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.
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.
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