Using Multiple Data Sources to Improve Respondent Driven Sampling Estimation

描述(申请人提供)：使用多个数据来源改进受访者驱动抽样估计摘要：本研究旨在获得中国暗娼人群中性病感染流行情况和危险和预防性健康行为的有效估计。我们利用多种观察模式来提高响应者驱动抽样(RDS)的效用。RDS是一种越来越受欢迎的抽样方法，用于招募隐藏人口的样本，目的是为诸如注射吸毒者、性工作者、男男性行为者和人口群体的代表性提供一个基于概率的推断结构，这些人口群体的状况特征不太可能被综合调查研究揭示，因为他们很罕见，在社会上被污名化和/或非法。RDS利用隐藏人群的社交网络结构来识别和采访参与者。其有效性取决于严格的理论假设，即参与者转介给新参与者的做法以及未被观察到的基础网络结构。尽管疾控中心和类似组织在RDS上进行了大量投资，但我们几乎没有对其履行其代表性承诺的有效性进行实证评估。在这里，我们建议通过将对现实世界转介过程的考虑从理论领域转移到经验领域，来改进《中国》中女性性工作者的RDS。我们结合了最近通过两项RDS研究和在上海和柳州(广西)基于场地抽样方法收集的数据分析来实现这一点。我们使用这种重叠的数据收集来观察RDS招聘过程中嵌入的社交网络信息，并真实地模拟RDS设置，以便制定适应于观察到的网络推荐过程的改进的RDS估计。我们为使用RDS方法的研究人员提炼指南，以改进对其他隐藏种群的RDS估计。

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.