DT-Optimal Designs for Probit Models in Clinical Trials

Abstract

The Optimal designs used in a clinical trial depends on the goals of the study. Common goals are estimating model parameters and choosing between models. D-optimal designs are used when the goal is to estimate the model parameters. This is achieved by maximizing the determinant of the information matrix. When the goal is model discrimination, T-optimal designs are used. The design is optimal when the minimum difference between the models is maximized. Generally, D-optimal designs are not efficient when the goal is model discrimination and T-optimal designs perform poorly when the goal is parameter estimation. However, because D-optimal and T-optimal designs have a common criterion structure, they can be combined into a new design called a DT-optimal design. DT-optimal designs provide a balance between parameter estimation and model discrimination. The efficiency of DT-optimal designs relative to D and T-optimal designs shows that they work for parameter estimation and model discrimination.