Dose/exposure-response modeling in dose titration trials: Overcoming the titration paradox

Response-based dose individualization or dose titration is a powerful approach to achieve precision dosing. Yet, titration as an individualization strategy is underused in drug development and therefore not reflected in labeling, possibly partly because of the data analysis challenges associated with assessing dose/exposure-response under dose titration, where there is an inherent risk of selection bias because poor responders would get high doses, whereas good responders would get low doses. In a recent article, this issue of selection bias was termed the "titration paradox." In this study, we demonstrate by means of simulation that the titration paradox may be overcome if longitudinal data from dose titration trials is analyzed using a population approach that accounts for the fact that dose/exposure-response relationships differ between individuals. We show that with an appropriate sample size and missing data missing at random, stepwise dose/exposure-response modeling based on data obtained under dose titration is not by definition subject to model selection bias or bias in parameter estimates. We also illustrate the challenges of graphical exploration of data obtained under dose titration and discuss the use of model diagnostic tools with such data. Our study shows that if, at every timepoint in the course of a trial, there is a clear causal relationship between the response and the dose/exposure level, and a population approach is used, it will in many cases be possible to develop, estimate, and appropriately qualify a dose/exposure-response model also for data obtained under dose titration, thus overcoming the titration paradox.