Variance Modeling of Smoking-related EMA Data

吸烟相关 EMA 数据的方差建模

• 批准号: 7706604
• 负责人: Donald Hedeker
• 金额: $ 20.72万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7706604
• 关键词: Address; Adolescent; Area; Books; Calculi; Classification; Computer software; Data; Data Analyses; Dependency; Development; Emotional; Future; Grant; Health; Linear Models; Malignant Neoplasms; Measurement; Methodology; Methods; Modeling; Moods; NIH Program Announcements; National Cancer Institute; Nature; Nicotine; Nicotine Dependence; Outcome; Pain Measurement; Pattern; Persons; Program Research Project Grants; Relapse; Research; Research Proposals; Role; Science; Scientist; Smoke; Smoker; Smoking; Statistical Methods; Symptoms; Techniques; Time; Tobacco; Variant; adolescent smoking; anticancer research; behavioral/social science; cohort; diaries; diet and exercise; high risk; improved; innovation; meetings; mood regulation; novel strategies; programs; public health relevance; smoking cessation; social; social science research; symposium; tool

DESCRIPTION (provided by applicant): As noted in Program Announcement # PAR-08-213, Methodology and Measurement in the Behavioral and Social Sciences, there is a need for "developing appropriate analytic techniques for use with new kinds of data and new approaches to behavioral and social science research." This proposal is aimed at addressing this need for data generated from diary or Ecological Momentary Assessment (EMA) methods. Use of EMA methods in smoking and cancer research has become a new and vital approach. Data from EMA studies are inherently multilevel in nature with, for example, (level-1) observations nested within (level-2) days and (level-1) subjects. Thus, linear mixed models (LMMs, aka multilevel or hierarchical linear models) are increasingly used for analysis of EMA data. In EMA studies, it is not unusual for there to be up to thirty or forty observations per subject, and this allows greater modeling opportunities than what conventional LMMs for longitudinal data allow. In particular, one very promising extended approach is the modeling of variances as a function of covariates, in addition to their effect on overall mean levels. For example, if a smoker's mood is the outcome, then one can consider the effect of covariates on their mood level (e.g., how happy/sad are they on average), as well as on their variation in mood (e.g., how labile/erratic is their mood). Or, one can examine mood changes when a person smokes in terms of the mean (does mood improve?) and variance (does mood stabilize?), and what variables might be related to those smoking-related changes of mood level and variation. Thus, by allowing within-subject variance to be a function of covariates, we can more directly examine the hypothesis that smoking helps to regulate mood. Thus, the aims of the proposed study are to (1) develop accessible software for general 3-level modeling of means and variances of EMA data; and (2) examine the role of smoking on mood regulation in adolescents using data from our program project grant, "Social and Emotional Contexts of Adolescent Smoking Patterns" (NCI grant #PO1 CA98262), which established a cohort of adolescents at high risk for the development of smoking and nicotine dependence. This study has the potential to make notable methodological and substantive contributions for analysis of EMA data and understanding the relationship between mood variation and smoking dependency. These methods can easily generalize to a variety of cancer -relevant research areas, including the assessment of pain and symptoms, as well as diet and exercise. PUBLIC HEALTH RELEVANCE: Use of Ecological Momentary Assessment (EMA) methods in smoking and cancer research has become a new and vital approach, allowing for the examination of smoking-related phenomena as they happen over time. As noted in Program Announcement # PAR-08-213, Methodology and Measurement in the Behavioral and Social Sciences, there is a need for "developing appropriate analytic techniques for use with new kinds of data and new approaches to behavioral and social science research." This proposal is aimed at addressing this need by developing statistical methods and software for EMA data consisting of observations nested within days and subjects, allowing for effects on both average levels (is the variable consistently higher or lower) and levels of variation (is the variable more labile or erratic). With the new focus on variation, the proposed research will examine previously un-addressable questions in smoking research.

描述（由申请人提供）：如计划公告中所述，PAR-08-213，行为和社会科学的方法和测量值，需要“开发适当的分析技术，以与新型数据一起使用，并采用新型的数据以及新的行为和社会科学研究方法”。该建议旨在解决这种对日记或生态瞬时评估（EMA）方法产生的数据的需求。在吸烟和癌症研究中使用EMA方法已成为一种新的至关重要的方法。来自EMA研究的数据本质上是自然界的，例如，（1级）观察值嵌套在（级别2）天和（1级）主题之内。因此，线性混合模型（LMM，又名多级或分层线性模型）越来越多地用于分析EMA数据。在EMA研究中，每个受试者的三十或四十个观察结果与纵向数据允许的常规LMM相比，这允许更大的建模机会。特别是，除了对整体平均水平的影响外，一种非常有前途的扩展方法是方差的建模作为协变量的函数。例如，如果吸烟者的情绪是结果，那么人们可以考虑协变量对他们的情绪水平的影响（例如，平均而言有多快乐/悲伤），以及他们的情绪变化（例如，他们的情绪不稳定/不稳定）。或者，当一个人在平均值（情绪改善）和差异方面吸烟（情绪稳定？）时，可以检查情绪变化，哪些变量可能与与吸烟有关的情绪水平和变化的变化有关。因此，通过允许受试者内方差成为协变量的函数，我们可以更直接地研究吸烟有助于调节情绪的假设。因此，拟议的研究的目的是（1）开发可访问的软件，用于对EMA数据的均值和方差的一般3级建模； （2）使用我们的计划项目赠款中的数据“青少年吸烟模式的社会和情感环境”（NCI Grant＃PO1 CA98262）研究吸烟对青少年情绪调节的作用，该批准建立了一系列青少年，该群体具有很高的吸烟和尼古丁依赖性的风险。这项研究有可能做出显着的方法论和实质性贡献，以分析EMA数据并了解情绪变化与吸烟依赖性之间的关系。这些方法可以轻松地推广到各种癌症的研究领域，包括评估疼痛和症状以及饮食和运动。公共卫生相关性：在吸烟和癌症研究中使用生态瞬时评估（EMA）方法已成为一种新的至关重要的方法，可以随着时间的流逝而检查与吸烟相关的现象。正如计划公告＃PAR-08-213中所述，行为和社会科学的方法和测量方法，需要“开发适当的分析技术，以与新型数据一起使用，并采用新的数据和新方法来进行行为和社会科学研究”。该建议旨在通过开发统计方法和软件来解决这种需求，以用于在几天和受试者内嵌套的观测值组成的EMA数据，从而允许对平均水平的影响（始终如一地或更低）和变化水平（是可变的或更不稳定或不稳定的）。随着对变化的新关注，拟议的研究将研究吸烟研究中先前不可避免的问题。