Variance Modeling of Smoking-related EMA Data

描述（由申请人提供）：正如计划公告# PAR-08-213中所指出的，行为和社会科学的方法和测量，需要“开发适当的分析技术，用于新类型的数据和行为和社会科学研究的新方法。“这项提案旨在解决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和重要的方法。EMA研究的数据本质上是多水平的，例如，（1级）观察结果嵌套在（2级）天和（1级）受试者中。因此，线性混合模型（Linear mixed models，又称多水平或层次线性模型）越来越多地用于EMA数据的分析。在EMA研究中，每个受试者有多达30或40个观察结果并不罕见，这使得建模机会比传统的纵向数据LRDR更大。特别是，一个非常有前途的扩展方法是建模的方差作为一个函数的协变量，除了他们对整体平均水平的影响。例如，如果吸烟者的情绪是结果，则可以考虑协变量对其情绪水平的影响（例如，他们平均有多高兴/悲伤），以及他们情绪的变化（例如，他们的情绪有多不稳定/不稳定）。或者，人们可以根据平均值来检查一个人吸烟时的情绪变化（情绪改善了吗？）和方差（情绪稳定吗？），以及哪些变量可能与吸烟相关的情绪水平和变化有关。因此，通过允许受试者内方差是协变量的函数，我们可以更直接地检验吸烟有助于调节情绪的假设。因此，本研究的目的是：（1）开发一个通用的软件，用于EMA数据的均值和方差的三水平建模;以及（2）使用我们的项目资助数据“青少年吸烟模式的社会和情感背景”来研究吸烟对青少年情绪调节的作用（NCI资助#PO1CA98262），其建立了一组处于吸烟和尼古丁依赖发展的高风险的青少年。这项研究有可能作出显着的方法和实质性的贡献EMA数据的分析和了解情绪变化和吸烟依赖之间的关系。这些方法可以很容易地推广到各种癌症相关的研究领域，包括疼痛和症状的评估，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和重要的方法，允许检查吸烟相关的现象，因为它们随着时间的推移发生。正如计划公告# PAR-08-213，行为和社会科学的方法和测量中所指出的，有必要“开发适当的分析技术，用于新类型的数据和行为和社会科学研究的新方法。“这项提案旨在通过开发EMA数据的统计方法和软件来满足这一需求，EMA数据由嵌套在天数和主题内的观察结果组成，考虑到对平均水平（变量始终较高或较低）和变化水平（变量更不稳定或不稳定）的影响。随着对变化的新关注，拟议的研究将检查吸烟研究中以前无法解决的问题。

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.