Bayesian topics in biostatistics : treatment selection, sample size, power, and misclassification.

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dc.contributor.advisor Tubbs, Jack Dale.
dc.contributor.advisor Stamey, James D. Doty, Tave Parker. 2011-12
dc.description.abstract Bayesian methodology is implemented to investigate three problems in biostatistics. The first problem considers using biomarkers to select optimal treatments for individual patients. A Bayesian adaptation of the selection impact (SI) curve developed by Pepe and Song (2004) is investigated. The second problem considers a Bayesian approach for determining specific sample sizes to achieve a desired range of power for fixed-dose combination drug trials. Sidik and Jonkman (2003) developed a sample size formula using the intersection-union test for testing the efficacy of combination drugs. Our results are compared to their frequentist approach. The third problem considers response misclassification in fixed-dose combination drug trials under two scenarios: when the sensitivity and specificity are known, and when the sensitivity and specificity are unknown but have specified informative prior structures. en_US
dc.publisher 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 for inquiries about permission. en_US
dc.subject Bayesian statistics. en_US
dc.subject Biomarkers. en_US
dc.subject Combination drugs. en_US
dc.subject Treatment selection. en_US
dc.subject Sample size. en_US
dc.subject Power. en_US
dc.subject Misclassification. en_US
dc.title Bayesian topics in biostatistics : treatment selection, sample size, power, and misclassification. en_US
dc.type Thesis en_US Ph.D. en_US
dc.rights.accessrights Worldwide access. en_US
dc.rights.accessrights Access changed 5/21/14.
dc.contributor.department Statistical Sciences. en_US
dc.contributor.schools Baylor University. Dept. of Statistical Sciences. en_US

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