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Comparison of subject-specific and population averaged models for count data from cluster-unit intervention trials.
- Source :
-
Statistical Methods in Medical Research . Apr2007, Vol. 16 Issue 2, p167-184. 18p. - Publication Year :
- 2007
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *ESTIMATION theory
*LINEAR statistical models
*MATHEMATICAL models
*GENERALIZED estimating equations
*CLUSTER analysis (Statistics)
*STATISTICAL correlation
*POISSON processes
*POISSON algebras
*COMPARATIVE studies
*INTERVIEWING
*RESEARCH methodology
*MEDICAL cooperation
*PROBABILITY theory
*RESEARCH
*EVALUATION research
ALCOHOL drinking prevention
Subjects
Details
- Language :
- English
- ISSN :
- 09622802
- Volume :
- 16
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Statistical Methods in Medical Research
- Publication Type :
- Academic Journal
- Accession number :
- 25219071
- Full Text :
- https://doi.org/10.1177/0962280206071931