Modeling time-varying recruitment rates in multicenter clinical trials

Abstract

Multicenter phase II/III clinical trials are large-scale operations that often include hundreds of recruiting centers in several countries. Therefore, the operational aspects of a trial must be thoroughly planned and closely monitored to ensure better oversight and study conduct. Predicting patient recruitment plays a pivotal role in trial monitoring as it informs how many people are expected to be recruited on a given day. As such, study teams may rely on predictions to assess progress and detect any deviations from the original plan that might put the study and potentially, patients at risk. Recruitment predictions are often based on a Poisson-Gamma model that assumes that centers have a constant recruitment rate throughout the trial. The model has useful features and has extensively been used, yet its main assumption is often unrealistic. A nonhomogeneous Poisson process has been recently proposed that can incorporate time-varying recruitment rates. In this work, we predict patient recruitment using both approaches and assess the impact of said assumption in a real-world setting where studies may not necessarily have constant center-specific recruitment rates. The paper showcases experience from modeling recruitment in trials sponsored by AstraZeneca between 2005 and 2018. In these data, there is evidence of time-varying recruitment rates. The predictive performance of models that account for both constant and time-varying recruitment effects is presented. Following a descriptive analysis of data, we assess model performance and investigate the impact of model misspecification.