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.

鉴定艾滋病毒30年后，艾滋病的流行继续，估计3700万目前在全球范围内感染的人，每年有180万个新感染。预防的研究治疗艾滋病毒感染越来越复杂，分析挑战已经变得相应的复杂。该应用程序旨在解决及时且重要的统计问题在艾滋病毒/艾滋病研究中。特别是，将开发新的统计方法来（i）数据分析来自HIV临床试验，包括实施试验的方法，预防前的试验（准备）和疫苗研究（ii）必要的监视研究的最佳设计和分析表征HIV流行病和（iii）对HIV中使用的关键实验室测定的数据分析和疫苗研究。首先，提出了统计方法，以解决当前预防HIV的关键挑战，治疗和实施试验。这些方法包括因果建模和无模型/模型 - 消除对复杂建模假设的需求的不可知论方法。提出的方法对艾滋病毒/艾滋病以及其他领域的临床和实施研究具有广泛的适用性。其次，提出了统计方法来帮助表征和描述HIV的关键特征流行性。其中包括可以提供精细规模的空间建模的新方法艾滋病毒患病率，发病率，抑制百分比和其他的地图（带有不确定性信息）措施以及一种结合（随机）计数过程方法的新方法具有（确定性）微分方程的生存分析，分析交互式和动态系统，例如社会性网络。这些方法有可能为优化治疗和预防资源的分布。最后，拟议的研究将研究用于分析用于治疗和疫苗的关键实验室测定的统计方法研究，包括用于量化潜在艾滋病毒大小的定量病毒负荷生长测定储层，以及用于疫苗研究中用于疫苗研究的基于细胞内染色的基于流动仪的测定量化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参与的广泛和持续参与艾滋病毒/艾滋病研究领域。解决的统计问题是由密钥中的申请激励艾滋病毒研究领域。概述的解决方案具有高度创新性，直接适用于当前疫苗开发，HIV预防试验，实施研究和其他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）

Nonparametric variable importance assessment using machine learning techniques.

DOI：
10.1111/biom.13392
发表时间：
2021-03
期刊：
Biometrics
影响因子：
1.9
作者：
Williamson BD;Gilbert PB;Carone M;Simon N
通讯作者：
Simon N

Estimating a Treatment Effect in Residual Time Quantiles under the Additive Hazards Model.

估计附加风险模型下剩余时间分位数的治疗效果。

DOI：
10.1007/s12561-016-9180-x
发表时间：
2017
期刊：
Statistics in biosciences
影响因子：
1
作者：
Crouch,LuisAlexander;Zheng,Cheng;Chen,YingQing
通讯作者：
Chen,YingQing

Effectiveness of Bivalent Boosters against Severe Omicron Infection.

DOI：
10.1056/nejmc2215471
发表时间：
2023-02-23
期刊：
NEW ENGLAND JOURNAL OF MEDICINE
影响因子：
158.5
作者：
Lin, Dan-Yu;Xu, Yangjianchen;Gu, Yu;Zeng, Donglin;Wheeler, Bradford;Young, Hayley;Sunny, Shadia K.;Moore, Zack
通讯作者：
Moore, Zack

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