Using Multiple Data Sources to Improve Respondent Driven Sampling Estimation

使用多个数据源改进受访者驱动的抽样估计

• 批准号: 8471552
• 负责人: Giovanna M Merli
• 金额: $ 21.36万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-15 至 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8471552
• 关键词: AIDS/HIV problem; Accounting; Address; Centers for Disease Control and Prevention (U.S.); Characteristics; China; Clinic; Communicable Diseases; Communities; Data; Data Analyses; Data Collection; Data Sources; Development; Effectiveness; Evaluation; Female; Geography; Guidelines; HIV; Health; Health behavior; Infection; Injecting drug user; Interview; Investments; Laboratories; Lead; Methodology; Methods; Modeling; Participant; Population; Population Group; Prevalence; Preventive; Probability; Probability Samples; Procedures; Process; Production; Protocols documentation; Province; Recruitment Activity; Research; Research Personnel; Respondent; Rest; Risk Behaviors; Running; Sampling; Sampling Studies; Scheme; Sexually Transmitted Diseases; Simulate; Social Network; Statistical Models; Structure; Surveys; Techniques; base; design; disorder risk; high risk; improved; intravenous drug user; men who have sex with men; population health; public health relevance; sex; simulation; virtual

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.

描述（由申请人提供）：使用多个数据源改进受访者驱动的抽样估计 摘要：本研究旨在努力获得隐藏人群（女性）中性传播疾病 (STD) 感染患病率以及危险和预防性健康行为的有效估计中国的性工作者。我们利用多种观察模式来提高受访者驱动抽样 (RDS) 的实用性。 RDS 是一种越来越流行的抽样方法，用于招募隐藏人群的样本，旨在为注射吸毒者、性工作者、男男性行为者和具有身份特征的人群等人群的表示提供基于概率的推理结构综合调查研究不太可能揭示它们，因为它们很罕见并且受到社会污名和/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。它的有效性依赖于关于参与者向新参与者的推荐实践以及未观察到的底层网络结构的严格理论假设。尽管 CDC 和类似组织对 RDS 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进行实证评估。在这里，我们建议通过将现实世界转介流程的考虑从理论领域转移到实证领域来改善中国女性性工作者代表性的 RDS。我们通过结合最近通过两项 RDS 研究收集的数据以及上海和柳州（广西省）的基于场地的抽样方法来实现这一目标。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，以便开发适应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了关于改进 RDS 估计以代表其他隐藏群体所需步骤的指南。