Effect of uncertainty about population parameters on pharmacodynamics-based prediction of clinical trial power

Abstract

Clinical trial simulation (CTS) may be applied to predict power of intended drug trials on the basis of pharmacokinetic/pharmacodynamic (PKPD) drug models. The validity of such predictions will, among other factors, depend on the degree of uncertainty about population parameters entering the simulation. In the current article, we illustrate how population parameter uncertainty may be incorporated in the overall simulation model, using a worked example to demonstrate our approach. Moreover, we suggest an ANOVA-based method for sensitivity analysis, aimed at distinguishing important population parameters, required to be input in the model with a low degree of uncertainty for precise power predictions, from unimportant parameters, which may be entered with a high degree of uncertainty without affecting the reliability of predictions. Our results from simulations with different settings of hyperparameters and doses suggest that indices derived from this type of sensitivity analysis may be used for grading the influence on prediction quality of uncertainty about different population parameters and, thus, facilitate the allocation of resources expended for the preparation of a successful CTS project.