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American Journal of Epidemiology Advance Access originally published online on November 22, 2006
American Journal of Epidemiology 2007 165(4):453-463; doi:10.1093/aje/kwk020
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American Journal of Epidemiology Copyright © 2006 by the Johns Hopkins Bloomberg School of Public Health All rights reserved; printed in U.S.A.

ORIGINAL CONTRIBUTIONS

Marginal Modeling of Nonnested Multilevel Data using Standard Software

Diana L. Miglioretti1 and Patrick J. Heagerty2

1 Group Health Center for Health Studies, Seattle, WA
2 Department of Biostatistics, University of Washington, Seattle, WA

Correspondence to Dr. Diana Miglioretti, Group Health Center for Health Studies, 1730 Minor Avenue, Suite 1600, Seattle, WA 98101 (e-mail: miglioretti.d{at}ghc.org).

Received for publication November 1, 2005. Accepted for publication July 11, 2006.

Epidemiologic data are often clustered within multiple levels that may not be nested within each other. Generalized estimating equations are commonly used to adjust for correlation among observations within clusters when fitting regression models; however, standard software does not currently accommodate nonnested clusters. This paper introduces a simple generalized estimating equation strategy that uses available commercial or public software for the regression analysis of nonnested multilevel data. The authors describe how to obtain empirical standard error estimates for constructing valid confidence intervals and conducting statistical hypothesis tests. The method is evaluated using simulations and illustrated with an analysis of data from the Breast Cancer Surveillance Consortium that estimates the influence of woman, radiologist, and facility characteristics on the positive predictive value of screening mammography. Performance with a small number of clusters is discussed. Both the simulations and the example demonstrate the importance of accounting for the correlation within all levels of clustering for proper inference.

clustered data; generalized estimating equation; generalized linear model

Abbreviations: C1ID, observations belonging to cluster 1 (C2ID defined analogously); GEE, generalized estimating equation; ID, cluster-identifying variable; n-ID, ID for neighborhood; p-ID, ID for provider; PPV, positive predictive value


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