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

项目概要/摘要 拟议的 K01 指导研究科学家发展奖将为 Manshu Yang 博士做好准备。到 成为开发尖端实用统计方法的独立研究员，以解决 及时发布吸毒和成瘾（DUA）研究中缺失数据的问题。杨博士现任助理 罗德岛大学定量健康心理学教授。概述的提案构建 基于她在统计学和心理学方面的培训和研究经验，将有助于她成为一名 致力于将最先进的方法论和实质性理论联系起来以推进知识的学者 DUA 病因学和干预策略。 DUA 具有多种有害影响，并且持续加剧 美国的公共卫生问题。近二十年来，生态瞬时评估（EMA） 越来越多地用于帮助研究人员了解心理社会和背景因素对 现实世界中几乎实时的物质使用情况，以便可以采取更有效的干预措施 相应地发展起来。然而，随着使用 EMA 的机会而来的是一个重要的方法论 挑战：缺失响应是不可避免的，通常是大量的，并且在数据分析中没有得到正确处理， 因此大大增加了研究人员得出错误结论和开发无效或无效结论的风险 甚至不安全的干预措施。当前的方法无法解决 DUA 中所有独特的方法学挑战 EMA 研究由于其复杂的缺失数据模式和无法测试的缺失数据假设。上 另一方面，EMA 提供了一个独特的机会，通过很快重新提示参与者来应对这些挑战 他们错过了 EMA 调查以检索数据。这些后来检索到的数据很容易从 现有数据（例如，捕获前一天错过的 DUA 后果的早间报告）或可以轻松地 添加到自动化 EMA 系统而不改变 EMA 计划。概述的提案包括 全面的指导和教学计划，以支持杨博士的职业发展并推动她的进步 DUA 病因学/干预、EMA 设计、数据管理和分析、贝叶斯知识和技能 缺少数据分析和统计编程。具体来说，拟议研究的目的是 (1) 使用所有可用数据（最初观察到的和后来检索的）来表征缺失数据机制 DUA EMA，(2) 开发一种新颖的贝叶斯方法来处理缺失数据并做出有效推断 DUA 病因学，(3) 开发贝叶斯敏感性分析方法来测试研究结果的稳健性 可能偏离缺失数据的假设。拟议的研究将调查缺失数据问题 并使用来自三个 NIDA 资助项目的经验 EMA 数据集和计算机开发分析方法 以经济高效的方式模拟数据。研究开发的统计方法将有很大帮助 研究人员阐明了 DUA 的现实机制，并制定了量身定制的有效干预措施。

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.