Improving Reproducibility of Respondent Driven Sampling through Adaptive Design

通过自适应设计提高受访者驱动抽样的可重复性

• 批准号: 10761958
• 负责人: Sung-Hee Lee
• 金额: $ 2.77万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10761958
• 关键词: Address; Attention; Behavior; Behavioral; Characteristics; Communities; Computer software; Data; Data Collection; Eligibility Determination; Face; Foundations; Funding; Generations; Goals; Guidelines; HIV; Immigrant; Incentives; Individual; Injecting drug user; Internet; Investments; Knowledge; Literature; Markov Chains; Measures; Methods; Monitor; Outcome; Participant; Pattern; Persons; Population; Probability Samples; Process; Reporting; Reproducibility; Research; Research Personnel; Respondent; Rest; Risk; Running; Sample Size; Sampling; Sampling Studies; Social Network; Specific qualifier value; Stigmatization; Subgroup; Time; United States National Institutes of Health; Work; blind; cost; dashboard; design; ethnic minority; experience; improved; innovation; meetings; operation; opioid use; peer; population health; racial minority; real time monitoring; recruit; response; rural area; screening; statistics; suburb; success; tool; training opportunity; trait; transgender

Respondent driven sampling (RDS) is a recruitment method for hard-to-sample populations that are rare in number and/or elusive due to highly-stigmatized or illicit behaviors. For these groups, traditional probability sampling rarely offers feasibility, because it requires prohibitively high screening costs to locate eligible persons, and, even when eligible persons are located, their desire to hide produces false negatives. Based on the premise that people of similar traits form some type of social networks, RDS exploits the existing networks for recruitment and has been applied to numerous studies. What sets RDS apart from traditional sampling is that the recruitment process is mostly controlled by participants themselves through their chain- referral that asks participants to recruit other eligible persons from their networks. The use of organic social networks for sampling is an innovative feature of RDS. This, however, comes with one major challenge. In order to capitalize on RDS, participants need to cooperate with recruitment requests. Because of noncooperation, the sample may stop growing in size, resulting in a project overrun. However, the lack of attention to this noncooperation process in the literature makes RDS data collection progress extremely difficult to predict at the design stage, and when faces with undesirable (and often unexpected) challenges, researchers are forced to make unplanned design changes (e.g., offering larger incentives) on the spur of the moment in hopes of making RDS “work”. Additionally, noncooperation leads to a violation of a critical assumption of RDS inferences. In sum, the current practice of RDS lacks operational and statistical reproducibility, making its scientific integrity questionable. This study attempts to improve reproducibility of RDS by proposing Adaptive-RDS (A-RDS) as a design framework and to provide practical tools on which researchers rely for successful implementation of RDS and by developing A-RDS specific design guidelines and software that will allow monitoring RDS data collection progress and improve inferences that closely mirror the true data generation process. Under A-RDS, we will plan design adaptation strategies, including indicators and rules for adaptations prior to the data collection. During the field work, instead of assuming the same recruitment cooperation patterns across participants, we will predict individual-level cooperation propensities from incoming data and tailor the number and type of coupons for each participant received based on the pre-specified rules. For doing so, data collection progress will be closely monitored and used for making adaptation decisions. In particular, this approach is empirically applied to PWID studies to provide data for addressing rapidly escalated issues with opioid use. By providing a practical yet data-driven, rule-based tool to the research community, the proposed study will boost researchers' control on the operations of RDS, leading to not only improved reproducibility but also increased chances of meeting critical assumptions in RDS required for valid inferences.

受访者驱动抽样 (RDS) 是一种针对难以抽样人群的招募方法，这些人群 对于这些群体来说，由于受到高度侮辱或非法行为，其数量很少和/或难以捉摸。 概率抽样很少提供可行性，因为它需要极高的筛选成本来定位 合格人员，并且即使找到合格人员，他们隐藏的愿望也会产生假阴性。 基于具有相似特征的人形成某种类型的社交网络的前提，RDS 利用了现有的 RDS 与传统的招聘网络有何不同？ 抽样的特点是招聘过程主要是由参与者自己通过他们的链条控制的。 要求参与者从他们的网络中招募其他合格人员的推荐 使用有机社交。 采样网络是 RDS 的一项创新功能，但这也带来了一个重大挑战。 为了利用 RDS，参与者需要配合招聘请求。 如果不合作，样本规模可能会停止增长，从而导致项目超支。 文献中对这种不合作过程的关注使得 RDS 数据收集进展极其困难 在设计阶段以及面临不良（通常是意想不到的）挑战时进行预测， 研究人员被迫做出计划外的设计改变（例如，提供更大的激励） 此外，在希望 RDS“发挥作用”的时刻，不合作会导致违反关键规定。 RDS推论的假设 总而言之，目前的RDS实践缺乏可操作性和统计性。 重复性，使其科学完整性受到质疑。 本研究试图通过提出自适应 RDS (A-RDS) 作为设计来提高 RDS 的再现性 框架并提供研究人员成功实施 RDS 所依赖的实用工具 通过开发 A-RDS 特定设计指南和软件来监控 RDS 数据收集 在 A-RDS 下，我们将取得进展并改进密切反映真实数据生成过程的推论。 在数据收集之前规划设计适应策略，包括适应指标和规则。 在实地工作中，我们没有假设相同的招聘合作模式，而是 将根据传入数据预测个人层面的合作倾向，并调整合作的数量和类型 根据预先指定的规则，每个参与者都会收到优惠券，为此，数据收集进度。 将受到密切监测并用于制定适应决策。特别是，这种方法是凭经验得出的。 应用于注射吸毒者研究，为解决阿片类药物使用迅速升级的问题提供数据。 通过向研究界提供实用且数据驱动、基于规则的工具，拟议的研究将 增强研究人员对 RDS 操作的控制，不仅提高了再现性，而且 满足有效推论所需的 RDS 关键假设的机会增加。