Statistical and Regulatory Considerations for Multiple Measures in Bioequivalence Testing

Hyslop, Terry; Anderson, Sharon; Bois, Frédéric Y.; Tozer, Thomas N.

doi:10.3109/10601339509019618

Cited by 15 publications

(14 citation statements)

References 5 publications

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“…In fact, as suggested in Bio-International 94 in Munich, the Schuirmann confidence interval for Cmax of highly variable drugs might be larger than for the AVC (5,6).…”

Section: Resultsmentioning

confidence: 99%

Use of the repeated cross-over design in assessing bioequivalence: (within and between subjects variability — Schuirmann Confidence Intervals estimation)

Roux¹,

Guimart²,

Tenenhaus³

1998

European Journal of Drug Metabolism and Pharmacokinetics

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In 1992, the Division of Bioequivalence in the Office of Generic Drugs published a guide to Statistical procedures for bioequivalence studies using a standard two-treatments cross-over design (1). This paper describes the application of the guidelines to a practical protocol and the recent Proc MIXED (SAS) will be shown to be much more convenient than the traditional Proc GLM for theoretical and practical reasons (correct estimation of residuals, analysis of the within-subjects variation, direct calculation of the Schuirmann 90% Confidence Intervals). This new procedure was applied to a study protocol on riluzole (Rilutek) including a replicate design with the within-subject and between-subject variances being estimated on Cmax and AUC biopharmaceutic parameters.

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“…In fact, as suggested in Bio-International 94 in Munich, the Schuirmann confidence interval for Cmax of highly variable drugs might be larger than for the AVC (5,6).…”

Section: Resultsmentioning

confidence: 99%

Use of the repeated cross-over design in assessing bioequivalence: (within and between subjects variability — Schuirmann Confidence Intervals estimation)

Roux¹,

Guimart²,

Tenenhaus³

1998

European Journal of Drug Metabolism and Pharmacokinetics

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“…The problems are expected to be even more complex in the multivariate case. While extensive literature is now available on bioequivalence testing based on a single pharmacokinetic parameter (see Chow and Liu, 2000;Patterson and Jones, 2006), the literature in the multivariate case is very limited, and is restricted to hypothesis testing concerning the mean vector only; see Hauck et al (1995), Berger and Hsu (1996), Wang et al (1999), Munk and Pfluger (1999), and Romano (2005). Equivalence hypotheses concerning the variance covariance matrices are yet to be formulated and tested.…”

Section: Introductionmentioning

confidence: 98%

“…However, the overall type I error probability (and also the overall power) of the resulting procedure can be assessed only based on a joint analysis of the (AUC, C max data. In particular, Hauck et al (1995) have investigated the overall type I error probability and power of the test for average bioequivalence based on the univariate analysis of AUC and C max . A second issue of concern is the relevance of investigating the subject-by-formulation interaction, since the FDA no longer requires individual bioequivalence (IBE) and population bioequivalence (PBE) testing.…”

Section: Introductionmentioning

confidence: 99%

The Subject-by-Formulation Interaction in Multivariate Bioequivalence

Cao

Mathew

2007

Journal of Biopharmaceutical Statistics

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This paper addresses hypothesis testing problems concerning the subject-by-formulation interaction matrix for the assessment of multivariate bioequivalence. Two problems are addressed: (a) the problem of testing if the subject-by-formulation interaction matrix itself is zero, and (b) the problem of testing if suitable scalar valued functions of the subject-by-formulation interaction matrix is below a threshold. Approximate tests are developed in both cases and the accuracy of the approximation is numerically investigated. The results are illustrated with an example. Even though the literature on univariate bioequivalence testing addresses average bioequivalence, variance bioequivalence and subject-by-formulation interaction, the literature on multivariate bioequivalence deals only with the problem of average bioequivalence. This work appears to be the first attempt to address tests for the subject-by-formulation interaction matrix for testing multivariate bioequivalence.

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“…AUC and C max are analysed separately, and each onesided test is performed at the 5% level. No adjustments for multiplicity are made [6]. In practical terms, a 90% confidence interval for d is constructed using the estimated difference # m m T À # m m R : If the confidence intervals for both AUC and C max fall within the range Àln 1.25 to ln 1.25, then ABE is demonstrated.…”

Section: Introductionmentioning

confidence: 99%

Simulation assessments of statistical aspects of bioequivalence in the pharmaceutical industry

Patterson

Jones

2004

Pharmaceutical Statistics

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Since the early 1990s, average bioequivalence (ABE) has served as the international standard for demonstrating that two formulations of drug product will provide the same therapeutic benefit and safety profile. Population (PBE) and individual (IBE) bioequivalence have been the subject of intense international debate since methods for their assessment were proposed in the late 1980s. Guidance has been proposed by the Food and Drug Administration (FDA) for the implementation of these techniques in the pioneer and generic pharmaceutical industries. Hitherto no consensus among regulators, academia and industry has been established on the use of the IBE and PBE metrics.The need for more stringent bioequivalence criteria has not been demonstrated, and it is known that the PBE and IBE criteria proposed by the FDA are actually less stringent under certain conditions. The statistical properties of method of moments and restricted maximum likelihood modelling in replicate designs will be summarized, and the application of these techniques in the assessment of ABE, IBE and PBE will be considered based on a database of 51 replicate design studies and using simulation.

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Statistical and Regulatory Considerations for Multiple Measures in Bioequivalence Testing

Cited by 15 publications

References 5 publications

Use of the repeated cross-over design in assessing bioequivalence: (within and between subjects variability — Schuirmann Confidence Intervals estimation)

Use of the repeated cross-over design in assessing bioequivalence: (within and between subjects variability — Schuirmann Confidence Intervals estimation)

The Subject-by-Formulation Interaction in Multivariate Bioequivalence

Simulation assessments of statistical aspects of bioequivalence in the pharmaceutical industry

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