Journal
STATISTICS IN MEDICINE
Volume 24, Issue 16, Pages 2557-2575Publisher
WILEY
DOI: 10.1002/sim.2121
Keywords
generalized estimating equations; marginal model; multivariate negative binomial model; overdispersed correlated count data; subject specific covariates
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A robust likelihood approach for the analysis of overdispersed correlated count data that takes into account cluster varying covariates is proposed. We emphasise two characteristics of the proposed method: That the correlation structure satisfies the constraints on the second moments and that the estimation of the correlation structure guarantees consistent estimates of the regression coefficients. In addition we extend the mean specification to include within- and between-cluster effects. The method is illustrated through the analysis of data from two studies. In the first study, cross-sectional count data from a randomised controlled trial are analysed to evaluate the efficacy of a communication skills training programme. The second study involves longitudinal count data which represent counts of damaged hand joints in patients with psoriatic arthritis. Motivated by this study, we generalize our model to accommodate for a subpopulation of patients who are not susceptible to the development of damaged hand joints. Copyright (C) 2005 John Wiley & Sons, Ltd.
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