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Stepwise Confidence Intervals for Monotone Dose–Response Studies

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  • Jianan Peng
  • Chu‐In Charles Lee
  • Karelyn A. Davis
  • Weizhen Wang

Abstract

Summary In dose–response studies, one of the most important issues is the identification of the minimum effective dose (MED), where the MED is defined as the lowest dose such that the mean response is better than the mean response of a zero‐dose control by a clinically significant difference. Dose–response curves are sometimes monotonic in nature. To find the MED, various authors have proposed step‐down test procedures based on contrasts among the sample means. In this article, we improve upon the method of Marcus and Peritz (1976, Journal of the Royal Statistical Society, Series B38, 157–165) and implement the dose–response method of Hsu and Berger (1999, Journal of the American Statistical Association94, 468–482) to construct the lower confidence bound for the difference between the mean response of any nonzero‐dose level and that of the control under the monotonicity assumption to identify the MED. The proposed method is illustrated by numerical examples, and simulation studies on power comparisons are presented.

Suggested Citation

  • Jianan Peng & Chu‐In Charles Lee & Karelyn A. Davis & Weizhen Wang, 2008. "Stepwise Confidence Intervals for Monotone Dose–Response Studies," Biometrics, The International Biometric Society, vol. 64(3), pages 877-885, September.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:3:p:877-885
    DOI: 10.1111/j.1541-0420.2007.00958.x
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    1. Miwa, Tetsuhisa & Hayter, A. J. & Liu, Wei, 2000. "Calculations of level probabilities for normal random variables with unequal variances with applications to Bartholomew's test in unbalanced one-way models," Computational Statistics & Data Analysis, Elsevier, vol. 34(1), pages 17-32, July.
    2. Charles W. Dunnett, 1989. "Multivariate Normal Probability Integrals with Product Correlation Structure," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 38(3), pages 564-579, November.
    3. M. Hellmich & W. Lehmacher, 2005. "Closure Procedures for Monotone Bi-Factorial Dose–Response Designs," Biometrics, The International Biometric Society, vol. 61(1), pages 269-276, March.
    4. F. Bretz & J. C. Pinheiro & M. Branson, 2005. "Combining Multiple Comparisons and Modeling Techniques in Dose-Response Studies," Biometrics, The International Biometric Society, vol. 61(3), pages 738-748, September.
    5. Lee, Chu-In Charles & Peng, Jianan & Liu, Lin, 2006. "Statistical Inference for the Difference Between the Best Treatment Mean and a Control Mean," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1050-1058, September.
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