Bayesian optimal designs for dose-response curves with common parameters
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Date
2017
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Abstract
The issue of determining not only an adequate dose but also a dosing frequency
of a drug arises frequently in Phase II clinical trials. This results in the comparison
of models which have some parameters in common. Planning such studies based on
Bayesian optimal designs offers robustness to our conclusions since these designs,
unlike locally optimal designs, are efficient even if the parameters are misspecified.
In this paper we develop approximate design theory for Bayesian D-optimality for
nonlinear regression models with common parameters and investigate the cases of
common location or common location and scale parameters separately. Analytical
characterisations of saturated Bayesian D-optimal designs are derived for frequently
used dose-response models and the advantages of our results are illustrated via a
numerical investigation.