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）：为了响应对开发适用于行为和社会科学研究的新数据和新方法的适当分析技术的强烈需求，我们建议开发原则性和简洁的统计方法，这些方法适用于使用密集测量方法的研究，例如生态瞬时评估（EMA）方法，以量化实证结果的可靠性和有效性，以避免不可忽视的缺失。与任何涉及人类受试者的研究一样，数据缺失在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的情绪是否不同”这个问题时，可能会有适量的数据丢失，因为研究参与者对这些随机提示没有反应。人们常常怀疑，由这种提示性不反应引起的缺失数据是非随机的，因为提示性不反应行为与同期情绪结果有关，因此观察到的数据可能是一个人背景情绪的选择非随机子集，即使计划提示是随机的。这种不可忽视的缺失需要在EMA数据分析中得到适当的解释。然而，与更传统的研究不同，密集EMA数据中不可忽视的缺失带来了重大的新分析挑战，需要更通用、灵活和强大的方法，适用于EMA研究，以量化和提高可靠性、有效性