Multilevel latent class and social network models for observational adolescent ob

描述（由申请人提供）：青少年肥胖症现在被认为具有复杂的病因。生态模型经常被用来解释这种复杂性，在个人，家庭，同伴，学校和邻里水平上有不同程度的影响。不仅有多个层次的影响，而且这些不同层次之间有双向的影响关系，进一步增加了复杂性。虽然生态学框架经常被用来从概念上解释肥胖，但很少有统计学上测试这种类型的多层次模型的例子。该提案将开发和研究新的统计模型和方法，其中包括潜在变量、社交网络和倾向分数，这些模型和方法同时包括来自对青少年肥胖的多层次影响的多个变量，以便更深入地了解这一现象。利用现有的数据，从一个大的人口为基础的纵向和连续的横截面调查的种族和社会经济上不同的青少年（项目EAT），统计模型和适当的估计方法，为他们是计算上可行的。除了广泛的个人层面的问卷数据和测量的身高和体重用于计算BMI，青少年还提供了提名的朋友名单提供有关同行网络的信息。这些数据都是在学校收集的。此外，学校环境和政策的数据收集以及青少年的住宅邻里信息。EAT项目数据源提供了丰富的新的，更完整的开发层次模型可以利用建立我们对青少年肥胖的理解。提出的分析都不是EAT项目最初分析计划的一部分，每一项都代表我们试图超越已经完成的工作。 公共卫生相关性：预防和减少青少年肥胖需要更好地了解其多重影响因素（包括社区、学校、家庭、同伴和同伴网络）之间的复杂相互作用以及青少年的个体特征和行为。该提案将开发和研究新的统计模型和方法，其中包括潜在变量、社交网络和倾向分数，这些模型和方法同时包括来自对青少年肥胖的多层次影响的多个变量，以便更深入地了解这一现象。

DESCRIPTION (provided by applicant): Adolescent obesity is now recognized as having a complex etiology. Ecological models are often used to explain this complexity, with different levels of influence at the individual, familial, peer, school, and neighborhood levels. Not only are there multiple levels of influence, but these different levels have bidirectional relationships of influence between them, further adding to the complexity. While the ecological framework is often used to conceptually explain obesity, there are few examples of statistically testing this type of multilevel model. This proposal will develop and examine new statistical models and methods incorporating latent variables, social networks and propensity scores that simultaneously incorporate multiple variables from multilevels of influence on adolescent obesity in order to inform a richer understanding of the phenomena. Using existing data from a large population-based longitudinal and serial cross-sectional survey of ethnically and socioeconomically diverse adolescents (Project EAT), statistical models and appropriate estimation methods for them which are computationally feasible will be developed. In addition to extensive individual level questionnaire data and measured heights and weights used to calculate BMI, adolescents also provide lists of nominated friends providing information about peer-networks. These data are all collected at school. Furthermore, school environmental and policy data are collected as well as the adolescent's residential neighborhood information. The Project EAT data source provides the richness that new, more completely developed hierarchical models could exploit in building our understanding about adolescent obesity. None of the analyses being proposed were part of the original analysis plan of Project EAT and each represents our attempt to go a step beyond what has been done. PUBLIC HEALTH RELEVANCE: The prevention and reduction of adolescent obesity requires a better understanding of the complex interplay among its multiple influences including neighborhoods, schools, family, peers and peer-network, and the adolescents' individual characteristics and behaviors. This proposal will develop and examine new statistical models and methods incorporating latent variables, social networks and propensity scores that simultaneously incorporate multiple variables from multilevels of influence on adolescent obesity in order to inform a richer understanding of the phenomena.