Dynamic Multichain Graphical Models for the Analysis of Childhood Obesity Data

描述（由申请人提供）：该项目旨在制定有针对性的策略和方法，以解决儿童肥胖数据分析中具有挑战性的方法学问题。特别感兴趣的是统计和计算模型，可以参加多层次的风险行为和风险因素，被认为是儿童肥胖的直接或间接原因。多层次的视角可以通过一个行为-社会-生态概念模型来捕捉，其中个人因素，信仰;口味偏好;饮食结构;环境因素，如家庭，学校和食物供应;社会因素，如文化规范;生理因素，如子宫内和遗传倾向，被包括在解释儿童肥胖的因果链中，所谓的风险调节器（一组稳定的生态条件），向上和向下调节肥胖结果的概率。为了操作和实施概念性的多层次模型，我们建议建立，围绕一个核心技术，我们称之为动态多链图形模型（DMGM），一套相关的策略和方法（1）数据的预处理，（2）建模的多个因果途径肥胖。DMGM将直接风险因素和风险监管者分为两个不同的空间，即所谓的因果空间和监管空间。对因果空间内基于机制的模型的兴趣集中在直接风险因素的联合分布上。 或者，监管空间内的风险监管机构通过对系统参数施加的基于回归的模型来影响因果空间中的变量系统;然而，这里对回归量的联合分布没有什么兴趣。通过利用因果空间和监管空间的分离所提供的概念和计算优势，DMGM能够处理三个或更多个级别的数据，以识别直接和间接风险变量。其他策略也可用于处理特定空间内的多级数据。 除了DMGM，该项目还将包括开发一些其他工具，这些工具专门用于解决儿童肥胖研究人员在其实证工作中经常遇到的分析复杂性。该工具包包括一个基于递归分区的决策树，可以处理时态数据，一个功能性的数据分析工具，用于处理历史数据，以及潜在变量模型，用于总结多个测量和处理空间内聚类效应。该项目的其他具体目标包括应用所提出的方法，以两个国家的数据集收集，分别从路易斯安那州儿童健康研究和心跳！项目，并传播方便用户的软件程序，以增加项目的潜在影响。 公共卫生相关性：作为一种社会流行病，儿童肥胖是多层次的个人行为和危险因素之间相互作用的结果，以及跨越家庭、学校和社区等多个来源的致肥胖生态因素。该项目利用由临床和方法科学家组成的广泛的跨学科团队，开发先进的分析工具，帮助临床医生更好地了解多种风险因素如何导致儿童肥胖的机制，包括风险因素的相对重要性。

DESCRIPTION (provided by applicant): This project aims to develop targeted strategies and methods for solving challenging methodological problems in the analysis of data on childhood obesity. Of special interest are statistical and computational models that can attend to the multiple levels of risk behaviors and risk factors that are deemed to be direct or indirect causes of childhood obesity. The multilevel perspective can be captured through a behavioral-social-ecological conceptual model in which personal factors, beliefs; taste preferences; dietary composition; environmental factors such as homes, schools, and food availability; societal factors such as cultural norms; and physiological factors such as intrauterine and genetic disposition are included either as factors in causal chains explaining childhood obesity or as so-called risk regulators (a set of stable ecological conditions) that up- and down-regulate probabilities of obesogenic outcomes. In order to operationalize and implement the conceptual multilevel model, we propose to build, around a core technology that we call the Dynamic Multi-chain Graphical Model (DMGM), a set of related strategies and methods for (1) the preprocessing of data, and (2) the modeling of multiple causal pathways to obesity. The DMGM separates direct risk factors and risk regulators into two distinct spaces the so-called causal space and the regulatory space. Interest in the mechanism-based model within the causal space focuses on the joint distribution of direct risk factors. Alternatively, risk regulators within the regulatory space affect the system of variables in the causal space through regression-based models imposed upon system parameters; the joint distribution of regressors, however, is of little interest here. By capitalizing on the conceptual and computational advantages offered by the segregation of the causal and regulatory spaces, the DMGM is able to handle three or more levels of data, to the extent to which direct and indirect risk variables can be identified. Other strategies are also available for handling multiple levels of data within a specific space. Besides the DMGM, this project will also include the development of a number of other tools that are especially designed to address the analytical complexity that childhood obesity researchers often encounter in their empirical work. The toolkit includes a recursive-partition-based decision tree that can handle temporal data, a functional data-analysis tool for processing history data, and latent-variable models for summarizing multiple measurements and handling within-space clustering effects. Other specific aims of the project include the application of the proposed methods to two national data sets collected, respectively, from the Louisiana Child Health Study and the Heartbeat! Project, and the dissemination of a user-friendly software program for increasing the potential impact of the project. PUBLIC HEALTH RELEVANCE: As a social epidemic, childhood obesity is the result of the interaction between many levels of personal behavior and risk factors, as well as obesogenic ecological factors that span many sources, including families, schools, and communities. Using a broad and interdisciplinary team of clinical and methodology scientists, this project develops advanced analytic tools that could help clinicians better understand the mechanisms of how multiple levels of risk factors lead to childhood obesity, including the relative importance of the risk factors.