  In a Phase II dose-finding study with a placebo control, a new drug with
several dose levels is compared with a placebo to test for the effectiveness of
the new drug. The main focus of such studies often lies in the characterization
of the dose-response relationship followed by the estimation of a target dose
that leads to a clinically relevant effect over the placebo. This target dose
is known as the minimum effective dose (MED) in a drug development study.
Several approaches exist that combine multiple comparison procedures with
modeling techniques to efficiently estimate the dose-response model and
thereafter select the target dose. Despite the flexibility of the existing
approaches, they cannot completely address the model uncertainty in the
model-selection step and may lead to target dose estimates that are biased. In
this article, we propose two new MED estimation approaches based on weighted
regression modeling that are robust against deviations from the dose-response
model assumptions. These approaches are compared with existing approaches with
regard to their accuracy in point and interval estimation of the MED. We
illustrate by a simulation study that by integrating one of the new dose
estimation approaches with the existing dose-response profile estimation
approaches one can take into account the uncertainty of the model selection
step.
