title: Stepwise confidence intervals for monotone dose-response studies.
abstract: 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 B 38, 157-165) and implement the dose-response method of Hsu and Berger (1999, Journal of the American Statistical Association 94, 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.
authors.affiliation: Department of Mathematics and Statistics, Acadia University, Wolfville, Nova Scotia, B4P 2R6 Canada.
authors.forename: Jianan
authors.initials: J
authors.lastname: Peng
authors.affiliation: Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John's, Newfoundland, A1C 5S7 Canada.
authors.forename: Chu-In Charles
authors.initials: CC
authors.lastname: Lee
authors.affiliation: School of Mathematics and Statistics, Carleton University, Ottawa, Ontario, K1S 5B6 Canada.
authors.forename: Karelyn A
authors.initials: KA
authors.lastname: Davis
authors.affiliation: Department of Mathematics and Statistics, Wright State University, Dayton, Ohio 45435, U.S.A.
authors.forename: Weizhen
authors.initials: W
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000818:Animals; D001699:Biometry / Q000379:methods; D016001:Confidence Intervals; D004305:Dose-Response Relationship, Drug; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical