Optimal designs for estimating the interesting part of a dose-effect curve
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Date
2007-07-13T12:15:30Z
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Abstract
We consider a dose-finding trial in phase IIB of drug development. For choosing
an appropriate design for this trial the specification of two points is critical: an
appropriate model for describing the dose-effect relationship and the specification
of the aims of the trial (objectives), which will be the focus in the present paper.
For many practical situations it is essential to have a robust trial objective that
has little risk of changing during the complete trial due to external information.
An important and realistic objective of a dose-finding trial is to obtain precise
information about the interesting part of the dose-effect curve. We reflect this
goal in a statistical optimality criterion and derive efficient designs using optimal
design theory. In particular we determine non-adaptive Bayesian optimal designs,
i.e. designs which are not changed by information obtained from an interim
analysis. Compared with a traditional balanced design for this trial it is shown
that the optimal design is substantially more efficient. This implies either a gain
in information or essential savings in sample size. Further, we investigate an
adaptive Bayesian optimal design that uses two different optimal designs before
and after an interim analysis, and we compare the adaptive with the non-adaptive
Bayesian optimal design. The basic concept is illustrated using a modification of
a recent AstraZeneca trial.
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Keywords
adaptive design, Bayesian design, clinical trial, dose-finding, interesting part of dose-effect curve, optimal design, prior knowledge, two-stage design