Seaman, John Weldon, 1956-Stamey, James D.Crixell, JoAnna Christine, 1979-Baylor University. Dept. of Statistical Sciences.2008-10-142017-04-072008-10-142017-04-072008-082008-10-14http://hdl.handle.net/2104/5229Includes bibliographical references (p. 69-71)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.ix, 71 p. : ill.158072 bytes836432 bytesapplication/pdfapplication/pdfen-USBaylor 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.Bayesian statistical decision theory.Error analysis (Mathematics)Logistic regression analysis.Clinical trials -- Statistical methods.ThesisBaylor University access only