Type of Document Master's Thesis Author Ujamaa, Dawud A. Author's Email Address DUjamaa@yahoo.com URN etd-07242006-090226 Title Assessing the Effect of Prior Distribution Assumption on the Variance Parameters in Evaluating Bioequivalence Trials Degree Master of Science Department Mathematics and Statistics Advisory Committee
Advisor Name Title Dr. Pulak Ghosh Committee Chair Dr. Xu Zhang Committee Member Dr. Yichuan Zhao Committee Member Dr. Yu-Sheng Hsu Committee Member Keywords
- Average Bioequivalence
- Bayesian methods
- Carry-Over Effect
- Crossover design
- DIC
- Individual Bioequivalence
- Inter-subject variance
- Intra-subject variance
- Markov Chain Monte Carlo
- Population Bioequivalence
- Prior Distributions
- WinBUGS
Date of Defense 2006-07-03 Availability unrestricted Abstract Bioequivalence determines if two drugs are alike. The three kinds of bioequivalence are Average, Population, and Individual Bioequivalence. These Bioequivalence criteria can be evaluated using aggregate and disaggregate methods. Considerable work assessing bioequivalence in a frequentist method exists, but the advantages of Bayesian methods for Bioequivalence have been recently explored. Variance parameters are essential to any of theses existing Bayesian Bioequivalence metrics. Usually, the prior distributions for model parameters use either informative priors or vague priors. The Bioequivalence inference may be sensitive to the prior distribution on the variances. Recently, there have been questions about the routine use of inverse gamma priors for variance parameters. In this paper we examine the effect that changing the prior distribution of the variance parameters has on Bayesian models for assessing Bioequivalence and the carry-over effect. We explore our method with some real data sets from the FDA.Files
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