Improving Methods for Dealing with Missing Data in Drug Use and Addiction Research: The Use of Later-Retrieval in Ecological Momentary Assessment

改进处理药物使用和成瘾研究中缺失数据的方法：后期检索在生态瞬时评估中的应用

• 批准号: 10721443
• 负责人: Manshu Yang
• 金额: $ 17.17万
• 依托单位: UNIVERSITY OF RHODE ISLAND
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10721443
• 关键词: Address; Adoption; Affect; Alcohol consumption; Alcohols; Area; Bayesian Analysis; Bayesian Method; Bayesian Modeling; Behavior; Cannabis; Career Mobility; Characteristics; Code; Complement; Complex; Computers; Data; Data Analyses; Data Collection; Data Set; Dedications; Development; Development Plans; Doctor of Philosophy; Drug Addiction; Drug usage; Ecological momentary assessment; Emotions; Environment; Etiology; Evaluation; Feeling; Functional disorder; Funding; Goals; Health Psychology; Heavy Drinking; Hour; Intervention; Knowledge; Light; Longitudinal Studies; Mandatory Reporting; Marijuana; Measurement; Mentored Research Scientist Development Award; Mentorship; Methodology; Methods; Names; National Institute of Drug Abuse; National Research Council; Participant; Pattern; Persons; Pharmaceutical Preparations; Prevention strategy; Process; Psychology; Psychosocial Factor; Psychosocial Influences; Public Health; Publications; Recommendation; Reporting; Reproducibility; Research; Research Personnel; Retrieval; Rhode Island; Risk; Schedule; Smoking; Social Environment; Statistical Data Interpretation; Statistical Methods; Surveys; System; Target Populations; Techniques; Technology; Testing; Time; Tobacco; Training; Trauma; Universities; Work; behavioral health; career; career development; contextual factors; cost; cost effective; data handling; data management; design; effective intervention; efficacious intervention; experience; improved; intimate partner violence; marijuana use; marijuana user; method development; novel; professor; programs; psychological symptom; psychosocial; research study; response; skills; smartphone application; social; statistics; substance use; theories; treatment strategy; university student

Project Summary/Abstract The proposed K01 Mentored Research Scientist Development Award will prepare Manshu Yang, Ph.D. to become an independent researcher in developing cutting-edge and practical statistical methodology to address the timely issue of missing data in drug use and addiction (DUA) research. Dr. Yang is currently an Assistant Professor of Quantitative Health Psychology at the University of Rhode Island. The outlined proposal builds upon her training and research experience in statistics and psychology and will facilitate her path to become a scholar dedicated to bridging state-of-the-art methodologies and substantive theory to advance knowledge of DUA etiology and intervention strategies. DUA has a myriad of deleterious impacts and continues to raise public health concerns in the US. In the past two decades, ecological momentary assessment (EMA) has been increasingly used to help researchers understand the influence of psychosocial and contextual factors on substance use in the real world and nearly in real time, so that more efficacious interventions can be developed accordingly. However, along with the opportunity of using EMA comes a significant methodological challenge: missing responses are inevitable, often substantial, and not properly handled in data analysis, hence significantly increasing researchers’ risks of reaching incorrect conclusions and developing ineffective or even unsafe interventions. Current methods cannot address all the unique methodological challenges in DUA EMA studies due to their complex missing data patterns and untestable missing data assumptions. On the other hand, EMA brings a unique opportunity to address these challenges by re-prompting participants shortly after they missed an EMA survey to retrieve their data. Such later-retrieved data are readily available from existing data (e.g., morning reports capturing missed DUA consequences in the prior day) or can be easily added to an automated EMA system without altering EMA schedule. The outlined proposal includes a comprehensive mentorship and didactic plan to support Dr. Yang’s career development and advance her knowledge and skills in DUA etiology/intervention, EMA design, data management, and analysis, Bayesian missing data analysis, and statistical programming. Specifically, the aims of the proposed research study are (1) to use all available data (initially observed and later retrieved) to characterize missing data mechanisms in DUA EMA, (2) to develop a novel Bayesian method for handling missing data and making valid inference on DUA etiology, (3) to develop a Bayesian sensitivity analysis method to test the robustness of findings to possible departures from missing data assumptions. The proposed study will investigate missing data issues and develop analysis methods using empirical EMA datasets from three NIDA-funded projects and computer- simulated data in a cost-effective way. Statistical methods developed from the study will greatly help researchers elucidate the real-world mechanisms of DUA and develop tailored, effective interventions.

项目摘要/摘要 拟议的K01指导研究科学家发展奖将为Manshu Yang博士准备。到 成为开发前沿和实用统计方法的独立研究人员，以解决 毒品使用和成瘾（DUA）研究中缺少数据的及时问题。杨博士目前是助手 罗德岛大学定量健康心理学教授。概述的提案构建 根据她在统计和心理学方面的培训和研究经验，并将促进她成为 学者致力于桥接最先进的方法和实质性理论，以促进知识 DUA病因和干预策略。杜阿（Dua）有无数的有害影响，并继续提高 美国的公共卫生问题。在过去的二十年中，生态时刻评估（EMA）一直是 越来越多地帮助研究人员了解心理和情境因素对 现实世界中的物质使用几乎是实时的，因此可以更有效的干预措施 相应地开发。但是，随着使用EMA的机会，有一个重要的方法论 挑战：缺少响应是不可避免的，通常是实质的，并且在数据分析中无法正确处理， 因此，显着增加了研究人员得出不正确结论并发展无效或 甚至不安全的干预措施。当前方法无法解决DUA中所有独特的方法论挑战 EMA由于其复杂的数据模式和无法测试的数据假设而进行的研究。在 另一方面，EMA带来了一个独特的机会来解决这些挑战，通过重新提出参与者来应对这些挑战 他们错过了EMA调查以检索数据后。这样的后期退回数据很容易从 现有数据（例如，早晨的报告在上一天捕获DUA后果），也可以轻松 在不更改EMA计划的情况下添加到自动化EMA系统中。概述的提案包括 全面的精神训练和教学计划，以支持杨博士的职业发展并推进她 DUA病因/干预，EMA设计，数据管理和分析的知识和技能，贝叶斯 缺少数据分析和统计编程。具体而言，拟议的研究的目的是 （1）使用所有可用的数据（最初观察并后来检索）以表征缺失的数据机制 DUA EMA，（2）开发一种新颖的贝叶斯方法来处理缺失的数据并对 DUA病因，（3）开发一种贝叶斯灵敏度分析方法来测试发现的鲁棒性 可能偏离丢失的数据假设。拟议的研究将调查缺失的数据问题 并使用来自三个NIDA资助的项目和计算机的经验EMA数据集开发分析方法 以具有成本效益的方式模拟数据。从研究开发的统计方法将大大帮助 研究人员阐明了DUA的实际机制，并开发了量身定制的有效干预措施。