Optimal designs for dose response curves with common parameters
| Autor: | Chrystel Feller, Holger Dette, Kirsten Schorning, Björn Bornkamp, Georgina Bermann |
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| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Optimal design admissible design Location parameter Loewner ordering Mathematics - Statistics Theory Statistics Theory (math.ST) Dose level 01 natural sciences Methodology (stat.ME) $D$-optimal design 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine 62K05 FOS: Mathematics sort Applied mathematics 62F03 030212 general & internal medicine 0101 mathematics D-optimal design Statistics - Methodology Mathematics Parametric statistics Bayesian optimal design Regression analysis different treatment groups nonlinear regression models with common parameters Statistics Probability and Uncertainty Scale parameter Nonlinear regression |
| Zdroj: | Ann. Statist. 45, no. 5 (2017), 2102-2132 |
| ISSN: | 0090-5364 |
| DOI: | 10.1214/16-aos1520 |
| Popis: | 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. Comment: 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 |
| Databáze: | OpenAIRE |
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