Valid Inference for Respondent Driven Sampling of Hidden Networked Populations

描述(申请人提供)：受访者驱动抽样(RDS)可以说是最好和最常见的方法，被用来调查用标准概率方法难以抽样的“隐藏”人群。例子包括注射吸毒者(IDU)、男男性行为者(MSM)和女性性工作者。仅两个隐藏人群(男男性接触者和注射吸毒者)的感染占2006年美国53,600新艾滋病毒感染人数的69%。我们建议开发统计方法来改进RDS样本的估计，重点是开发有助于该领域的研究人员并可使用的工具和方法。从统计学的角度来看，RDS是一种旨在获得概率样本的自适应抽样制度。它在获取样本方面通常是有效的，但它在多大程度上可以被视为具有已知纳入概率的概率样本，尚不清楚。目前对这些包含概率的估计值是有问题的。我们将开发包含最新方法的开放源码、用户友好的RDS统计软件，举办研讨会以建立应用研究人员和方法学研究人员之间的合作，传播这些新方法，并使用开发的工具提供培训。这个项目意义重大，因为它将在隐藏人口测量方法和相关科学问题方面取得重大进展。作为第一批与现场研究人员密切合作的统计学家，研究人员是唯一有资格对RDS的统计理解系统化的人。该项目的创新之处在于，它挑战了现有的RDS推理范式，并提出了一种基于尖端统计思想和模型的新方法。缺乏统计方法来证明RDS是合理的。这个项目将产生一个系统的统计框架，在这个框架内了解RDS的优势和劣势。在拟议的工作中，我们将表示RDS过程的复杂性，但也允许将结果推理的不确定性量化。RDS统计方法的发展对社会科学和行为科学具有重要意义。我们将通过面向实地研究人员的用户友好的开放源码软件，传播估计、诊断和量化不确定性的方法。这些都将适用于未来的铁路发展数据，以及铁路发展调查现有的大型数据库。 公共卫生相关性：受访者驱动抽样(RDS)可以说是用来调查用标准概率方法难以抽样的“隐藏”人群的最好和最常见的方法。例子包括注射吸毒者(IDU)、男男性行为者(MSM)和女性性工作者。仅两个隐藏人群(男男性接触者和注射吸毒者)的感染占2006年美国53,600新艾滋病毒感染人数的69%(CDC报告，霍尔，2008年)。这笔赠款的目的是改进这类估计，因为众所周知，目前的估计做法存在问题。

DESCRIPTION (provided by applicant): Respondent Driven Sampling (RDS) is arguably the best and most common method being used to survey "hidden" populations that are hard to sample using standard probability methods. Examples include injection drug users (IDU), men who have sex with men (MSM), and female sex workers. Infections amongst just two hidden populations (MSM, IDUs) accounted for the estimated 69% of 53,600 new HIV infections in the US during 2006. We propose to develop statistical methods for improving estimation in RDS samples with a focus on developing tools and methods that will help, and be accessible by, researchers in the field. From a statistical perspective, RDS is an adaptive sampling regime aimed at obtaining a probability sample. It is often effective at acquiring a sample, but the degree to which it can be considered a probability sample, with known inclusion probabilities, is unclear. The current estimators of these inclusion probabilities are known to be problematic. We will develop open-source user-friendly statistical software for RDS incorporating state-of-the-art methods, hold workshops to establish collaboration between applied and methodological researchers, disseminate these new methods and provide training using the tools developed. This project is significant because it will result in major advances in methodology for hidden population measurement and related scientific problems. The investigators are uniquely qualified as the first statisticians with close collaborative ties with field-researchers to systematize statistical understanding of RDS. The project is innovative in that it challenges the existing paradigm of RDS inference and proposes a new approach based on cutting edge statistical ideas and models. There is a dearth of statistical methodology justifying RDS. This project will produce a systematic statistical framework within which to understand the strengths and weaknesses of RDS. In the proposed work we will represent the complexities of the RDS procedure, but also allow the uncertainty of the resulting inference to be quantified. The development of statistical methodology for RDS is of vital importance to the social and behavioral sciences. We will disseminate the methodology for estimation, diagnostics and quantification of uncertainty via open-source user-friendly software aimed at field researchers. These will be applicable to both future RDS data, and the large existing data bases of RDS surveys. PUBLIC HEALTH RELEVANCE: Respondent Driven Sampling (RDS) is arguably the best and most common method being used to survey "hidden" populations that are hard to sample using standard probability methods. Examples include injection drug users (IDU), men who have sex with men (MSM), and female sex workers. Infections amongst just two hidden populations (MSM, IDUs) accounted for the estimated 69% of 53,600 new HIV infections in the US during 2006 (CDC report, Hall, 2008). The purpose of this grant is to improve these kinds of estimates as current estimation practices are known to be problematic.