Alternate indexes of variation for the analysis of experimental data

Worley, Jimmy W.; Morrell, James A.; Duewer, David L.; Peterfreund, Laurie A.

doi:10.1021/ac00267a038

Cited by 2 publications

(2 citation statements)

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“…[1][2][3][4] Statistically, power represents the probability the null hypothesis will be rejected when the alternative hypothesis is true. [5][6][7] Since the null hypothesis in bioequivalence studies is that the substances are bioinequivalent, the power of a bioequivalence study is the probability of proving bioequivalence when the products are in fact bioequivalent. [5,7,8] Because finding the optimal sample size ensures adequate power, the sample size calculation is one of the most important steps in designing a bioequivalence study.…”

Section: Introductionmentioning

confidence: 99%

“…[5][6][7] Since the null hypothesis in bioequivalence studies is that the substances are bioinequivalent, the power of a bioequivalence study is the probability of proving bioequivalence when the products are in fact bioequivalent. [5,7,8] Because finding the optimal sample size ensures adequate power, the sample size calculation is one of the most important steps in designing a bioequivalence study. Sample sizes that are too large increase the cost of the study and unnecessarily expose many subjects to the drug.…”

Section: Introductionmentioning

confidence: 99%

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A post hoc analysis of intra-subject coefficients of variation in pharmacokinetic measures to calculate optimal sample sizes for bioequivalence studies

Chung

Lee

et al. 2018

Transl Clin Pharmacol

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Because bioequivalence studies are performed using a crossover design, information on the intrasubject coefficient of variation (intra-CV) for pharmacokinetic measures is needed when determining the sample size. However, calculated intra-CVs based on bioequivalence results of identical generic drugs produce different estimates. In this study, we collected bioequivalence results using public resources from the Ministry of Food and Drug Safety (MFDS) and calculated the intraCVs of various generics. For the generics with multiple bioequivalence results, pooled intra-CVs were calculated. The estimated intra-CVs of 142 bioequivalence studies were 14.7±8.2% for AUC and 21.7±8.8% for C max . Intra-CVs of C max were larger than those of area under the concentrationtime curve (AUC) in 129 studies (90.8%). For the 26 generics with multiple bioequivalence results, the coefficients of variation of intra-CVs between identical generics (mean±sd (min ~ max)) were 38.0±24.4% (1.9 ~ 105.3%) for AUC and 27.9±18.2% (4.0 ~ 70.1%) for C max . These results suggest that substantial variation exists among the bioequivalence results of identical generics. In this study, we presented the intra-CVs of various generics with their pooled intra-CVs. The estimated intraCVs calculated in this study will provide useful information for planning future bioequivalence studies. (This is republication of the article 'Transl Clin Pharmacol 2017;25:179-182

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