A Robust Bayesian Design Criterion for Nonlinear Models

Abstract

Nonlinear models pervade the statistical literature on drug development, and specifically in pharmacokinetics (PK), pharmacodynamics (PD), and the biological and physical sciences in general. Obtaining efficient experimental designs for such models is non-trivial due to the well-documented parametersensitivity problem. Bayesian methods, which integrate prior information about the model parameters into the design process, have been proposed as a solution to the problem. In implementing such methods, the assumption is made that a single prior distribution exists for the parameters which may not be the case. In this research, we discuss situations in which there may be multiple (or competing) prior distributions and propose a robust design criterion for obtaining efficient designs in such cases.