Analysis of Dose–Response Studies—Emax Model

  • James Macdougall
Part of the Statistics for Biology and Health book series (SBH)

Abstract

The E max model is a nonlinear model frequently used in dose–response analyses. The model is shown in Eq. (9.1)

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References

  1. Angus, B.J., Thaiaporn, I., Chanthapadith, K., Suputtamongkol, Y., and White, N.J. 2002. Oral artesunate dose-response relationship in acute falciparum malaria. Antimicrobial Agents and Chemotherapy 46(3):778–782.PubMedCrossRefGoogle Scholar
  2. Bates, D.M., and Watts, D.G. 1988. Nonlinear Regression Analysis and Its Applications. New York: Wiley.MATHGoogle Scholar
  3. Boroujerdi, M. 2002. Pharmacokinetics: Principles and Applications. New York: McGraw Hill.Google Scholar
  4. Davidian, M., and Giltinan, D. M. 1995. Nonlinear Models for Repeated Measurement Data. New York: Chapman and Hall.Google Scholar
  5. Draper, N., and Smith, H. 1966. Applied Regression Analysis, 2nd ed. New York: Wiley.Google Scholar
  6. Dutta, S., Matsumoto, Y., and Ebling, W.F. 1996. Is it possible to estimate the parameters of the sigmoid Emax model with truncated data typical of clinical studies? Journal of Pharmaceutical Sciences 85(2):232–239.PubMedCrossRefGoogle Scholar
  7. Girard P., Laporte-Simitsidis S., Mismetti P., Decousus H., and Boissel J. 1995. Influence of confounding factors on designs for dose-effect relationships estimates. Statistics in Medicine 14:987–1005.PubMedCrossRefGoogle Scholar
  8. ICH-E4 Guideline. 1998. Dose–Response Information to Support Drug Registration, Step 4. Web-site: http://www.ich.org/cache/compo/276-254-1.htmlGoogle Scholar
  9. O’Connell M.A., Belanger, B.A., and Haaland, P.D. 1993. Calibration and assay development using the four-parameter logistic model. Chemometrics and Intelligent Laboratory Systems 20:97–114.CrossRefGoogle Scholar
  10. Pinheiro, J.C., and Bates, D.M. 2000. Mixed-Effects Models in S and S-PLUS. New York: Springer-Verlag.MATHGoogle Scholar
  11. Ruberg, S.J. 1995. Dose–response studies II. Analysis and interpretation. Journal of Biopharmaceutical Statistics 5(1):15–42.PubMedCrossRefGoogle Scholar
  12. SAS/STAT User’s Guide Version 8 Volumes 1–3. 1999. Cary NC: SAS Publishing.Google Scholar
  13. Seber, G. F., and Wild, C.J. 2003. Nonlinear Regression. New Jersey: Wiley.Google Scholar
  14. Senn, S. 1997. Statistical Issues in Drug Development. West Sussex, England: Wiley.Google Scholar
  15. Sheiner, L.B., Beal, S.L., and Sambol, N.C. 1989. Study designs for dose-ranging. Clinical Pharmacology and Therapeutics 46:63–77.PubMedCrossRefGoogle Scholar
  16. Sheiner, L.B., Hashimoto Y., and Beal, S.L. 1991. A simulation study comparing designs for dose ranging. Statistics in Medicine 10:303–321.PubMedCrossRefGoogle Scholar
  17. Temple, R. 1982. Government viewpoint of clinical trials. Drug Information Journal 16: 10–17.Google Scholar
  18. Temple, R. 2004. Where protocol design has been a critical factor in success or failure. Presentation at the DIA Annual Meeting June 14, 2004.Google Scholar
  19. Vonesh, E.F. and Chinchilli,V.M. (1997) Linear and Nonlinear Models for the Analysis of Repeated Measurements. New York: Marcel Dekker.MATHGoogle Scholar

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© Springer 2006

Authors and Affiliations

  • James Macdougall

There are no affiliations available

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