When: June 23, 1-2 pm
Where: Department of Statistics, Albano, house 4, level 6. The door to the department is locked. Please send an e-mail to dan.hedlin@stat.su.se if you want to attend.

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.

We develop optimal design theory for the comparison of different regression models with common parameters. We derive upper bounds of the number of support points of admissible designs. We derive also explicit expressions for locally D-optimal designs 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 are derived.

Since the locally D-optimal designs depend on the unknown parameters, we will also derive Bayesian D-optimal designs that are more robust.

Finally, the results will be illustrated in a dose-finding study comparing monthly and weekly administration.