View Item 

Show simple item record

Hierarchical multiple informants models: examining food environment contributions to the childhood obesity epidemic

dc.contributor.authorBaek, Jonggyuen_US
dc.contributor.authorSánchez, Brisa N.en_US
dc.contributor.authorSanchez‐vaznaugh, Emma V.en_US
dc.date.accessioned2014-02-11T17:56:50Z
dc.date.available2015-04-01T19:59:06Zen_US
dc.date.issued2014-02-20en_US
dc.identifier.citationBaek, Jonggyu; Sánchez, Brisa N. ; Sanchez‐vaznaugh, Emma V. (2014). "Hierarchical multiple informants models: examining food environment contributions to the childhood obesity epidemic ." Statistics in Medicine 33(4): 662-674.en_US
dc.identifier.issn0277-6715en_US
dc.identifier.issn1097-0258en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/102625
dc.publisherWiley Periodicals, Inc.en_US
dc.publisherAddison‐Wesleyen_US
dc.subject.otherHierarchical Data Structureen_US
dc.subject.otherMultiple Informantsen_US
dc.subject.otherGeneralized Estimating Equationsen_US
dc.titleHierarchical multiple informants models: examining food environment contributions to the childhood obesity epidemicen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelPublic Healthen_US
dc.subject.hlbsecondlevelMedicine (General)en_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/102625/1/sim5967.pdf
dc.identifier.doi10.1002/sim.5967en_US
dc.identifier.sourceStatistics in Medicineen_US
dc.identifier.citedreferenceBeaton AE. The use of special matrix operators in statistical calculus. Research Bulletin RB–64–51, Princeton, NJ, 1964.en_US
dc.identifier.citedreferencePepe MS, Whitaker RC, Seidel K. Estimating and comparing univariate associations with application to the prediction of adult obesity. Statistics in Medicine 1999; 18 ( 2 ): 163 – 173.en_US
dc.identifier.citedreferenceHorton NJ, Fitzmaurice GM. Regression analysis of multiple source and multiple informant data from complex survey samples. Statistics in Medicine 2004; 23 ( 18 ): 2911 – 2933.en_US
dc.identifier.citedreferenceLitman HJ, Horton NJ, Hernández B, Laird NM. Estimation of marginal regression models with multiple source predictors. Handbook of Statistics 2008; 27 ( 7 ): 730 – 746.en_US
dc.identifier.citedreferenceLiang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73 ( 1 ): 13 – 22.en_US
dc.identifier.citedreferencePan W, Louis TA, Connett JE, Connett E. A note on marginal linear regression with correlated response data. The American Statistician 2000; 54 ( 3 ): 191 – 195.en_US
dc.identifier.citedreferenceSullivan PM, Anderson GL. A cautionary note on inference for marginal regression models with longitudinal data and general correlated response data. Communications in Statistics 1994; 23 ( 4 ): 939 – 951.en_US
dc.identifier.citedreferenceMancl LA, Leroux BG. Efficiency of regression estimates for clustered data. Biometrics 1996; 52 ( 2 ): 500 – 511.en_US
dc.identifier.citedreferenceRochon J. Analyzing bivariate repeated measures for discrete and continuous outcome variables. Biometrics 1996; 52 ( 2 ): 740 – 750.en_US
dc.identifier.citedreferenceZellner A. An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association 1962; 57 ( 298 ): 348 – 368.en_US
dc.identifier.citedreferenceMust A, Anderson SE. Body mass index in children and adolescents: considerations for population‐based applications. International Journal of Obesity 2006; 30 ( 4 ): 590 – 594.en_US
dc.identifier.citedreferenceCenters for Disease Control and Prevention. CDC growth charts, United States. Atlanta (GA): CDC 2005; Retrieved January 28, 2010, from http://www.cdc.gov/GROWTHcharts/.en_US
dc.identifier.citedreferenceSánchez BN, Sanchez‐Vaznaugh EV, Uscilka A, Baek J, Zhang L. Differential associations between the food environment near schools and childhood overweight across race/ethnicity, gender, and grade. American Journal of Epidemiology 2012; 175 ( 12 ): 1284 – 1293.en_US
dc.identifier.citedreferenceMancl LA, Derouen TA. A covariance estimator for GEE with improved small‐sample properties. Biometrics 2001; 57: 126 – 134.en_US
dc.identifier.citedreferenceDempster AP. Elements of Continuous Multivariate Analysis, Addison‐Wesley: Reading, MA, 1969.en_US
dc.identifier.citedreferenceDavis B, Carpenter C. Proximity of fast‐food restaurants to schools and adolescent obesity. American Journal of Public Health 2009; 99 ( 3 ): 505 – 510.en_US
dc.identifier.citedreferenceHorton NJ, Laird NM, Zahner GEP. Use of multiple informant data as a predictor in psychiatric epidemiology. International Journal of Methods in Psychiatric Research 1999; 8 ( 1 ): 6 – 18.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
sim5967.pdf
Size:
411.7KB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.