Estimating the predictive quality of dose-response after model selection.

Abstract

Prediction of dose-response is important in dose selection in drug development. As the true dose-response shape is generally unknown, model selection is frequently used, and predictions based on the final selected model. Correctly assessing the quality of the predictions requires accounting for the uncertainties caused by the model selection process, which has been difficult. Recently, a new approach called data perturbation has emerged. It allows important predictive characteristics be computed while taking model selection into consideration. We study, through simulation, the performance of data perturbation in estimating standard error of parameter estimates and prediction errors. Data perturbation was found to give excellent prediction error estimates, although at times large Monte Carlo sizes were needed to obtain good standard error estimates. Overall, it is a useful tool to characterize uncertainties in dose-response predictions, with the potential of allowing more accurate dose selection in drug development. We also look at the influence of model selection on estimation bias. This leads to insights into candidate model choices that enable good dose-response prediction.