Speaker
Description
Optimal designs maximize the experimental efficiency and precision, but are sometimes difficult to obtain, especially in cases with non-trivial underlying model functions. A possible application area providing the motivating example is toxicology. Liver carcinoma cells are modelled as a function of valproic acid (VPA) concentration using the common four-parameter log-logistic (4PLL) model. To prevent modelling implausible negative values, the model function is split into two segments: the standard 4PLL segment and a constant zero segment in parts modelled negatively. A non-linear continuous segmented regression model with unknown segment borders is at hand, for which it is difficult to define optimal designs. I derived and analyzed Bayesian A- and D-optimal segmented designs, along with a newly proposed A-mixed criterion to obtain a more accurate estimation of the parameter that defines the location of the segment border.
Compared to conventional A- and D-optimal designs, that ignore the segmented model structure, segmented designs improve precision, as demonstrated in the VPA application and in various simulation studies based on the truncated 4PLL model. However, segmented designs may lack robustness under misspecified priors due to the model’s truncated structure. Therefore, a sequential approach is recommended: if data from a non-segmented design suggests a truncated 4PLL structure, combining it with a segmented design yields more stable and precise results.