Using Multiple Data Sources to Improve Respondent Driven Sampling Estimation

描述（由申请人提供）：使用多个数据源，以改善受访者驱动的抽样估计摘要：这项研究致力于获得有效的估计流行性传播疾病（STD）感染和危险的和预防性的健康行为在一个隐藏的人群，女性性工作者在中国。我们利用多个观察方案，以提高响应者驱动采样（RDS）的效用。RDS是一种越来越流行的抽样方法，用于招募隐藏人口的样本，目的是为注射毒品使用者、性工作者、男男性行为者和综合调查研究不太可能揭示其地位特征的人口群体提供一个基于概率的推理结构，因为他们是罕见的，社会上的耻辱和/或非法的。RDS利用隐藏人群的社会网络结构来识别和采访参与者。它的有效性依赖于严格的理论假设，参与者的推荐实践，新的参与者和结构的基础网络，没有观察到。尽管CDC和类似组织在RDS中进行了大量投资，但我们对其在保持其代表性承诺方面的有效性几乎没有经验评估。在这里，我们建议改善RDS的女性性工作者在中国的代表性，从理论到实证领域的考虑，对现实世界的转介过程。我们完成了这一点，我们最近通过两个RDS研究和场地为基础的抽样方法在上海和柳州（广西省）收集的数据相结合的分析。我们使用这种重叠的数据收集来观察嵌入在RDS招聘过程中的社交网络信息，并逼真地模拟RDS设置，以开发适应所观察到的网络转介过程的改进的RDS估计。我们提取的指导方针，研究人员使用RDS方法所需的步骤，以提高RDS估计代表其他隐藏的人口。 公共卫生相关性：本研究的主要目的是获得有效的估计流行性传播疾病（STD）感染和危险行为在一个隐藏的人群，女性性工作者在中国，抽样与不同的策略，包括受访者驱动抽样（RDS）和改进RDS方法和程序，使用收集的数据作为这种多个数据收集工作的一部分。这将导致产生更准确的信息，对这一人群，更好地了解其对更大的人口健康动态的影响，并指导研究人员使用RDS的步骤，以改善RDS估计代表其他隐藏的人群。

DESCRIPTION (provided by applicant): Using Multiple Data Sources to Improve Respondent Driven Sampling Estimation ABSTRACT: This study addresses efforts to obtain valid estimates of the prevalence of sexually transmitted disease (STD) infection and risky and preventive health behaviors in a hidden population, female sex workers in China. We take advantage of multiple observations schemas to improve the utility of Respondent Driven Sampling (RDS). RDS is an increasingly popular sampling method used to recruit samples of hidden populations with the aim to provide a probability-based inferential structure for representations of populations such as injection drug users, sex workers, men who have sex with men and population groups whose status characteristics are not likely to be revealed by omnibus survey research because they are rare and socially stigmatized and/or illegal. RDS capitalizes on the social network structure of the hidden population to identify and interview participants. Its validity rests on stringent theoretical assumptions about the referral practices of participants to new participants and the structure of the underlying network that are not observed. Despite significant investments by CDC and similar organizations in RDS, we have few empirical evaluations of its effectiveness at keeping its representation promise. Here we propose to improve RDS for representation of female sex workers in China by moving considerations regarding real-world referral processes from the theoretical to the empirical realms. We accomplish this with a combination of analyses of data we have recently collected through two RDS studies and a venue-based sampling approach in Shanghai and Liuzhou (Guangxi Province). We use this overlapping data collection to observe the social network information embedded in the RDS recruitment process and to realistically simulate RDS settings in order to develop improved RDS estimates adaptive to the observed network referral process. We distill guidelines for researchers using RDS methods on needed steps to improve RDS estimation for representation of other hidden populations. PUBLIC HEALTH RELEVANCE: The main aims of this research are to obtain valid estimates of the prevalence of sexually transmitted disease (STD) infection and risk behaviors in a hidden population, female sex workers in China, sampled with different strategies including Respondent Driven Sampling (RDS) and to improve RDS methodology and procedures using data collected as part of this multiple data collection effort. This will lead to the production of more accurate information on this population, a better understanding of its impact on the larger population health dynamics, and guidelines for researchers using RDS on steps to improve RDS estimation for representation of other hidden populations.