  Identifying subgroups of patients with an enhanced response to a new
treatment has become an area of increased interest in the last few years. When
there is knowledge about possible subpopulations with an enhanced treatment
effect before the start of a trial it might be beneficial to set up a testing
strategy, which tests for a significant treatment effect not only in the full
population, but also in these prespecified subpopulations. In this paper we
present a parametric multiple testing approach for tests in multiple
populations for dose-finding trials. Our approach is based on the MCP-Mod
methodology, which uses multiple comparison procedures to test for a
dose-response signal, while considering multiple possible candidate
dose-response shapes. Our proposed methods allow for heteroscedasticity between
populations and control the FWER over tests in multiple populations and for
multiple candidate models. We show in simulations, that the proposed
multi-population testing approaches can increase the power to detect a
significant dose-response signal over the standard single-population MCP-Mod,
when the considered subpopulation has an enhanced treatment effect.
