title: Hierarchical multiple informants models: examining food environment contributions to the childhood obesity epidemic.
abstract: Methods for multiple informants help to estimate the marginal effect of each multiple source predictor and formally compare the strength of their association with an outcome. We extend multiple informant methods to the case of hierarchical data structures to account for within cluster correlation. We apply the proposed method to examine the relationship between features of the food environment near schools and children's body mass index z-scores (BMIz). Specifically, we compare the associations between two different features of the food environment (fast food restaurants and convenience stores) with BMIz and investigate how the association between the number of fast food restaurants or convenience stores and child's BMIz varies across distance from a school. The newly developed methodology enhances the types of research questions that can be asked by investigators studying effects of environment on childhood obesity and can be applied to other fields.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.A.
authors.forename: Jonggyu
authors.initials: J
authors.lastname: Baek
authors.forename: Brisa N
authors.initials: BN
authors.lastname: Sánchez
authors.forename: Emma V
authors.initials: EV
authors.lastname: Sanchez-Vaznaugh
mesh_terms: D000293:Adolescent; D015992:Body Mass Index; D002140:California / Q000453:epidemiology; D002648:Child; D016000:Cluster Analysis; D003198:Computer Simulation; D004777:Environment; D058872:Epidemics; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D063766:Pediatric Obesity / Q000453:epidemiology*; D012147:Restaurants
keywords: generalized estimating equations; hierarchical data structure; multiple informants
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