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Editorial

. 2010 Jul;21(4):475-8; discussion 479-81.

doi: 10.1097/EDE.0b013e3181d74a71.

Modeling neighborhood effects: the futility of comparing mixed and marginal approaches

S V Subramanian, A James O'Malley

PMID: 20539108
PMCID: PMC3085136
DOI: 10.1097/EDE.0b013e3181d74a71

Editorial

Modeling neighborhood effects: the futility of comparing mixed and marginal approaches

S V Subramanian et al. Epidemiology. 2010 Jul.

. 2010 Jul;21(4):475-8; discussion 479-81.

doi: 10.1097/EDE.0b013e3181d74a71.

Authors

S V Subramanian, A James O'Malley

PMID: 20539108
PMCID: PMC3085136
DOI: 10.1097/EDE.0b013e3181d74a71

No abstract available

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Comment on

To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health.
Hubbard AE, Ahern J, Fleischer NL, Van der Laan M, Lippman SA, Jewell N, Bruckner T, Satariano WA. Hubbard AE, et al. Epidemiology. 2010 Jul;21(4):467-74. doi: 10.1097/EDE.0b013e3181caeb90. Epidemiology. 2010. PMID: 20220526

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References

1. Hubbard AE, Ahern J, Fleischer NL, et al. To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health. Epidemiology. 2010;21:467–474. - PubMed
1. Goldstein H. Multilevel Statistical Models. London: Arnold; 2003.
1. Raudenbush SW, Bryk AS. Advanced Quantitative Techniques in the Social Sciences 1. 2nd ed. Thousand Oaks, CA: Sage Publications; 2002. Hierarchical linear models: Applications and data analysis methods.
1. Zeger SL, Liang KY, Albert PS. Models for longitudinal data: a generalized estimating equation approach. Biometrics. 1988;44:1049–1060. - PubMed
1. Raudenbush S. Marginalized multilevel models and likelihood inference: Comment. Stat Sci. 2000;15:22–24.

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