Statistical Qualification of the Impact of Missing Data in EMA Studies

EMA 研究中缺失数据影响的统计资格

• 批准号: 9020218
• 负责人: Hui Xie
• 金额: $ 28.66万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9020218
• 关键词: Accounting; Address; Adolescent; Affect; Algorithms; Area; Attention; Behavior; Books; Complex; Computer software; Data; Data Analyses; Data Collection; Dependence; Development; Ecological momentary assessment; Emotional; Evaluation; Grant; Guidelines; Health; Health behavior; Joints; Malignant Neoplasms; Measurement; Measures; Methods; Modeling; Moods; National Cancer Institute; Nature; Nicotine Dependence; Outcome; Parents; Participant; Pattern; Persons; Program Research Project Grants; Public Health; Research; Research Personnel; Role; Science; Scientist; Smoking; Statistical Methods; Systematic Bias; Techniques; Testing; Time; Validity and Reliability; Variant; adolescent smoking; anticancer research; behavioral response; behavioral/social science; cohort; flexibility; high risk; human subject; improved; indexing; innovation; insight; mHealth; meetings; mood regulation; novel strategies; programs; public health research; response; simulation; social; social science research; software development; tool; usability; user-friendly

DESCRIPTION (provided by applicant): In response to the strong demand for developing appropriate analytic techniques for use with new kinds of data and new approaches to behavioral and social science research, we propose to develop principled and parsimonious statistical measures that are applicable in studies using intensive measurement methods, such as Ecological Momentary Assessment (EMA) methods, to quantify the reliability and validity of empirical findings to nonignorable missingness. Like any study involving human subjects, missing data are common in EMA studies. For example, when studying the question "Are moods just prior smoking different than moods during random background times", there can be a moderate amount of missing data because of study participants' nonresponses to those random prompts. It is often suspected that the missing data caused by such prompt nonresponses are nonrandom in that the prompt nonresponse behaviors are related to contemporaneous mood outcomes and consequently the observed data may be a selected nonrandom subset of a person's background mood even though the planned prompts are random. Such nonignorable missingness needs to be properly accounted for in the analysis of EMA data. However, unlike in more traditional studies, nonignorable missingness in intensive EMA data poses significant new analytic challenges and calls for more general, flexible and robust methods that are applicable in EMA studies to quantify and improve the reliability, validity and usability of the collected data. Thus, the aims of the proposed study are to (1) develop general, robust and tractable statistical measures and accessible software for assessing the impact of missing data on analysis of EMA data, and (2) examine the role of smoking on mood regulation in adolescents while accounting for the impact of nonrandom missingness, using data from our program project grant, "Social and Emotional Contexts of Adolescent Smoking Patterns" (NCI grant #PO1 2CA98262), which established a cohort of adolescents at high risk for the development of smoking and nicotine dependence. This study has the potential to make methodological and substantive contributions to EMA data analysis and understanding the relationship between mood variation and smoking dependence. The principled and simple statistical measures and accessible software to be developed will allow researchers to conveniently quantify the robustness of empirical findings from studies using EMA or other types of measurement-intensive methods to nonignorable missingness for a wide range of data types and models, missing data patterns and mechanisms. These methods can also easily generalize to a variety of cancer-relevant research areas, including studies using other types of new intensive measurements, such as mHealth (mobile heath) studies.

描述（由申请人提供）：为了满足开发适当的分析技术以用于行为和社会科学研究的新类型数据和新方法的强烈需求，我们建议开发适用于使用以下方法进行的研究的有原则且简约的统计措施强化测量方法，例如生态瞬时评估（EMA）方法，以量化实证结果对不可忽略缺失的可靠性和有效性。与任何涉及人类受试者的研究一样，缺失数据在 EMA 研究中很常见。例如，当研究“吸烟前的情绪与随机背景时间期间的情绪是否不同”这个问题时，由于研究参与者对这些随机提示没有反应，可能会存在适量的缺失数据。人们经常怀疑由这种即时无反应引起的缺失数据是非随机的，因为即时无反应行为与同时期情绪结果相关，因此观察到的数据可能是一个人背景情绪的选定非随机子集，即使计划的提示是随机的。在 EMA 数据分析中需要适当考虑这种不可忽视的缺失。然而，与更传统的研究不同，密集 EMA 数据中不可忽略的缺失带来了重大的新分析挑战，并需要适用于 EMA 研究的更通用、灵活和稳健的方法来量化和提高可靠性、有效性 以及所收集数据的可用性。因此，拟议研究的目的是（1）开发通用、稳健且易于处理的统计方法和可访问的软件，用于评估缺失数据对 EMA 数据分析的影响，以及（2）检查吸烟对情绪调节的作用青少年吸烟的影响，同时考虑非随机缺失的影响，使用我们的项目资助“青少年吸烟模式的社会和情感背景”（NCI 资助#PO1 2CA98262）中的数据，该资助建立了一个队列青少年有吸烟和尼古丁依赖的高风险。这项研究有可能对 EMA 数据分析和理解情绪变化与吸烟依赖之间的关系做出方法论和实质性贡献。待开发的有原则且简单的统计方法和可访问的软件将使研究人员能够方便地量化使用 EMA 或其他类型的测量密集型方法的研究的实证结果的稳健性，以消除各种数据类型和模型的不可忽略的缺失、缺失数据模式和机制。这些方法还可以轻松推广到各种与癌症相关的研究领域，包括使用其他类型的新密集测量的研究，例如 mHealth（移动健康）研究。