  In this paper we consider two-stage adaptive dose-response study designs,
where the study design is changed at an interim analysis based on the
information collected so far. In a simulation study, two approaches will be
compared for these type of designs; (i) updating the study design by
calculating the maximum likelihood estimate for the dose-response model
parameters and then calculating the design for the second stage that is locally
optimal for this estimate, and (ii) using the complete posterior distribution
of the model parameter at interim to calculate a Bayesian optimal design (i.e.
taking into account parameter uncertainty). In particular, for an early interim
analysis respecting parameter uncertainty seems more adequate, on the other
hand for a Bayesian approach dependency on the prior is expected and an
adequately thought-through prior is required. A computationally efficient
method is proposed for calculating the Bayesian design at interim based on
approximating the full posterior sample using k-means clustering. The sigmoid
Emax dose-response model and the D-optimality criterion will be used in this
paper.
