Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

To GEE or Not to GEE Comparing Population Average and Mixed Models for Estimating the Associations Between Neighborhood Risk Factors and Health

To GEE or Not to GEE Comparing Population Average and Mixed Models for Estimating the... ORIGINAL ARTICLE To GEE or Not to GEE Comparing Population Average and Mixed Models for Estimating the Associations Between Neighborhood Risk Factors and Health a b b a b Alan E. Hubbard, Jennifer Ahern, Nancy L. Fleischer, Mark Van der Laan, Sheri A. Lippman, a c b,d Nicholas Jewell, Tim Bruckner, and William A. Satariano level outcomes, while adjusting for individual- and neighbor- Abstract: Two modeling approaches are commonly used to esti- hood-level confounders. One objective of these studies has mate the associations between neighborhood characteristics and been to determine the associations of community character- individual-level health outcomes in multilevel studies (subjects istics (eg, crime statistics and environmental exposures) with within neighborhoods). Random effects models (or mixed models) health outcomes after adjusting for individual characteristics use maximum likelihood estimation. Population average models typically use a generalized estimating equation (GEE) approach. of residents. These methods are used in place of basic regression approaches Two modeling approaches are commonly used to esti- because the health of residents in the same neighborhood may be mate the associations between neighborhood characteristics correlated, thus violating independence assumptions made by tradi- and health outcomes in multilevel studies. One is the random tional regression procedures. This http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Epidemiology Wolters Kluwer Health

To GEE or Not to GEE Comparing Population Average and Mixed Models for Estimating the Associations Between Neighborhood Risk Factors and Health

Epidemiology , Volume 21 (4) – Jul 1, 2010

Loading next page...
 
/lp/wolters-kluwer-health/to-gee-or-not-to-gee-comparing-population-average-and-mixed-models-for-6I53asz1Jn

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

ISSN
1044-3983
eISSN
1531-5487
DOI
10.1097/EDE.0b013e3181caeb90
pmid
20220526
Publisher site
See Article on Publisher Site

Abstract

ORIGINAL ARTICLE To GEE or Not to GEE Comparing Population Average and Mixed Models for Estimating the Associations Between Neighborhood Risk Factors and Health a b b a b Alan E. Hubbard, Jennifer Ahern, Nancy L. Fleischer, Mark Van der Laan, Sheri A. Lippman, a c b,d Nicholas Jewell, Tim Bruckner, and William A. Satariano level outcomes, while adjusting for individual- and neighbor- Abstract: Two modeling approaches are commonly used to esti- hood-level confounders. One objective of these studies has mate the associations between neighborhood characteristics and been to determine the associations of community character- individual-level health outcomes in multilevel studies (subjects istics (eg, crime statistics and environmental exposures) with within neighborhoods). Random effects models (or mixed models) health outcomes after adjusting for individual characteristics use maximum likelihood estimation. Population average models typically use a generalized estimating equation (GEE) approach. of residents. These methods are used in place of basic regression approaches Two modeling approaches are commonly used to esti- because the health of residents in the same neighborhood may be mate the associations between neighborhood characteristics correlated, thus violating independence assumptions made by tradi- and health outcomes in multilevel studies. One is the random tional regression procedures. This

Journal

EpidemiologyWolters Kluwer Health

Published: Jul 1, 2010

There are no references for this article.