Penalized bayesian optimal designs for nonlinear models of Continuous Response (2019)
Experimental design is an important phase in both scienti_c and industrial research. In recent years, Bayesian optimal designs have become more and more popular, particularly in biomedical research and clinical trials. The Bayesian experimental design approach allows the prior information of unknown parameters to be incorporated into the design process in order to achieve a better design. The Bayesian optimal design theory can, however, produce inadequate designs from a practical perspective that conict with common practice in laboratories or other guidelines established. In this research, the penalized optimal design strategy with the Bayesian approach is suggested to reduce problems associated with the inadequacy of experimental designs from a practical perspective. New optimality criteria, which combine the use of desirability functions and the Bayesian approach, are constructed for linear and nonlinear regression models. The proposed technique based on the use of desirability functions helps to obtain optimal designs that ful_ll Bayesian optimal design criteria and also satisfy practical preferences. The proposed penalized strategy is illustrated with corresponding examples for both linear and nonlinear models. Furthermore, the methodology of choosing the appropriate desirability functions according to the practical design preferences is proposed and illustrated by an example of the Michaelis-Menten model.