A simple and robust model for enrollment projection in clinical trials

Enrollment projection in clinical trials is a topic gaining attention in the statistics literature in recent years. A number of methods have been proposed in this area. Some approaches are sophisticated but complicated to implement. We aim to implement a simple and robust empiric Bayes Poisson Gamma model (PGM) that is suitable for practical use. We assume a constant and site-specific underlying enrollment rate over time, which comes from a common Gamma distribution. Choice of prior parameters is data driven. We tested the proposed PGM in a simulation study as well as a number of oncology trials with various enrollment patterns, which yield satisfactory results. Compared to a flexible nonparametric model (Zhang and Long, 2010), the PGM is associated with a narrower credible interval as a result of parametric assumptions. However, the model prediction may be off when the assumptions are substantially violated.