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We consider identifying the minimum effective dose (MED) in a dose-response study, where the MED is defined to be the lowest dose level producing an effect over that of the zero-dose control. Proposed herein is a nonparametric procedure based on the Mann-Whitney statistic incorporated with the step-down closed testing scheme. A numerical example demonstrates the feasibility of the proposed nonparametric procedure. Finally, the comparative results of a Monte Carlo level and power study for small sample sizes are presented and discussed.

Lim and Wolfe (1997, Biometrics 53, 410-418) proposed rank-based multiple test procedures for identifying the dose levels that are more effective than the zero-dose control in randomized complete block designs when it can be assumed that the efficacy of the increasing dose levels is monotonically increasing up to a point, followed by a monotonic decrease. Modifications of the Lim-Wolfe tests are suggested that provide more practical and powerful alternatives. Two numerical examples are illustrated and the results of a Monte Carlo power study are presented.

In this paper we are concerned with comparing umbrella pattern treatment effects with a control in a one-way layout. The problem of testing whether there is at least one treatment that is better than the control is considered. Distribution-free tests are proposed for both cases where the peak of the umbrella is known or unknown. Approximate small-sample critical values are presented and the results of a Monte Carlo power study are discussed.