Comparison of subject-specific and population averaged models for count data from cluster-unit intervention trials.
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.
Young
Mary L
ML
Department of Biostatistics, University of North Carolina School of Public Health, Chapel Hill, NC, USA.
Preisser
John S
JS
Qaqish
Bahjat F
BF
Wolfson
Mark
M
D000293:Adolescent; D000328:Adult; D000428:Alcohol Drinking / Q000517:prevention & control*; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D007407:Interviews as Topic; D016013:Likelihood Functions; D008297:Male; D014481:United States