Real time monitoring and prediction of time to endpoint maturation in clinical trials

• Published in: Statistics in medicine Vol. 41; no. 18; pp. 3596 - 3611
• Main Authors: Wang, Li, Liu, Yang, Chen, Xiaotian, Pulkstenis, Erik
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 15.08.2022
• Subjects: Bayesian; Clinical trials; enrollment; Poisson process; time to event; time to event; Poisson process; Bayesian; enrollment
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.9436

In clinical trials, almost all key milestone dates can be defined in terms of time to endpoint maturation (TTEM). The real time monitoring and accurate prediction of TTEM have a significant impact on clinical trial planning and execution and can bring significant value to clinical trial practitioners. TTEM is defined as the time to achieve or observe a certain number or percentage of some endpoint of interest. It is a combination of time to site initiation, time to subject enrollment after site initiation and time to event of interest after subject enrollment. To better predict TTEM during the trial, the future site initiation and subject enrollment have to be taken into account while predicting the number of events. In this article, we propose a novel simulation‐based framework combining time to site initiation, time to subject enrollment and time to event in order to predict TTEM. A nonhomogeneous Poisson process with a quadratic time‐varying rate function is used to model site initiation and subject enrollment and more advanced time to event models had been explored and integrated on top of them, such as Weibull, piecewise exponential, and model averaging which is equivalent to a Bayesian model selection strategy. To evaluate the predictive performance of the proposed methodology, we conducted extensive simulations and applied the methodology to 14 randomly selected real oncology phase 2 and phase 3 studies in both solid tumor and hematology with a total 31 study‐endpoint combinations. The predictive performance of the proposed methodology was then compared with popular and commonly available commercial software, for example, East (Cytel, Cambridge, MA, USA). From both simulation and real data, the proposed methodology can significantly improve the prediction accuracy by up to 54% compared to the commonly available method.