Extension of Slippage Test and Selection Procedures

Abstract

The slippage test, considered by Mosteller, Paulson, Karlin and Truax, only deals with the situation where one treatment is better than all others. The usual selection procedures, developed by Bechhofer and Gupta, only select the best treatment or a random subset containing the best treatment. These methods are not appropriate for phase III clinical trials where more precision is needed. We pool all the hypotheses considered in the above two approaches into a family of hypotheses. The statistical procedures are developed to differentiate between many hypotheses in this big family. This solution is more precise and suitable for application to clinical trials. The interpretation of our new procedure is straight-forward and more relevant than the multiple-comparison procedures of Duncan, Newman, and Keuls. In comparison of clinical interventions, in addition to the efficacy main endpoint, consideration should also be given to toxicity and cost. Consideration of these other factors leads to prior preference ranking of treatments, and results in one-sided, two-sided, or equivalence design in two-treatment trials. We extend our procedures to cover the situation when the prior preferences are not equal in multiple-treatment trials. Our procedures include Dunnett's many-one test as a sub-procedure. These statistical procedures are developed under the assumption of continuous normal data. They can be used for clinical trials with binary response and time-to-event endpoints.