• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.245-257
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• 2006

A modified Anderson and Hauck(1983) test for analyzing a two-sequence two-period crossover design in bioequivalence trials is proposed when some observations at the second period are missing. It is based on the maximum likelihood estimators of average bioequivalence model and designed for handling missing at random(MAR) situation. The performance of the proposed test is compared to other tests using Monte Carlo simulations.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1055-1076
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• 2011

This paper reviews the definition of highly variable drug(HVD), the present regulatory recommendations and the approaches proposed in the literature to deal with the bioequivalence issues of HVD. The concept and the statistical approach of scaled average bioequivalence(SABE) is introduced and discussed with the current regulatory methods. The recommendations for SABE approach are proposed and the further study topics related to HVDs are also presented.

• 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In 2001 US FDA proposed a draft guidance for future in vivo bioequivalence studies.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.

• 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.

• Korean Journal of Clinical Pharmacy
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• v.19 no.1
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• pp.50-60
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• 2009

Highly variable drugs (within-subject variability greater than 30%) have been difficult to meet current regulatory acceptance criteria using a reasonable number of study subjects. In this study, we reviewed previous studies presenting alternative approaches for bioequivalence evaluation of highly variable drugs, and focused on an approach for widening the bioequivalence acceptance limits using within-subject variability. We discussed the suggested five solutions for highly variable drug including the deletion of $C_{max}$ of the bioequivalence criteria, direct expansion of bioequivalence limit, multiple dose studies in steady state, bioequivalence assessment on the metabolite, add-on study, and widening the bioequivalence acceptance limits based on reference variability. The methods for widening of bioequivalence limits based on reference variability are scaled average bioequivalence containing within-subject variability on reference drug (${\sigma}_{WR}$), population bioequivalence derived from total variability on reference drug (${\sigma}_{TR}$) and test drug (${\sigma}_{TT}$), and individual bioequivalence derived from subject by formulation interaction variability (${\sigma}_D$) and within subject variability on reference drug (${\sigma}_{WR}$) and test drug (${\sigma}_{TR}$). To apply these methods, the switching variability (${\sigma}_0$) will have to be set by the regulatory authorities. The proposals of bioequivalence evaluation approach for the highly variable in Korea are presented for both of new drug and reevaluation drug.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.161-167
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• 2011

The primary variables are often systematically related to other influences apart from drug effect. For instance, there may be relationships to covariates such as health conditions or prognostic factors. When a $2{\times}2$ crossover experiment for bioequivalence is designed, the statistical adjustment for the influence of covariates should be considered if some covariates influence the drug effect. Statistical inference for assessing average bioequivalence for a $2{\times}2$ crossover design with covariates is given and an illustrated example is presented with discussion.

• 대한임상약리학회:학술대회논문집
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• 2007.11a
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• pp.81-81
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• 2007

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• 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.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.641-647
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• 2020

The 2 × 3 crossover design, a modified version of the 3 × 3 crossover design, is considered to compare the bioavailability of two generic candidates with a reference drug. The 2 × 3 crossover design is more economically favorable due to decrease in the number of sequences, rather than conducting a 3×3 crossover trial or two separate 2 × 2 crossover trials. However, when using a higher-order crossover trial, the risk of drop-outs and withdrawals of subjects increases, so the suitable statistical inferences for missing data is needed. The bioequivalence model of a of 2×3 crossover trial with missing data is defined and the statistical procedures of assessing bioequivalence is proposed. An illustrated example of the 2 × 3 trial with missing data is also presented with discussion.

• Journal of Pharmaceutical Investigation
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• v.39 no.4
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• pp.233-242
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• 2009

Bioequivalence (BE) studies provide important information in the overall set of data that ensure the availability of safe and effective medicines to patients and practitioners. Thus its determination of proper criterion for assessing BE is very important. BE is frequently expressed or measured by estimating area under the plasma concentration-time curve (AUC) and maximum concentration ($C_{max}$) that are reflective of systemic exposure. In all countries except Canada, the acceptance criteria of BE is that the 90% confidence interval of difference in the average values of logarithmic AUC and $C_{max}$ between test and reference products is within the acceptable range of log(0.8) ${\sim}$ log(1.25). In Canada, unlike other countries, point estimation instead of applying 90% confidence interval is applied to assess $C_{max}$ which is, in essence, more variable than AUC. We also compared other parts of BE guidelines which include a fed study, average BE (ABE), scaled-ABE, population BE (PBE), individual BE (IBE), dropout & withdrawal, sampling frequency & time and number of subjects. This article reviews the most recent BE guidelines of Korea, USA, Europe, Canada and Japan, highlighting the differences focused on Korean BE guidelines compared to other countries. It will help us to revise BE guideline of Korea reflecting international trends. Finally, it is strongly recommended that the extended acceptance criterion for the highly variable drug among all the considering aspects for the revision of current BE guideline has to be adopted into Korea BE guideline in the nearest future.