A semi-parametric Bayesian approach to average bioequivalence

Pulak Ghosh, Gary L. Rosner

Research output: Contribution to journalArticlepeer-review

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 languageEnglish (US)
Pages (from-to)1224-1236
Number of pages13
JournalStatistics in Medicine
Volume26
Issue number6
DOIs
StatePublished - Mar 15 2007
Externally publishedYes

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

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