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Selection of Dose Levels for Estimating a Percentage Point of a Logistic Quantal Response Curve

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  • Robert K. Tsutakawa

Abstract

Experimental designs are presented for estimating an extreme percentage point of a logistic distribution when the observations are quantal responses and the location and scale parameters are unknown. The method is based on a prior distribution of the parameters and a predicted value of the posterior variance. The paper is an extension of an earlier article (Tsutakawa, 1972) for the case when the scale parameter is known.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssc:v:29:y:1980:i:1:p:25-33
    DOI: 10.2307/2346406
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.

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