• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.645-657
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• 2007

Several statistical procedures for assessment of bioequivalence of variabilities between two drug formulations in bioequivalence trials are reviewed and modified methods for assessing total variability are suggested. The problem of the current US FDA aggregate criterion for population bioequivalence and the necessity of disaggregate criterion are discussed with an illustrated example.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.443-452
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• 2001

The 2x3 cross-over design is proposed for the bioequivalence of two test drug formulations with a reference drug formulation. Oh et al.(1999) and Park et al.(1998) derived 3x2 cross-over design and discussed its benefits, since the 3x3 cross-over design may not be of practical design. We discuss the statistical issues for2x3 cross-over design and show its statistical properties. Bioequivalence problem in 2x3 cross-over design is considered statistically and an illustrated example is given.

• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.633-640
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• 2007

We develop noninformative priors for the ratio of the lognormal means in equal variances case. The Jeffreys' prior and reference priors are derived. We find a first order matching prior and a second order matching prior. It turns out that Jeffreys' prior and all of the reference priors are first order matching priors and in particular, one-at-a-time reference prior is a second order matching prior. One-at-a-time reference prior meets very well the target coverage probabilities. We consider the bioequivalence problem. We calculate the posterior probabilities of the hypotheses and Bayes factors under Jeffreys' prior, reference prior and matching prior using a real-life example.

• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.675-686
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• 2013

The Korea Food and Drug Administration(KFDA) recommends the use of a $2{\times}2$ crossover design to assess the bioequivalence of generic drugs. However, a standard $2{\times}2$ crossover design for bioequivalence trials is often considered problematic due to ethical and economic issues as highly variable drugs are usually required by large numbers of subjects when designing the trial. To overcome this problem a $2{\times}4$ crossover design has been a recommended option as per US regulations; in addition, a $2{\times}3$ crossover design has also recently drawn special attention as an efficient alternative. The current KFDA regulation requires an ANOVA table for every bioequivalence study; however, ANOVA tables of $2{\times}4$ and $2{\times}3$ crossover designs have never been published in the literature. This study shows the derivation of tables of analysis of variance for a $2{\times}4$ cross-over design and a $2{\times}3$ cross-over design. We also suggest a sample size formulas for $2{\times}2$, $2{\times}4$ and $2{\times}3$ crossover designs to provide information on the selection of efficient designs for highly variable drugs.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.251-261
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• 2004

The paper focuses primarily on the standard linear multiple regression model where the parameter of interest is a ratio of two regression coefficients. The general model includes the calibration model, the Fieller-Creasy problem, slope-ratio assays, parallel-line assays, and bioequivalence. We provide an orthogonal transformation (cf. Cox and Reid (1987)) of the original parameter vector. Also, we give some remarks on the difficulties associated with likelihood based confidence interval.