Synopses & Reviews
This book examines the theory of optimal model-based design and provides examples of optimal designs for various models, mostly related to biopharmaceutical applications, such as dose-response studies. The authors pay special attention to adaptive or sequential optimal designs for nonlinear regression models when estimation and optimal design are performed in stages. Ideal for researchers in regression analysis and experimental design, the text illustrates optimal designs for various models using an example of the application of a first-order optimization algorithm in the space of information matrices, which is implemented in both MATLABA(R) and SAS.
Optimal Design for Nonlinear Response Models discusses the theory and applications of model-based experimental design with a strong emphasis on biopharmaceutical studies. The book draws on the authors many years of experience in academia and the pharmaceutical industry.
While the focus is on nonlinear models, the book begins with an explanation of the key ideas, using linear models as examples. Applying the linearization in the parameter space, it then covers nonlinear models and locally optimal designs as well as minimax, optimal on average, and Bayesian designs. The authors also discuss adaptive designs, focusing on procedures with non-informative stopping.
The common goals of experimental design such as reducing costs, supporting efficient decision making, and gaining maximum information under various constraints are often the same across diverse applied areas. Ethical and regulatory aspects play a much more prominent role in biological, medical, and pharmaceutical research. The authors address all of these issues through many examples in the book.