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

描述（由申请人提供）：为了了解和控制艾滋病毒/艾滋病的传播，需要关于高危人群中危险行为和疾病流行率的更好数据。不幸的是，这些信息很难用标准的抽样方法收集。本研究的目的是改善响应驱动抽样，一个有前途的新的统计方法来收集这样的信息。响应者驱动抽样（RDS）是一种滚雪球抽样形式，允许研究人员研究难以用标准抽样方法研究的“隐藏”或“难以接触”的人群（例如，男男性行为者、注射毒品使用者和性工作者）。RDS数据是通过同行推荐过程收集的，当前的样本成员招募未来的样本成员。这一过程产生的样本虽然不能直接代表隐藏的人口，但如果满足某些条件，可以得出无偏估计数，例如艾滋病毒流行率。由于迫切需要了解隐藏的艾滋病毒/艾滋病高危人群，以及以前收集这些信息的方法的局限性，RDS已经在世界各地的120多项研究中使用，包括疾病控制和预防中心（CDC）的国家艾滋病毒行为监测系统。尽管这种广泛采用，RDS的改进是迫切需要的，因为该方法的统计基础仍然知之甚少，关键的实施问题仍然没有答案。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功效水平; 2）制定RDS数据的多变量分析程序; 3）制定诊断方法，以评估RDS背后的假设是否得到满足。本研究将通过数学建模、计算机模拟和对现有RDS数据集的分析来实现这些特定目标。一旦完成，这项研究将有助于建立收集和分析RDS数据的统计最佳实践。RDS的改进将导致关于隐藏人口的更准确的信息，这将促进社会科学和公共卫生的研究。 公共卫生相关性：这项研究的目标是改进受调查者驱动的抽样，这是一种研究“隐藏”或“难以接触”人口的统计方法，包括艾滋病毒/艾滋病高危群体（例如，男男性行为者、注射毒品使用者和性工作者）。关于这些群体中的风险行为和疾病流行率的改进信息可用于设计和评估预防计划，将资源用于最需要的地方，并最终帮助阻止疾病的传播。

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.