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Semiparametric AUC regression for testing treatment effect in clinical trial.

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dc.contributor.advisor Tubbs, Jack Dale.
dc.contributor.author Zhang, Lin, 1978-
dc.contributor.other Baylor University. Dept. of Statistical Sciences. en
dc.date.copyright 2008-08
dc.identifier.uri http://hdl.handle.net/2104/5237
dc.description.abstract We investigated distribution free methods for testing covariate adjusted treatment effects. Dodd and Pepe (2003) proposed a semiparametric logistic regression model for the area under the ROC curve (AUC). Their model was motivated by the observation that the commonly used non-parametric Mann-Whitney statistic is a non-parametric estimate of the AUC, where the AUC gives a measure of the separation between two density functions. Their result allows one to test hypotheses using distribution free methods when the covariates are discrete, however, the standard errors generated using standard GLM software were not correct since the Bernoulli data used in the Mann-Whitney statistic are correlated. They used bootstrapping to compute the standard errors. In Chapter 2, we present an analytical method for estimating the standard errors as an alternative to the bootstrap procedure. In Chapter 3, we present a new semiparametric beta regression model for the AUC. This was done by defining the response variable as the placement value of the treatment responses with respect to a placebo population. This model allows for both discrete and continuous covariate effects. In Chapter 4, we expand our model in two ways. The first is for a clinical trial with multiple treatments arms and a placebo. The second extension is for longitudinal or repeated measures data. These extensions are illustrated using both simulated and real data. 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 Nonparametric statistics. en
dc.subject Medical statistics. en
dc.subject Regression analysis -- Testing. en
dc.subject Clinical trials -- Statistical methods. en
dc.subject Medicine -- Research -- Statistical methods. en
dc.title Semiparametric AUC regression for testing treatment effect in clinical trial. 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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