@article{ref1,
title="Comparison of subject-specific and population averaged models for count data from cluster-unit intervention trials",
journal="Statistical methods in medical research",
year="2007",
author="Wolfson, Mark and Qaqish, Bahjat F. and Preisser, John S. and Young, Mary L.",
volume="16",
number="2",
pages="167-184",
abstract="Maximum likelihood estimation techniques for subject-specific (SS) generalized linear mixed models and generalized estimating equations for marginal or population-averaged (PA) models are often used for the analysis of cluster-unit intervention trials. Although both classes of procedures account for the presence of within-cluster correlations, the interpretations of fixed effects including intervention effect parameters differ in SS and PA models. Furthermore, closed-form mathematical expressions relating SS and PA parameters from the two respective approaches are generally lacking. This paper investigates the special case of correlated Poisson responses where, for a log-linear model with normal random effects, exact relationships are available. Equivalent PA model representations of two SS models commonly used in the analysis of nested cross-sectional cluster trials with count data are derived. The mathematical results are illustrated with count data from a large non-randomized cluster trial to reduce underage drinking. Knowledge of relationships among parameters in the respective mean and covariance models is essential to understanding empirical comparisons of the two approaches.<p /> <p>Language: en</p>",
language="en",
issn="0962-2802",
doi="",
url="http://dx.doi.org/"
}