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.

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.像任何涉及人类受试者的研究一样，缺少数据在EMA研究中也很常见。例如，当研究这个问题“情绪是在随机背景时间与情绪不同的情绪不同的情绪”时，由于研究参与者对这些随机提示的不反应，可能会有适度的数据丢失。人们通常会怀疑，这种迅速反应引起的丢失数据是非随机的，因为迅速的无响应行为与同时的情绪结果有关，因此，即使计划的提示是随机的，观察到的数据也可能是一个人背景情绪的选定非随机子集。在EMA数据的分析中，需要正确考虑这种不可忽视的丧失性。但是，与更传统的研究不同，密集的EMA数据中的不可忽视的缺失构成了重大的新分析挑战，并要求采用更通用，灵活和健壮的方法，这些方法适用于EMA研究，以量化和提高可靠性，有效性 和收集数据的可用性。 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），建立了一系列青少年，有吸烟和尼古丁依赖性的高风险。这项研究有可能为EMA数据分析做出方法论和实质性贡献，并了解情绪变化和吸烟依赖性之间的关系。原则上简单的统计措施和可访问的软件将使研究人员可以通过EMA或其他类型的测量密集型方法来方便地量化经验发现的鲁棒性，从而使多种数据类型和模型，缺失的数据模式和机制不可降低缺失。这些方法还可以轻松地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（Mobile Heath）研究。