Statistical Qualification of the Impact of Missing Data in EMA Studies

描述（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数据和新方法的强烈需求，我们建议开发适用于使用密集测量方法的研究的原则性和简约的统计措施，例如生态瞬时评估（EMA）方法，量化的可靠性和有效性的实证研究结果，以不可否认的失踪。与任何涉及人类受试者的研究一样，EMA研究中缺失数据很常见。例如，当研究“吸烟前的情绪是否与随机背景时间的情绪不同”这个问题时，由于研究参与者对这些随机提示的不响应，可能会有适量的缺失数据。人们经常怀疑，这种提示无反应造成的缺失数据是非随机的，因为提示无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的背景情绪的选定的非随机子集，即使计划的提示是随机的。在EMA数据的分析中，需要适当地考虑这种不可解释的缺失。然而，与更传统的研究不同，密集的EMA数据中的不可替代的缺失带来了重大的新的分析挑战，并要求适用于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 收集的数据的可用性。因此，拟议研究的目的是（1）开发通用的，稳健的和易于处理的统计措施和可访问的软件，用于评估缺失数据对EMA数据分析的影响，以及（2）检查吸烟对青少年情绪调节的作用，同时考虑非随机缺失的影响，使用我们的计划项目资助的数据，“青少年吸烟模式的社会和情感背景”（NCI资助#PO1 2CA98262），建立了一个吸烟和尼古丁依赖发展高风险青少年队列。这项研究有可能作出方法和实质性的贡献EMA数据分析和理解情绪变化和吸烟依赖之间的关系。原则性和简单的统计措施和可访问的软件将允许研究人员方便地量化使用EMA或其他类型的测量密集型方法的研究的实证结果的鲁棒性，以广泛的数据类型和模型，缺失的数据模式和机制的不可否认的缺失。这些方法也可以很容易地推广到各种癌症相关的研究领域，包括使用其他类型的新的密集测量的研究，如mHealth（移动的健康）研究。

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.