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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/23761

Title: Models for zero-inflated, correlated count data with extra heterogeneity: when is it too complex?
Authors: Chebon, Sammy
Faes, Christel
Cools, Frank
Geys, Helena
Issue Date: 2017
Citation: STATISTICS IN MEDICINE, 36(2), p. 345-361
Abstract: Statistical analysis of count data typically starts with a Poisson regression. However, in many real-life applications, it is observed that the variation in the counts is larger than the mean, and one needs to deal with the problem of overdispersion in the counts. Several factors may contribute to overdispersion: (1) unobserved heterogeneity due to missing covariates, (2) correlation between observations (such as in longitudinal studies), and (3) the occurrence of many zeros (more than expected from the Poisson distribution). In this paper, we discuss a model that allows one to explicitly take each of these factors into consideration. The aim of this paper is twofold: (1) investigate whether we can identify the cause of overdispersion via model selection, and (2) investigate the impact of a misspecification of the model on the power of a covariate. The paper is motivated by a study of the occurrence of drug-induced arrhythmia in beagle dogs based on electrocardiogram recordings, with the objective to evaluate the effect of potential drugs on the heartbeat irregularities. Copyright (C) 2016 John Wiley & Sons, Ltd.
Notes: [Chebon, Sammy; Faes, Christel; Geys, Helena] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium. [Cools, Frank; Geys, Helena] Janssen Pharmaceut NV, Turnhoutseweg 30, B-2340 Beerse, Belgium.
URI: http://hdl.handle.net/1942/23761
DOI: 10.1002/sim.7142
ISI #: 000392825500013
ISSN: 0277-6715
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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