Valid Inference for Respondent Driven Sampling of Hidden Networked Populations

隐藏网络群体受访者驱动抽样的有效推断

基本信息

项目摘要

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.
描述(由申请人提供):受访者驱动抽样(RDS)可以说是最好和最常用的方法,用于调查“隐藏”的人口,很难使用标准概率方法进行抽样。例子包括注射毒品使用者(IDU)、男男性行为者(MSM)和女性性工作者。2006年,美国53,600例新的艾滋病毒感染中,仅两个隐藏人群(男男性行为者和注射吸毒者)中的感染估计占69%。我们建议开发统计方法,以提高估计RDS样本的重点开发工具和方法,这将有助于,并可由该领域的研究人员。从统计学的角度来看,RDS是一种自适应抽样制度,旨在获得概率样本。它在获取样本时通常是有效的,但在已知包含概率的情况下,它在多大程度上可以被视为概率样本,目前还不清楚。这些包含概率的当前估计是有问题的。我们将为RDS开发开放源码的用户友好的统计软件,其中包括最先进的方法,举办研讨会以建立应用和方法研究人员之间的合作,传播这些新方法,并提供使用开发的工具的培训。 该项目意义重大,因为它将导致隐藏人口测量方法和相关科学问题的重大进展。研究人员是唯一合格的第一个与现场研究人员密切合作的统计学家,以系统化RDS的统计学理解。该项目的创新之处在于它挑战了现有的RDS推理范式,并提出了一种基于前沿统计思想和模型的新方法。 缺乏统计方法来证明RDS的合理性。这个项目将建立一个系统的统计框架,以便了解铁路发展战略的长处和短处。在拟议的工作中,我们将代表的RDS过程的复杂性,但也允许量化的不确定性所产生的推断。RDS统计方法的发展对社会科学和行为科学至关重要。 我们将通过面向实地研究人员的开放源码用户友好型软件传播不确定性的估计、诊断和量化方法。这些将适用于未来的RDS数据和RDS调查的大型现有数据库。 公共卫生相关性:受访者驱动抽样(RDS)可以说是最好和最常用的方法,用于调查“隐藏”的人口,很难使用标准的概率方法进行抽样。例子包括注射毒品使用者(IDU)、男男性行为者(MSM)和女性性工作者。在2006年期间,仅两个隐藏人群(男男性行为者、注射吸毒者)中的感染就占美国53,600例新艾滋病毒感染的估计69%(CDC报告,Hall,2008)。这笔赠款的目的是改善这类估计,因为目前的估计做法是有问题的。

项目成果

期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Comment: On the Concept of Snowball Sampling.
评论:关于雪球抽样的概念。
  • DOI:
    10.1111/j.1467-9531.2011.01243.x
  • 发表时间:
    2011-08-01
  • 期刊:
  • 影响因子:
    3
  • 作者:
    Handcock MS;Gile KJ
  • 通讯作者:
    Gile KJ
Comment.
评论。
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Mark Stephen Handcock其他文献

Mark Stephen Handcock的其他文献

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{{ truncateString('Mark Stephen Handcock', 18)}}的其他基金

Innovations in Network Modeling for HIV Prevention Studies
HIV 预防研究网络建模的创新
  • 批准号:
    8659962
  • 财政年份:
    2013
  • 资助金额:
    $ 14.7万
  • 项目类别:
Statistics and Methods Core
统计和方法核心
  • 批准号:
    8368541
  • 财政年份:
  • 资助金额:
    $ 14.7万
  • 项目类别:
Statistics and Methods Core
统计和方法核心
  • 批准号:
    8786401
  • 财政年份:
  • 资助金额:
    $ 14.7万
  • 项目类别:
Statistics and Methods Core
统计和方法核心
  • 批准号:
    8987433
  • 财政年份:
  • 资助金额:
    $ 14.7万
  • 项目类别:
Statistics and Methods Core
统计和方法核心
  • 批准号:
    8399019
  • 财政年份:
  • 资助金额:
    $ 14.7万
  • 项目类别:

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