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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/11833

Title: Marginal correlation from an extended random-effects model for repeated and overdispersed counts
Authors: Vangeneugden, Tony
Molenberghs, Geert
Verbeke, Geert
Demetrio, Clarice G. B.
Issue Date: 2011
Citation: JOURNAL OF APPLIED STATISTICS, 38 (2). p. 215-232
Abstract: Vangeneugden et al. [15] derived approximate correlation functions for longitudinal sequences of general data type, Gaussian and non-Gaussian, based on generalized linear mixed-effects models (GLMM). Their focus was on binary sequences, as well as on a combination of binary and Gaussian sequences. Here, we focus on the specific case of repeated count data, important in two respects. First, we employ the model proposed by Molenberghs et al. [13], which generalizes at the same time the Poisson-normal GLMM and the conventional overdispersion models, in particular the negative-binomial model. The model flexibly accommodates data hierarchies, intra-sequence correlation, and overdispersion. Second, means, variances, and joint probabilities can be expressed in closed form, allowing for exact intra-sequence correlation expressions. Next to the general situation, some important special cases such as exchangeable clustered outcomes are considered, producing insightful expressions. The closed-form expressions are contrasted with the generic approximate expressions of Vangeneugden et al. [15]. Data from an epileptic-seizures trial are analyzed and correlation functions derived. It is shown that the proposed extension strongly outperforms the classical GLMM.
Notes: [Vangeneugden, Tony; Molenberghs, Geert; Verbeke, Geert] Hasselt Univ, Ctr Stat, Diepenbeek, Belgium. [Vangeneugden, Tony] Johnson & Johnson, Tibotec, Mechelen, Belgium. [Molenberghs, Geert; Verbeke, Geert] Katholieke Univ Leuven, Ctr Biostat, Louvain, Belgium. [Demetrio, Clarice G. B.] ESALQ, Sao Paulo, Brazil. geert.molenberghs@uhasselt.be
URI: http://hdl.handle.net/1942/11833
DOI: 10.1080/02664760903406405
ISI #: 000286976100001
ISSN: 0266-4763
Category: A1
Type: Journal Contribution
Validation: ecoom, 2012
Appears in Collections: Research publications

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