Bayesian optimal designs for a quantal dose-response study with potentially missing observations

In a dose-response study, there are frequently multiple goals and not all planned observations are realized at the end of the study. Subjects drop out and the initial design can be quite different from the final design. Consequently, the final design can be inefficient. Single- and multiple-objective Bayesian optimal designs that account for potentially missing observations in quantal response models were recently proposed in Baek (2005). In this work, we investigate the efficiencies of the conventional optimal designs that do not incorporate potential missing information relative to our proposed designs. Furthermore, we examine the impact of restricted dose range on the resulting optimal designs. As an application, we used missing data information from a study by Yocum et al. (2003) to design a study for estimating dose levels of tacrolimus that will result in a certain percentage of rheumatoid arthritis patients having an ACR20 response at 6 months.