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: 10552018
• 负责人: Sung-Hee Lee
• 金额: $ 21.9万
• 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/10552018
• 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.

受访者驱动器采样（RDS）是用于难以样本人群的招聘方法 由于高度污名化或非法行为，数量很少和/或难以捉摸。对于这些群体，传统 概率采样很少提供可行性，因为它需要积极的筛选成本才能找到 合格的人，即使是合格的人，他们隐藏的渴望也会产生假否定性。 基于类似特征的人们形成某种类型的社交网络的前提，RD会利用现有的 招聘网络已应用于众多研究。是什么使RD与传统不同 抽样是，招聘过程主要由参与者自己控制。 推荐要求参与者从其网络中招募其他合格人员。有机社会的使用 抽样网络是RDS的创新功能。但是，这带来了一个主要挑战。在 为了大写RD，参与者需要与招聘请求合作。由于 非合作，样本可能会停止生长，从而导致项目超支。但是，缺乏 在文献中，注意这个非合作过程使RDS数据收集进步极为困难 在设计阶段进行预测，以及面对不良（通常是意外）挑战的面孔时， 研究人员被迫对计划外的设计更改（例如，提供更大的激励措施） 希望使RDS“工作”。此外，非合作导致侵犯关键 RDS推论的假设。总而言之，RDS的当前做法缺乏运营和统计 可重复性，使其科学完整性值得怀疑。 这项研究试图通过提出Adaptive-RD（A-RDS）作为设计来提高RD的可重复性 框架并提供实用工具，研究人员依靠成功实施RD和 通过制定A-RDS特定设计指南和软件，该指南将允许监视RDS数据收集 进步并改善推断，这些推论紧密反映了真实的数据生成过程。在A-RD下，我们将 计划设计适应策略，包括数据收集之前的适应指标和规则。 在现场工作中，我们没有假设参与者的相同招聘合作模式 将从传入的数据中预测个人级合作属性，并定制数量和类型 根据预先指定的规则收到的每个参与的优惠券。为此，数据收集进度 将密切监控并用于做出适应决策。特别是，这种方法在经验上是 应用于PWID研究，以提供用于解决阿片类药物使用快速升级问题的数据。 通过向研究社区提供实用但基于数据的基于规则的工具，拟议的研究将 促进研究人员对RD的运营的控制，不仅可以提高可重复性，还可以 增加有效推论所需的RD中关键假设的机会增加。