Improvements to Respondent-Driven Sampling for the Study of Hidden Populations

隐藏群体研究中受访者驱动抽样的改进

• 批准号: 7900988
• 负责人: Matthew J. Salganik
• 金额: $ 13.64万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7900988
• 关键词: AIDS/HIV problem; Accounting; Adoption; Africa South of the Sahara; Behavioral; Centers for Disease Control and Prevention (U.S.); Cities; Computer Simulation; Country; Data; Data Set; Diagnostic; Disease; Ensure; Foundations; Future; Goals; Guidelines; HIV; HIV Infections; Homelessness; Infection; Injecting drug user; Joints; Lansky Play-Performance Status; Life; Logistic Regressions; Methods; Multivariate Analysis; Population; Prevalence; Prevention program; Probability; Procedures; Process; Public Health; Recruitment Activity; Research; Research Personnel; Resources; Respondent; Risk Behaviors; Risk Factors; Sample Size; Sampling; Sampling Studies; Series; Social Sciences; Statistical Methods; System; United Nations; base; design; high risk; improved; mathematical model; meetings; member; men who have sex with men; peer; programs; public health relevance; sex; simulation; social movement

DESCRIPTION (provided by applicant): Better data about the risk behaviors and disease prevalence within high-risk groups are needed for understanding and controlling the spread of HIV/AIDS. Unfortunately, this information is difficult to collect with standard sampling methods. The goal of this research is to improve respondent-driven sampling, a promising new statistical method for collecting such information. Respondent-driven sampling (RDS) is a form of snowball sampling that allows researchers to study "hidden" or "hard-to-reach" populations that are difficult to study with standard sampling methods (e.g., men who have sex with men, injection drug users, and sex workers). RDS data is collected through a peer-referral process where current sample members recruit future sample members. This process results in a sample that, while not directly representative of the hidden population, can yield unbiased estimates of, for example, HIV prevalence, if certain conditions are met. Because of the pressing need to understand the hidden populations at high risk for HIV/AIDS and the limitations of previous methods to collect this information, RDS has already been used in more than 120 studies around the world including the Centers for Disease Control and Prevention's (CDC) National HIV Behavioral Surveillance System. Despite this widespread adoption, improvements to RDS are urgently needed because the statistical foundations of the method are still poorly understood and key implementation questions remain unanswered. In order to improve RDS, we propose to: 1) develop guidelines for RDS sample size calculation to ensure that studies have the desired level of statistical power; 2) develop multivariate analysis procedures for RDS data; and 3) develop diagnostics to assess whether the assumptions behind RDS have been met. This research will achieve these specific aims through a combination of mathematical modeling, computer simulation, and the analysis of existing RDS data sets. Once complete, this research will help to establish statistical best practices for collecting and analyzing RDS data. Improvements to RDS will result in more accurate information about hidden populations that will facilitate research in the social sciences and public health. PUBLIC HEALTH RELEVANCE: The goal of this research is to improve respondent-driven sampling, a statistical method for studying "hidden" or "hard-to-reach" populations, including groups at high risk for HIV/AIDS (e.g., men who have sex with men, injection drug users, and sex workers). Improved information about risk behaviors and disease prevalence within these groups can be used to design and evaluate prevention programs, target resources where they are most needed, and ultimately help stop the spread of disease.

描述（由申请人提供）：需要更好地了解高风险群体内的风险行为和疾病患病率的更好数据才能理解和控制艾滋病毒/艾滋病的传播。不幸的是，这些信息很难使用标准采样方法收集。这项研究的目的是改善受访者驱动的抽样，这是一种有希望收集此类信息的新统计方法。受访者驱动的抽样（RDS）是一种雪球抽样的形式，使研究人员可以研究“隐藏”或“难以到达的人群”，这些人群难以使用标准抽样方法（例如，与男性，注射吸毒者和性工作者发生性关系的男人）进行研究。 RDS数据是通过当前样本成员募集未来样本成员的同行推荐过程收集的。此过程产生了一个样本，尽管不直接代表隐藏人群，但如果满足某些条件，可以产生例如HIV患病率的公正估计。由于紧迫的需要了解艾滋病毒/艾滋病高风险的隐藏人群以及以前收集此信息的局限性，因此在世界各地的120多项研究中，RDS已被使用，包括疾病控制和预防中心（CDC）国家HIV行为行为监测系统。尽管采用了这种广泛的采用，但迫切需要对RD的改进，因为该方法的统计基础仍然很少了解，并且关键的实施问题仍未得到答复。为了改善RDS，我们建议：1）制定RDS样本量计算指南，以确保研究具有所需的统计能力； 2）为RDS数据制定多元分析程序； 3）开发诊断，以评估是否已经满足RDS背后的假设。这项研究将通过数学建模，计算机模拟以及对现有RDS数据集的分析的结合来实现这些特定目标。完成后，这项研究将有助于建立用于收集和分析RDS数据的统计最佳实践。改进RD将导致有关隐藏人群的更准确的信息，这些信息将有助于社会科学和公共卫生的研究。 公共卫生相关性：这项研究的目的是改善受访者驱动的抽样，这是一种研究“隐藏”或“难以到达”人群的统计方法，包括对HIV/AIDS高风险的群体（例如，与男性发生性关系，注射吸毒者和性工作者发生性关系的男人）。这些群体内有关风险行为和疾病患病率的改进信息可用于设计和评估预防计划，针对最需要的资源，并最终有助于阻止疾病的传播。