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Computational and Mathematical Methods in Medicine
Volume 2016 (2016), Article ID 4680642, 6 pages
http://dx.doi.org/10.1155/2016/4680642
Research Article

A Bioequivalence Test by the Direct Comparison of Concentration-versus-Time Curves Using Local Polynomial Smoothers

1Division of Clinical Research, First Hospital of Jilin University, 71 Xinmin Street, Changchun, Jilin 130021, China
2Center for Clinical and Translational Science, The Rockefeller University, 1230 York Avenue, New York, NY 10065, USA
3School of Mathematics, Jilin University, 2699 Qianjin Street, Changchun, Jilin 130012, China
4Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA
5Laboratory of Molecular Neurooncology, The Rockefeller University, 1230 York Avenue, New York, NY 10065, USA
6Division of Rheumatology, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
7Clinical Pharmacology Center, Research Institute of Translational Medicine, First Hospital of Jilin University, Dongminzhu Street, Changchun 130021, China
8College of Life Science, Jilin University, 2699 Qianjin Street, Changchun 130012, China
9Center for Biostatistics, Department of Population, Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
10Department of Genetics and Genomics Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA

Received 23 June 2016; Revised 29 August 2016; Accepted 7 September 2016

Academic Editor: Andrzej Kloczkowski

Copyright © 2016 Suyan Tian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. B. Ewald, “Making sense of equivalence and non-inferiority trials,” Australian Prescriber, vol. 36, no. 5, pp. 170–173, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Ocana, M. Sánchez, A. Sanchez, and J. L. Carrasco, “On equivalence and bioequivalence testing,” SORT. Statistics and Operations Research Transactions, vol. 32, no. 2, pp. 151–176, 2008. View at Google Scholar · View at MathSciNet
  3. A. S. Kesselheim, A. S. Misono, J. L. Lee et al., “Clinical equivalence of generic and brand-name drugs used in cardiovascular disease: a systematic review and meta-analysis,” Journal of the American Medical Association, vol. 300, no. 21, pp. 2514–2526, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. FDA, Guidance for Industry: Bioequivalence Guidance, 2006.
  5. L. E. Barker, E. T. Luman, M. M. McCauley, and S. Y. Chu, “Assessing equivalence: an alternative to the use of difference tests for measuring disparities in vaccination coverage,” American Journal of Epidemiology, vol. 156, no. 11, pp. 1056–1061, 2002. View at Publisher · View at Google Scholar · View at Scopus
  6. E. Walker and A. S. Nowacki, “Understanding equivalence and noninferiority testing,” Journal of General Internal Medicine, vol. 26, no. 2, pp. 192–196, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. S. S. Jambhekar and P. J. Breen, Basic Pharmacokinetics, Pharmaceutical Press, 2009.
  8. H. Quan, J. Bolognese, and W. Yuan, “Assessment of equivalence on multiple endpoints,” Statistics in Medicine, vol. 20, no. 21, pp. 3159–3173, 2001. View at Publisher · View at Google Scholar · View at Scopus
  9. W. Wang, J. T. G. Hwang, and A. Dasgupta, “Statistical tests for multivariate bioequivalence,” Biometrika, vol. 86, no. 2, pp. 395–402, 1999. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  10. P. Ghosh and M. Gönen, “Bayesian modeling of multivariate average bioequivalence,” Statistics in Medicine, vol. 27, no. 13, pp. 2402–2419, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. H. A. Bayoud and A. M. Awad, “Performance of several bioequivalence metrics for assessing the rate and extent of absorption,” Journal of Bioequivalence and Bioavailability, vol. 3, no. 7, pp. 174–177, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. W. S. Cleveland and C. Loader, “Smoothing by local regression: principles and methods,” in Statistical Theory and Computational Aspects of Smoothing, W. Härdle and M. G. Schimek, Eds., Contributions to Statistics, pp. 10–49, Springer, New York, NY, USA, 1996. View at Publisher · View at Google Scholar
  13. J. Quackenbush, “Microarray data normalization and transformation,” Nature Genetics, vol. 32, no. 5, supplement, pp. 496–501, 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Anders and W. Huber, “Differential expression analysis for sequence count data,” Genome Biology, vol. 11, no. 10, article R106, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. W. S. Cleveland, “Robust locally weighted regression and smoothing scatterplots,” Journal of the American Statistical Association, vol. 74, no. 368, pp. 829–836, 1979. View at Publisher · View at Google Scholar · View at MathSciNet
  16. J. Fan and I. Gijbels, “Variable bandwidth and local linear regression smoothers,” The Annals of Statistics, vol. 20, no. 4, pp. 2008–2036, 1992. View at Publisher · View at Google Scholar · View at MathSciNet
  17. C. R. Loader, “Bandwidth selection: classical or plug-in?” The Annals of Statistics, vol. 27, no. 2, pp. 415–438, 1999. View at Publisher · View at Google Scholar · View at MathSciNet
  18. B. Efron, “Bootstrap methods: another look at the jackknife,” The Annals of Statistics, vol. 7, no. 1, pp. 1–26, 1979. View at Publisher · View at Google Scholar · View at MathSciNet
  19. P. I. Good, Permutation, Parametric and Bootstrap Tests of Hypotheses, Springer, Berlin, Germany, 3rd edition, 2005.
  20. W. J. Welch, “Construction of permutation tests,” Journal of the American Statistical Association, vol. 85, no. 411, pp. 693–698, 1990. View at Publisher · View at Google Scholar
  21. D. E. Orange, N. E. Blachere, J. Fak et al., “Dendritic cells loaded with FK506 kill T cells in an antigen-specific manner and prevent autoimmunity in vivo,” eLife, vol. 2013, no. 2, article e00105, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. K. Y. Liang and S. L. Zeger, “Longitudinal data analysis using generalized linear models,” Biometrika, vol. 73, no. 1, pp. 13–22, 1986. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  23. S. L. Zeger and K. Y. Liang, “Longitudinal data analysis for discrete and continuous outcomes,” Biometrics, vol. 42, no. 1, pp. 121–130, 1986. View at Publisher · View at Google Scholar · View at Scopus
  24. A. van der Meersch, A. Dechartres, and P. Ravaud, “Quality of reporting of bioequivalence trials comparing generic to brand name drugs: a methodological systematic review,” PLoS ONE, vol. 6, no. 8, article e23611, 2011. View at Publisher · View at Google Scholar · View at Scopus