Methods to Advance the HIV Prevention Research Agenda

推进艾滋病毒预防研究议程的方法

• 批准号: 8211677
• 负责人: VICTOR GERARD DEGRUTTOLA
• 金额: $ 40.38万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-03-01 至 2015-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8211677
• 关键词: AIDS prevention; Address; Age; Anti-Retroviral Agents; Area; Behavior; Characteristics; Clinic; Collection; Communities; Data; Data Analyses; Databases; Development; Effectiveness; Epidemic; Goals; HIV; HIV Infections; Health Professional; Household; Incidence; Individual; Information Networks; Intervention; Investigation; Knowledge; Longitudinal Studies; Male Circumcision; Measures; Methods; Modality; Modeling; Outcome; Pathway Analysis; Pharmaceutical Preparations; Policy Maker; Population Characteristics; Prevalence; Prevention; Prevention Research; Preventive Intervention; Property; Public Health; Randomized; Research; Research Design; Research Personnel; Resources; Risk; Role; Sampling; Simulate; Specific qualifier value; Surveillance Methods; Surveys; Testing; Time; Vaccines; base; demographics; design; improved; interest; microbicide; randomized trial; research study; simulation; spatial temporal variation; success; theories; transmission process; treatment effect; trend

DESCRIPTION (provided by applicant): This proposal addresses statistical challenges that arise in HIV prevention research. Recent progress in prevention from different uses of antiretroviral drugs (in PrEP, microbicides, and treatment), male circumcision, and possibly vaccine has been noteworthy but modest. Combining across these modalities, however, might considerably enhance their effects. To address a crucial question facing policy makers--how best to deploy these modalities--requires knowledge of their combined effects and of the factors that impact on these effects. We propose to facilitate such research by developing efficient methods for analysis of data from observational and randomized studies of prevention interventions and for investigation of factors that may modify their degree of benefit. To do so, we propose to extend methods in variety of statistical areas, including causal inference and semiparametric theory, network analysis, and spatial analysis. We show how knowledge gained from new methods in each area can contribute to the usefulness of those in the other areas. Our first aim is to develop methods for using baseline covariates to improve efficiency of analyses of correlated outcome data that arise in group randomized trials (GRTs) and longitudinal studies; both types of studies are widely used in HIV prevention research. Because the number of experimental units in GRTs may be constrained by resource limitations, we develop methods appropriate for small as well as for large samples, and consider both individual- and group-level covariates. Estimation of network features that are important epidemic drivers from network samples is our second aim. This aim also considers methods for using such estimates in the construction of collections of networks--essential for realistic simulation of epidemics and of the impact of combination prevention interventions on them. These investigations will be useful for assessing whether intervention packages should be tailored to network features (and if so how), both through simulation and in analyses of data from research studies. The third aim develops methods for using data from HIV surveillance, especially of antenatal clinics, to estimate spatial and temporal variation in age-specific HIV prevalence and incidence. Such methods can aid in evaluating the impact of deployment of prevention interventions, especially those which are rolled out in sequentially in different regions. PUBLIC HEALTH RELEVANCE: This proposal addresses statistical challenges that arise in HIV prevention research. We focus on development of efficient methods for analysis of data from observational and randomized studies of prevention interventions and for investigating factors, like features of sexual networks, that impact on their success. We do so by extending methods in variety of statistical areas, including causal inference and semiparametric theory, network analysis, and spatial analysis. We show how knowledge gained from new methods in each area can contribute to the usefulness of the others in guiding deployment of prevention interventions.

描述（由申请人提供）：本提案解决了艾滋病毒预防研究中出现的统计挑战。最近在不同用途的抗逆转录病毒药物（PrEP、杀微生物剂和治疗）、男性包皮环切以及可能的疫苗方面的预防进展值得注意，但进展不大。然而，将这些模式结合起来，可能会大大增强它们的效果。要解决决策者面临的一个关键问题——如何最好地部署这些模式——需要了解它们的综合影响以及影响这些影响的因素。我们建议通过开发有效的方法来分析预防干预的观察性和随机研究的数据，并调查可能改变其获益程度的因素，从而促进此类研究。为此，我们建议扩展各种统计领域的方法，包括因果推理和半参数理论，网络分析和空间分析。我们展示了从每个领域的新方法中获得的知识如何有助于其他领域的有用性。我们的第一个目标是开发使用基线协变量的方法，以提高在组随机试验（GRTs）和纵向研究中出现的相关结果数据的分析效率；这两种类型的研究都广泛用于艾滋病预防研究。由于grt中的实验单元数量可能受到资源限制的限制，我们开发了适合小样本和大样本的方法，并考虑了个体和群体水平的协变量。从网络样本中估计作为重要流行病驱动因素的网络特征是我们的第二个目标。这一目标还考虑了在构建网络集合时使用这种估计的方法，这对于现实模拟流行病和综合预防干预措施对流行病的影响至关重要。通过模拟和研究数据分析，这些调查将有助于评估干预方案是否应该针对网络特征进行定制（如果是的话，如何定制）。第三个目标是开发利用艾滋病毒监测数据，特别是产前诊所数据的方法，以估计特定年龄艾滋病毒流行率和发病率的时空变化。这些方法有助于评估部署预防干预措施的影响，特别是在不同区域依次推出的干预措施。