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A common problem in Phase II clinical trials is the comparison of doseresponse curves corresponding to different treatment groups. If the effect ofthe dose level is described by parametric regression models and the treatmentsdiffer in the administration frequency (but not in the sort of drug) areasonable assumption is that the regression models for the differenttreatments share common parameters. This paper develops optimal design theoryfor the comparison of different regression models with common parameters. Wederive upper bounds on the number of support points of admissible designs, andexplicit expressions for $D$-optimal designs are derived for frequently useddose response models with a common location parameter. If the location andscale parameter in the different models coincide, minimally supported designsare determined and sufficient conditions for their optimality in the class ofall designs derived. The results are illustrated in a dose-finding studycomparing monthly and weekly administration.

Optimal designs for dose response curves with common parameters