Abstract
Phase III clinical studies are most often powered to detect an overall difference in response to treatment between two treatment arms. However in many cases, any response to treatment is restricted to a subset of patients. Therefore, traditional randomized clinical trials with broad eligibility criteria may result in missing effective treatments. If predictive assays have been developed beforehand to accurately identify patients who are likely to benefit, they should be used. However these are often not available before a Phase III study. The Adaptive Signature Design is an interesting approach that addresses this situation. In order to lay a framework for potential parametric extensions to this design, parametric classification methods are first reviewed, with focus on distances specific to each classification method. Modification of these methods for treatment subset prediction is then discussed.