Date of Award
8-2-2006
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Mathematics and Statistics
First Advisor
Dr. Pulak Ghosh - Chair
Second Advisor
Dr. Yu-Sheng Hsu
Third Advisor
Dr. Xu Zhang
Fourth Advisor
Dr. Yichuan Zhao
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.
DOI
https://doi.org/10.57709/1059669
Recommended Citation
Ujamaa, Dawud A., "Assessing the Effect of Prior Distribution Assumption on the Variance Parameters in Evaluating Bioequivalence Trials." Thesis, Georgia State University, 2006.
doi: https://doi.org/10.57709/1059669