Journal article
Hierarchical multiple informants models: examining food environment contributions to the childhood obesity epidemic
Statistics in medicine, v 33(4), pp 662-674
20 Feb 2014
PMID: 24038440
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Hierarchical multiple informants models: examining food environment contributions to the childhood obesity epidemic
- Creators
- Jonggyu Baek - Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.ABrisa N Sánchez - Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.AEmma V Sanchez-Vaznaugh - Department of Health Education, San Francisco State University, San Francisco, CA, U.S.A
- Publication Details
- Statistics in medicine, v 33(4), pp 662-674
- Publisher
- Wiley
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000330802300010
- Scopus ID
- 2-s2.0-84892482096
- Other Identifier
- 991014877778104721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Mathematical & Computational Biology
- Medical Informatics
- Medicine, Research & Experimental
- Public, Environmental & Occupational Health
- Statistics & Probability