A Model Selection Paradigm for Modeling Recurrent Adenoma Data in Polyp Prevention Trials

Persistent Link:
http://hdl.handle.net/10150/228465
Title:
A Model Selection Paradigm for Modeling Recurrent Adenoma Data in Polyp Prevention Trials
Author:
Davidson, Christopher L.
Issue Date:
2012
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Colorectal polyp prevention trials (PPTs) are randomized, placebo-controlled clinical trials that evaluate some chemo-preventive agent and include participants who will be followed for at least 3 years to compare the recurrence rates (counts) of adenomas. A large proportion of zero counts will likely be observed in both groups at the end of the observation period. Poisson general linear models (GLMs) are usually employed for estimation of recurrence in PPTs. Other models, including the negative binomial (NB2), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) may be better suited to handle zero-inflation or other forms of overdispersion that are common in count data. A model selection paradigm that determines a statistical approach for choosing the best fitting model for recurrence data is described. An example using a subset from a large Phase III clinical trial indicated that the ZINB model was the best fitting model for the data.
Type:
text; Electronic Thesis
Keywords:
Poisson; Polyp-prevention; Zero-inflation; ZINB; Public Health; Adenomas; Overdispersion
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College; Public Health
Degree Grantor:
University of Arizona
Advisor:
Hsu, Chiu-Hsieh (Paul)

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleA Model Selection Paradigm for Modeling Recurrent Adenoma Data in Polyp Prevention Trialsen_US
dc.creatorDavidson, Christopher L.en_US
dc.contributor.authorDavidson, Christopher L.en_US
dc.date.issued2012-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractColorectal polyp prevention trials (PPTs) are randomized, placebo-controlled clinical trials that evaluate some chemo-preventive agent and include participants who will be followed for at least 3 years to compare the recurrence rates (counts) of adenomas. A large proportion of zero counts will likely be observed in both groups at the end of the observation period. Poisson general linear models (GLMs) are usually employed for estimation of recurrence in PPTs. Other models, including the negative binomial (NB2), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) may be better suited to handle zero-inflation or other forms of overdispersion that are common in count data. A model selection paradigm that determines a statistical approach for choosing the best fitting model for recurrence data is described. An example using a subset from a large Phase III clinical trial indicated that the ZINB model was the best fitting model for the data.en_US
dc.typetexten_US
dc.typeElectronic Thesisen_US
dc.subjectPoissonen_US
dc.subjectPolyp-preventionen_US
dc.subjectZero-inflationen_US
dc.subjectZINBen_US
dc.subjectPublic Healthen_US
dc.subjectAdenomasen_US
dc.subjectOverdispersionen_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplinePublic Healthen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorHsu, Chiu-Hsieh (Paul)en_US
dc.contributor.committeememberHsu, Chiu-Hsieh (Paul)en_US
dc.contributor.committeememberRoe, Denise J.en_US
dc.contributor.committeememberJacobs, Elizabeth T.en_US
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