Abstract

Many clinical pharmacology studies require repeated measurements to be taken on each patient and analysis of the data are conducted within the framework of nonlinear mixed effects models. It is increasingly common to design these studies using information theoretic principles due to the need for parsimony because of the presence of many logistical and ethical constraints. D-optimal design methods are often used to identify the best possible study conditions, such as the dose and number and timing of blood sample collection. However, the optimal times for collecting blood samples may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility while preserving efficient parameter estimation. Due to the complexity of nonlinear mixed effects models there is generally no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method reaches the stationary distribution rapidly and provides time-sensitive windows around the optimal design points. The proposed method is applicable to determine windows around any continuous design variable for which repeated measures per run are required. This has particular importance for clinical pharmacology studies.