Testing Whether an Identified Treatment Is Best

We consider the problem of testing whether an identified treatment is better than each of K treatments. Suppose there are univariate test statistics Sithat contrast the identified treatment with treatment i for i = 1, 2,..., K. The min test is defined to be the α-level procedure that rejects the null hypothesis that the identified treatment is not best when, for all i, Sirejects the one-sided hypothesis, at the α-level, that the identified treatment is not better than the ith treatment. In the normal case where Siare t statistics the min test is the likelihood ratio test. For distributions satisfying mild regularity conditions, if attention is restricted to test statistics that are monotone nondecreasing functions of Si, then regardless of their covariance structure the min test is an optimal α-level test. Tables of the sample size needed to achieve power .5, .8, .90, and .95 are given for the min test when the Siare Student's t and Wilcoxon.