• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.43-48
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• 2006

In 2001 US FDA proposed a draft guidance for future in vivo bioequivalence studies. The guidance suggested specific criteria for new drug sponsors to show prescribability and switchability in bioequivalence testing for approval of generic drugs. However, there is less acceptance of the need to change statistical procedures and study designs from those currently used to assess the current criterion of average bioequivalence. The measures of population and individual bioequivalence testing are introduced and statistical procedures for them are discussed.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.67-72
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• 2002

A Bayesian testing procedure is proposed for assessment of bioequivalence in both mean and variance which ensures population bioequivalence under normality assumption. We derive the joint posterior distribution of the means and variances in a standard 2 ${\times}$ 2 crossover experimental design and propose a Bayesian testing procedure for bioequivalence based on a Markov chain Monte Carlo methods. The proposed method is applied to a real data set.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.159-171
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• 2005

The US Food and Drug Administration(FDA) recommends that population bioequivalence and individual bioequivalence would be assessed to address the prescribability and switchability between a brand-name drug and its new formulation or generic copy in its 2001 guidance document. The test for population bioequivalence in the latest FDA guidance is recommended in 2 x 4 crossover design, but it turns out to be very conservative. Recently Lee, Shao & Chow(2002), Chow, Shao & Wang(2003) and McNally, Iyer & Mathew(2002) proposed new statistical methods for assessing population bioequivalence between drugs to correct the biasness of current FDA method. Since 2 x 2 crossover experiment is most welcomed design in bioequivalence testing, we adopt their methods to 2 x 2 crossover designs and compare their methodologies with FDA one through the simulation study.