Multilevel latent class and social network models for observational adolescent ob

观察性青少年观察的多级潜在类别和社交网络模型

• 批准号: 8105554
• 负责人: Melanie M Wall
• 金额: $ 30.62万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-10 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8105554
• 关键词: Adolescent; Social Network; network models

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.

描述（由申请人提供）：青少年肥胖现在被认为具有复杂的病因。生态模型通常用于解释这种复杂性，在个人，家族，同伴，学校和邻里水平上具有不同水平的影响。不仅存在多种影响，而且这些不同的层面之间具有双向影响之间的影响，进一步增加了复杂性。虽然生态框架通常用于从概念上解释肥胖症，但很少有统计学测试这种类型的多级模型的例子。该提案将开发和研究结合潜在变量，社交网络和倾向分数的新的统计模型和方法，这些变量同时结合了来自对青少年肥胖的多层次的多个变量，以便对现象有更丰富的了解。使用来自种族和社会经济多样的青少年（项目EAT），统计模型和适当的计算可行性估计方法的大型基于人群的纵向和串行横断面调查的现有数据。除了广泛的个人级别问卷数据以及用于计算BMI的测量高度和权重外，青少年还提供了提名朋友的列表，提供有关PEER-NETWORKS的信息。这些数据全部在学校收集。此外，还收集了学校的环境和政策数据以及青少年的住宅邻里信息。项目饮食数据源提供了一种丰富的，即新的，更完全开发的层次模型可以利用我们对青少年肥胖的理解。提出的分析都不是项目EAT的原始分析计划的一部分，每种计划都代表了我们试图超越已完成的尝试。

项目成果

Mixture Factor Analysis for Approximating a Nonnormally Distributed Continuous Latent Factor With Continuous and Dichotomous Observed Variables.

用于近似具有连续和二分观测变量的非正态分布连续潜在因子的混合因子分析。

• DOI: 10.1080/00273171.2012.658339
• 发表时间: 2012
• 期刊: Multivariate behavioral research
• 影响因子: 3.8
• 作者: Wall,MelanieM;Guo,Jia;Amemiya,Yasuo
• 通讯作者: Amemiya,Yasuo

Is Probabilistic Bias Analysis Approximately Bayesian?

• DOI: 10.1097/ede.0b013e31823b539c
• 发表时间: 2012-01-01
• 期刊: EPIDEMIOLOGY
• 影响因子: 5.4
• 作者: MacLehose, Richard F.;Gustafson, Paul
• 通讯作者: Gustafson, Paul

Sensitivity analyses for sparse-data problems-using weakly informative bayesian priors.

• DOI: 10.1097/ede.0b013e318280db1d
• 发表时间: 2013-03
• 期刊: Epidemiology (Cambridge, Mass.)
• 作者: Hamra GB;MacLehose RF;Cole SR
• 通讯作者: Cole SR

Which of these things is not like the others?

• DOI: 10.1002/cncr.28359
• 发表时间: 2013-12-15
• 期刊: Cancer
• 影响因子: 6.2
• 作者: Kaufman JS;MacLehose RF
• 通讯作者: MacLehose RF