Logistic regression with covariate measurement error in an adaptive design : a Bayesian approach.
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Baylor University access onlyDate
2008-10-14Author
Crixell, JoAnna Christine, 1979-
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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.