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Logistic regression with covariate measurement error in an adaptive design : a Bayesian approach.

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dc.contributor.advisor Seaman, John Weldon, 1956-
dc.contributor.advisor Stamey, James D.
dc.contributor.author Crixell, JoAnna Christine, 1979-
dc.contributor.other Baylor University. Dept. of Statistical Sciences. en
dc.date.copyright 2008-08
dc.identifier.uri http://hdl.handle.net/2104/5229
dc.description.abstract Adaptive designs are increasingly popular in clinical trials. This is because such designs have the potential to decrease patient exposure to treatments that are less efficacious or unsafe. The Bayesian approach to adaptive designs is attractive because it makes systematic use of prior data and other information in a way that is consistent with the laws of probability. The goal of this dissertation is to examine the effects of measurement error on a Bayesian adaptive design. Measurement error problems are common in a variety of regression applications where the variable of interest cannot be measured perfectly. This is often unavoidable because infallible measurement tools to account for such error are either too expensive or unavailable. When modeling the relationship between a response variable and other covariates, we must account for any uncertainty introduced when one or both of these variables are measured with error. This dissertation will explore the consequence of imperfect measurements on a Bayesian adaptive design. en
dc.rights Baylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact librarywebmaster@baylor.edu for inquiries about permission. en
dc.subject Bayesian statistical decision theory. en
dc.subject Error analysis (Mathematics) en
dc.subject Logistic regression analysis. en
dc.subject Clinical trials -- Statistical methods. en
dc.title Logistic regression with covariate measurement error in an adaptive design : a Bayesian approach. en
dc.type Thesis en
dc.description.degree Ph.D. en
dc.rights.accessrights Baylor University access only en
dc.contributor.department Statistical Sciences. en


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