Abstract
Bioequivalence assessment is an issue of great interest. Development of statistical methods for assessing bioequivalence is an important area of research for statisticians. Bioequivalence is usually determined based on the normal distribution. We relax this assumption and develop a semi-parametric mixed model for bioequivalence data. The proposed method is quite flexible and practically meaningful. Our proposed method is based on a mixture normal distribution and a non-parametric Bayesian approach using a Dirichlet process mixture prior. A numerical example illustrates the use of our procedure.
Original language | English (US) |
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Pages (from-to) | 1224-1236 |
Number of pages | 13 |
Journal | Statistics in Medicine |
Volume | 26 |
Issue number | 6 |
DOIs | |
State | Published - Mar 15 2007 |
Externally published | Yes |
Keywords
- Average bioequivalence
- Cross-over design
- Gibbs sampling
- Markov chain Monte Carlo
- Mixture of Dirichlet process prior
- Mixture of normal
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability