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Andy P. Grieve, Ph.D.
The context of our case study will be a
dose-response study in the treatment of acute stroke. Such studies are an
extremely important part of the drug development process as knowledge of the
relationship between response and dose is an essential requirement for making
informed decisions about dosage. One
potential difficulty in the use of a limited number of doses in a parallel
group design to investigate dose response is the danger that the steep part of
the dose-response curve may fall between two doses and little is learned. The
use of a large number of doses in order to circumvent this problem is potentially
wasteful in its use of patients because a large number of patients will either
be receiving doses which are little different from placebo or, at the other
extreme, receiving doses which have a greater potential for causing adverse side effects.
Ideally, the vast majority of patients should receive doses in the steepest
part of the dose-response function. The design decided upon was a sequential
Bayesian adaptive design in which knowledge of the dose-response curve was
updated on an on-going basis in order to inform two decisions. First, the dose that should be allocated to the next patient;
second whether the study should continue or should be stopped. There have been two major hindrances to the
use of Bayesian methods in pharmaceutical R&D, one of which is practical,
the other more philosophical. The
practical constraint has been the lack of availability of methods and software
for their implementation. The philosophical constraint has been a perceived
antipathy from regulators to the use of priors. We will discuss aspects of these issues in the
practical implementation of the proposed design. In particular we will consider:
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