There has been considerable recent interest in pharmaceutical R&D
in the use of flexible, non-monotonic dose-response models. Two
examples of this are the ASTIN study (Krams et al, STROKE,
2003;34:2543-2548) and a proof-of-concept study in neuropathic pain
(Smith et al, PHARMACEUTICAL STATISTICS, 2006;5:39-50) both of which
used a flexible dose-response function based on the Normal Dynamic
Linear Model (NDLM), a model whose origins are in time series. This
model is potentially very important since the work of the Pharma
Working Group on Adaptive Dose-ranging Designs White paper of the PhRMA
PISC working group on adaptivedose-ranging designs. (Bornkamp et al,
J. BIOPHARMACEUTICAL STATISTICS, 2007;17:965-995).indicated that a
Bayesian adaptive design utilising the model outperformed alternative
approaches. There are, however, other possibilities including splines
and kernel regression. Up to now the use of this type of model has
required tailor made programs of suites of programs. In this talk I
show how the different approaches are related to one another and how
there are particular advantages in using one form over another and how
recent research
opens up different ways of fitting these models.