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Enrollment Modeling with Random Staggered Site Start-Up Times (302985)

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Keywords: clinical trials, enrollment, Poisson process, gamma

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 model and predict enrollment accurately 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 and start-up times. This approach has advantages over global predictive models in that, often, information is available at the site level and thus site-level assumptions can be incorporated into the model. We treat the enrollment rates and SSU times as possibly random quantities and provide nice closed-form calculations of enrollment moments that avoid Monte Carlo approximations. Our model also can perform re-projections using a straightforward Bayesian update of the enrollment parameters.