Optimal designs for dose response curves with common parameters
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Date
2016
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Abstract
A common problem in Phase II clinical trials is the comparison of dose response curves
corresponding to different treatment groups. If the effect of the dose level is described by
parametric regression models and the treatments differ in the administration frequency (but
not in the sort of drug) a reasonable assumption is that the regression models for the different
treatments share common parameters.
This paper develops optimal design theory for the comparison of different regression models
with common parameters. We derive upper bounds on the number of support points of
admissible designs, and explicit expressions for D-optimal designs are derived for frequently
used dose response models with a common location parameter. If the location and scale
parameter in the different models coincide, minimally supported designs are determined and
sufficient conditions for their optimality in the class of all designs derived. The results are
illustrated in a dose-finding study comparing monthly and weekly administration.
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Keywords
nonlinear regression, Bayesian optimal design, admissible design, models with common parameters, D-optimal design, different treatment groups