dieleman, Joseph L. L.;Pigott, David;Puttkammer, Nancy;Reiner, Robert C. C.
通讯作者：
Reiner, Robert C. C.