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http://hdl.handle.net/1942/23761
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DC Field | Value | Language |
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dc.contributor.author | CHEBON, Sammy | - |
dc.contributor.author | FAES, Christel | - |
dc.contributor.author | Cools, Frank | - |
dc.contributor.author | GEYS, Helena | - |
dc.date.accessioned | 2017-05-18T13:32:00Z | - |
dc.date.available | 2017-05-18T13:32:00Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | STATISTICS IN MEDICINE, 36(2), p. 345-361 | - |
dc.identifier.issn | 0277-6715 | - |
dc.identifier.uri | http://hdl.handle.net/1942/23761 | - |
dc.description.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. | - |
dc.description.sponsorship | This work was supported by the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office [IAP]. | - |
dc.language.iso | en | - |
dc.publisher | WILEY-BLACKWELL | - |
dc.rights | (C) 2016 John Wiley & Sons, Ltd. | - |
dc.subject.other | ECG arrhythmia data; random effect model; negative binomial model; combined models; zero-inflated model; overdispersion | - |
dc.subject.other | ECG arrhythmia data; random effect model; negative binomial model; combined models; zero-inflated model; overdispersion | - |
dc.title | Models for zero-inflated, correlated count data with extra heterogeneity: when is it too complex? | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 361 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 345 | - |
dc.identifier.volume | 36 | - |
local.format.pages | 17 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.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. | - |
local.publisher.place | HOBOKEN | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1002/sim.7142 | - |
dc.identifier.isi | 000392825500013 | - |
item.fulltext | With Fulltext | - |
item.fullcitation | CHEBON, Sammy; FAES, Christel; Cools, Frank & GEYS, Helena (2017) Models for zero-inflated, correlated count data with extra heterogeneity: when is it too complex?. In: STATISTICS IN MEDICINE, 36(2), p. 345-361. | - |
item.contributor | CHEBON, Sammy | - |
item.contributor | FAES, Christel | - |
item.contributor | Cools, Frank | - |
item.contributor | GEYS, Helena | - |
item.accessRights | Restricted Access | - |
item.validation | ecoom 2018 | - |
crisitem.journal.issn | 0277-6715 | - |
crisitem.journal.eissn | 1097-0258 | - |
Appears in Collections: | Research publications |
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chebon 1.pdf Restricted Access | Published version | 1.97 MB | Adobe PDF | View/Open Request a copy |
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