Optimal designs for dose response curves with common parameters
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 -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.
Cite
@article{arxiv.1603.04500,
title = {Optimal designs for dose response curves with common parameters},
author = {Chrystel Feller and Kirsten Schorning and Holger Dette and Georgina Bermann and Björn Bornkamp},
journal= {arXiv preprint arXiv:1603.04500},
year = {2016}
}
Comments
Keywords and Phrases: Nonlinear regression, different treatment groups, $D$-optimal design, models with common parameters, admissible design, Bayesian optimal design AMS Subject Classification: Primary 62K05; Secondary 62F03