2016/01/05 by Holger Dette, Dette, Holger, Katrin Kettelhake +7
Biochemistry, Genetics and Molecular Biology · Decision Sciences · Mathematics · #FOS: Computer and information sciences #Methodology (stat.ME) #Optimal Experimental Design Methods #Statistical Methods in Clinical Trials #Viral Infectious Diseases and Gene Expression in Insects
paper · pdf · doi:10.48550/arxiv.1601.00797
openalex publication_date 2016/01/05 · openalex created_date 2022/10/04 · openalex updated_date 2026/08/01
Nonlinear regression models addressing both efficacy and toxicity outcomes\nare increasingly used in dose-finding trials, such as in pharmaceutical drug\ndevelopment. However, research on related experimental design problems for\ncorresponding active controlled trials is still scarce. In this paper we derive\noptimal designs to estimate efficacy and toxicity in an active controlled\nclinical dose finding trial when the bivariate continuous outcomes are modeled\neither by polynomials up to degree 2, the Michaelis- Menten model, the Emax\nmodel, or a combination thereof. We determine upper bounds on the number of\ndifferent doses levels required for the optimal design and provide conditions\nunder which the boundary points of the design space are included in the optimal\ndesign. We also provide an analytical description of the minimally supported\nD-optimal designs and show that they do not depend on the correlation between\nthe bivariate outcomes. We illustrate the proposed methods with numerical\nexamples and demonstrate the advantages of the D-optimal design for a trial,\nwhich has recently been considered in the literature.\n