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

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    Dissertation (816.8Kb)
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    Date
    2008-10-14
    Author
    Crixell, JoAnna Christine, 1979-
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    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.
    URI
    http://hdl.handle.net/2104/5229
    Department
    Statistical Sciences.
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    • Theses/Dissertations - Statistical Sciences

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    Copyright © Baylor® University All rights reserved. Legal Disclosures.
    Baylor University Waco, Texas 76798 1-800-BAYLOR-U
    Baylor University Libraries | One Bear Place #97148 | Waco, TX 76798-7148 | 254.710.2112 | Contact: libraryquestions@baylor.edu
    If you find any errors in content, please contact librarywebmaster@baylor.edu
    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
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