Volume 21, Issue 5
Research Article

Individual bioequivalence testing under 2×3 designs

Shein‐Chung Chow

Statplus Inc., Heston Hall, Suite 206, 1790 Yardley‐Langhorne Road, Yardley, PA 19067, U.S.A.

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Jun Shao

Corresponding Author

Department of Statistics, University of Wisconsin, 1210 W. Dayton Street, Madison, WI 53706, U.S.A.

Department of Statistics, University of Wisconsin‐Madison, 1210 W. Dayton Street, Madison, WI 53706‐1685, U.S.A.Search for more papers by this author
Hansheng Wang

Department of Statistics, University of Wisconsin, 1210 W. Dayton Street, Madison, WI 53706, U.S.A.

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First published: 19 February 2002
Citations: 30

Abstract

In recent years, as more generic drug products become available, it is a concern not only whether generic drug products that have been approved based on the regulation of average bioequivalence will have the same quality, safety and efficacy as that of the brand‐name drug product, but also whether the approved generic drug products can be used interchangeably. In its recent draft guidance, the U.S. Food and Drug Administration (FDA) recommends that individual bioequivalence (IBE) be assessed using the method proposed by Hyslop, Hsuan, and Holder to address drug switchability. The FDA suggests that a 2×4 cross‐over design be considered for assessment of IBE, while a 2×3 cross‐over design may be used as an alternative design to reduce the length and cost of the study. Little or no information regarding the statistical procedures under 2×3 cross‐over designs is discussed in the guidance. In this paper, a detailed statistical procedure for assessment of IBE under 2×3 cross‐over designs is derived. The main purpose of this paper, however, is to derive an IBE test under an alternative 2×3 design and show that the resulting IBE test is better than that under a 2×3 cross‐over design and is comparable to or even better than that under a 2×4 cross‐over design. Our conclusions are supported by theoretical considerations and empirical results. Furthermore, a method of determining the sample sizes required for IBE tests to reach a given level of power is proposed. Copyright © 2002 John Wiley & Sons, Ltd.

Number of times cited according to CrossRef: 30

  • Reference Datasets for Studies in a Replicate Design Intended for Average Bioequivalence with Expanding Limits, The AAPS Journal, 10.1208/s12248-020-0427-6, 22, 2, (2020).
  • Studies on drug switchability showed heterogeneity in methodological approaches: a scoping review, Journal of Clinical Epidemiology, 10.1016/j.jclinepi.2018.05.003, 101, (5-16), (2018).
  • mHealth versus face-to-face: study protocol for a randomized trial to test a gender-focused intervention for young African American women at risk for HIV in North Carolina, BMC Public Health, 10.1186/s12889-018-5796-8, 18, 1, (2018).
  • Chapter 11 Design and Analysis of Studies for Assessing Interchangeability, Biosimilar Drug Product Development, 10.1201/9781315119878-12, (297-322), (2017).
  • Inflation of Type I Error in the Evaluation of Scaled Average Bioequivalence, and a Method for its Control, Pharmaceutical Research, 10.1007/s11095-016-2006-1, 33, 11, (2805-2814), (2016).
  • The design of clinical trials to support the switching and alternation of biosimilars, Expert Opinion on Biological Therapy, 10.1080/14712598.2017.1238454, 16, 12, (1445-1453), (2016).
  • Some thoughts on drug interchangeability, Journal of Biopharmaceutical Statistics, 10.1080/10543406.2015.1092027, 26, 1, (178-186), (2015).
  • FDA regulatory guidance, Biosimilars and Interchangeable Biologics, 10.1201/b19161, (157-226), (2015).
  • European regulatory guidance, Biosimilars and Interchangeable Biologics, 10.1201/b19161, (81-134), (2015).
  • Bioavailability and bioequivalence in drug development, Wiley Interdisciplinary Reviews: Computational Statistics, 10.1002/wics.1310, 6, 4, (304-312), (2014).
  • Bibliography, Handbook of Bioequivalence Testing, Second Edition, 10.1201/b17582-28, (873-936), (2014).
  • Sample Size Determination for Individual Bioequivalence Inference, PLoS ONE, 10.1371/journal.pone.0109746, 9, 10, (e109746), (2014).
  • References, Design and Analysis of Clinical Trials, undefined, (799-844), (2013).
  • Subject-Treatment Interaction, Encyclopedia of Biopharmaceutical Statistics, Third Edition, 10.1201/b14674, (1316-1321), (2012).
  • Bayesian Estimation in Bioequivalence Study, Korean Journal of Applied Statistics, 10.5351/KJAS.2011.24.6.1095, 24, 6, (1095-1102), (2011).
  • On Evaluation of Bioequivalence for Highly Variable Drugs, Korean Journal of Applied Statistics, 10.5351/KJAS.2011.24.6.1055, 24, 6, (1055-1076), (2011).
  • Optimal Thresholds from Mixture Distributions, Korean Journal of Applied Statistics, 10.5351/KJAS.2010.23.1.013, 23, 1, (13-28), (2010).
  • Individual Bioequivalence, Encyclopedia of Biopharmaceutical Statistics, 10.3109/9781439822463, (629-634), (2010).
  • Individual Bioequivalence Tests under 3 X 2 Design, Korean Journal of Applied Statistics, 10.5351/KJAS.2010.23.1.139, 23, 1, (139-150), (2010).
  • Technical Improvements of the Projection of Household Health Care Expenditure, Korean Journal of Applied Statistics, 10.5351/KJAS.2010.23.1.001, 23, 1, (1-11), (2010).
  • Subject-Treatment Interaction, Encyclopedia of Biopharmaceutical Statistics, 10.3109/9781439822463, (1316-1321), (2010).
  • Evaluation of Bioequivalence for Highly Variable Drugs with Scaled Average Bioequivalence, Clinical Pharmacokinetics, 10.2165/11318040-000000000-00000, 48, 11, (725-743), (2009).
  • Construction of confidence limits about effect measures: A general approach, Statistics in Medicine, 10.1002/sim.3095, 27, 10, (1693-1702), (2007).
  • Bioequivalence Studies in Drug Development, Methods and Applications by D. HAUSCHKE, V. STEINIJANS, and I. PIGEOT, Biometrics, 10.1111/j.1541-0420.2007.00856_4.x, 63, 3, (969-970), (2007).
  • A Comparative Study of Statistical Methods for Population Bioequivalence in 2 X 2 Crossover Design, Korean Journal of Applied Statistics, 10.5351/KJAS.2005.18.1.159, 18, 1, (159-171), (2005).
  • The Analysis of Academic Achievements for Different Selection Criteria via Linear Mixed Models, Korean Journal of Applied Statistics, 10.5351/KJAS.2005.18.1.015, 18, 1, (15-26), (2005).
  • Bayesian Clustering of Prostate Cancer Patients by Using a Latent Class Poisson Model, Korean Journal of Applied Statistics, 10.5351/KJAS.2005.18.1.001, 18, 1, (1-13), (2005).
  • A Bayesian Approach on Sample Size Calculation for Comparing Means, Journal of Biopharmaceutical Statistics, 10.1081/BIP-200067789, 15, 5, (799-807), (2005).
  • Simulation assessments of statistical aspects of bioequivalence in the pharmaceutical industry, Pharmaceutical Statistics, 10.1002/pst.88, 3, 1, (13-23), (2004).
  • Assessing individual bioequivalence with high‐order cross‐over designs: a unified procedure, Statistics in Medicine, 10.1002/sim.1382, 22, 18, (2847-2860), (2003).

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