Linked Life Data

17885875

Source:http://linkedlifedata.com/resource/pubmed/id/17885875

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2007-9-21
pubmed:abstractText
In this note, we comment on the zero-inflated and hurdle models for count data presented by Rose et al., 2006, J. Biopharma. Stat. 16:463-481. By viewing these models as finite mixture models, one gains a better understanding of the components of the models, including assumptions about the latent variable(s) in the finite mixture models. Deciding whether a zero-inflated or hurdle model is appropriate for a given data set requires close collaboration with subject matter experts. For instance, in modeling vaccine adverse event count data, the pharmacokinetic rationale for the occurrence of an adverse event and the likelihood of detecting or reporting the adverse event are important considerations for mixture model development.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1054-3406
pubmed:author
pubmed:issnType
Print
pubmed:volume
17
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
943-6
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Mixture model framework facilitates understanding of zero-inflated and hurdle models for count data.
pubmed:affiliation
National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia 30329, USA. ALB1@cdc.gov
pubmed:publicationType
Journal Article