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Given progressive developments and demands on clinical trials, accurateenrollment timeline forecasting is increasingly crucial for both strategicdecision-making and trial execution excellence. Naive approach assumes flatrates on enrollment using average of historical data, while traditionalstatistical approach applies simple Poisson-Gamma model using timeinvariantrates for site activation and subject recruitment. Both of them are lack ofnontrivial factors such as time and location. We propose a novel two-segmentstatistical approach based on Quasi-Poisson regression for subject accrual rateand Poisson process for subject enrollment and site activation. The inputstudy-level data is publicly accessible and it can be integrated withhistorical study data from user's organization to prospectively predictenrollment timeline. The new framework is neat and accurate compared topreceding works. We validate the performance of our proposed enrollment modeland compare the results with other frameworks on 7 curated studies.

Enrollment Forecast for Clinical Trials at the Planning Phase with Study-Level Historical Data