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Modeling Enrollment with Random Staggered Site Start-Up Times (302954)

Keywords: Poisson process, Bayesian, enrollment, forecasting

With rising competition between pharmaceutical companies and increasingly strict drug regulations, running clinical trials efficiently and effectively is crucial for the drug-development process. Enrolling subjects is a vital and costly part of that process, and being able to accurately model and predict enrollment is of utmost importance. Many current enrollment models are deterministic and fail to account for various sources of variability in the enrollment process. This could lead to missed deadlines and unmet enrollment goals. We propose a predictive model in which subjects arrive at sites according to Poisson processes with site-specific enrollment rates assumed to be samples from a Gamma distribution. We also model the site start-up times (SSU) for each site using a uniform, beta, or gamma distribution. This allows for increased flexibility when planning a clinical trial. One benefit of our approach is that all derivations have closed-form derivations that improve upon Monte Carlo approximations.