IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v30y2003i10p1135-1146.html
   My bibliography  Save this article

Selection of number of dose levels and its robustness for binary response data

Author

Listed:
  • Yangxin Huang

Abstract

Muller & Schmitt (1990) have considered the question of how to choose the number of doses for estimating the median effective dose (ED50) when a probit dose-response curve is correctly assumed. However, they restricted their investigation to designs with doses symmetrical about the true ED50. In this paper, we investigate how the conclusions of Muller & Schmitt may change as the dose designs become slightly asymmetric about the true ED50. In addition, we address the question of the robustness of the number of doses chosen for an incorrectly assumed logistic model, when the dose designs are asymmetric about the assumed ED50. The underlying true dose-response curves considered here include the probit, cubic logistic and Aranda- Ordaz asymmetric models. The simulation results show that, for various underlying true dose-response curves and the uniform design density with doses spaced asymmetrically around the assumed ED50, the choice of as many doses as possible is almost optimal. This agrees with the results obtained for a correctly assumed probit or logistic dose-response curve when the dose designs are symmetric or slightly asymmetric about the ED50.

Suggested Citation

  • Yangxin Huang, 2003. "Selection of number of dose levels and its robustness for binary response data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1135-1146.
    • Handle: RePEc:taf:japsta:v:30:y:2003:i:10:p:1135-1146
    DOI: 10.1080/0266476032000107150
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000107150
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/0266476032000107150?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Byron J. T. Morgan, 1985. "The Cubic Logistic Model for Quantal Assay Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 34(2), pages 105-113, June.
    2. Robert K. Tsutakawa, 1980. "Selection of Dose Levels for Estimating a Percentage Point of a Logistic Quantal Response Curve," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 25-33, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hanemann, W. Michael & Kanninen, Barbara, 1996. "The Statistical Analysis Of Discrete-Response Cv Data," CUDARE Working Papers 25022, University of California, Berkeley, Department of Agricultural and Resource Economics.
    2. Linda M. Haines & Inna Perevozskaya & William F. Rosenberger, 2003. "Bayesian Optimal Designs for Phase I Clinical Trials," Biometrics, The International Biometric Society, vol. 59(3), pages 591-600, September.
    3. Yangxin Huang, 2002. "Robustness of interval estimation of the 90% effective dose: Bootstrap resampling and some large-sample parametric methods," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(7), pages 1071-1081.
    4. Huang, Yangxin, 2001. "Interval estimation of the ED50 when a logistic dose-response curve is incorrectly assumed," Computational Statistics & Data Analysis, Elsevier, vol. 36(4), pages 525-537, June.
    5. Silvio S. Zocchi & Anthony C. Atkinson, 1999. "Optimum Experimental Designs for Multinomial Logistic Models," Biometrics, The International Biometric Society, vol. 55(2), pages 437-444, June.
    6. Zacks, S. & Rogatko, A. & Babb, J., 1998. "Optimal Bayesian-feasible dose escalation for cancer phase I trials," Statistics & Probability Letters, Elsevier, vol. 38(3), pages 215-220, June.
    7. Hui Li & Robert Malkin, 2000. "An approximate Bayesian up-down method for estimating a percentage point on a dose-response curve," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(5), pages 579-587.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:japsta:v:30:y:2003:i:10:p:1135-1146. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.