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Biometrika 2002 89(2):389-399; doi:10.1093/biomet/89.2.389
© 2002 by Biometrika Trust
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Analysing longitudinal count data with overdispersion

Vandna Jowaheer1 and Brajendra C.Sutradhar2

1 Department of Mathematics, University of Mauritius, Reduit, Mauritius vandnaj@uom.ac.mu 2 Department of Mathematics and Statistics, Memorial University of Newfoundland, St John's, NF, Canada A1C 5S7 bsutradh@math.mun.ca

In many biomedical studies, longitudinal count data comprise repeated responses and a set of multidimensional covariates for a large number of individuals.When the response variable in such models is subject to overdispersion, the overdispersion parameter influences the marginal variance. In such cases, the overdispersion parameter plays a significant role in efficient estimation of the regression parameters. This raises the need for joint estimation of the regression parameters and the overdispersion parameter, the longitudinal correlations being nuisance parameters. In this paper, we develop a generalised estimating equations approach based on a general autocorrelation structure for the repeated overdispersed data. The asymptotic properties of the estimators of the main parameters are discussed, and the estimation methodology is illustrated by analysing data on epileptic seizure counts.

Key Words: Consistency; Efficiency; Latent-process-driven longitudinal correlation; Observations-driven longitudinal autocorrelation; Overdispersion; Regression effect

Received May 2000. Revised October 2001


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