  Given progressive developments and demands on clinical trials, accurate
enrollment timeline forecasting is increasingly crucial for both strategic
decision-making and trial execution excellence. Naive approach assumes flat
rates on enrollment using average of historical data, while traditional
statistical approach applies simple Poisson-Gamma model using timeinvariant
rates for site activation and subject recruitment. Both of them are lack of
nontrivial factors such as time and location. We propose a novel two-segment
statistical approach based on Quasi-Poisson regression for subject accrual rate
and Poisson process for subject enrollment and site activation. The input
study-level data is publicly accessible and it can be integrated with
historical study data from user's organization to prospectively predict
enrollment timeline. The new framework is neat and accurate compared to
preceding works. We validate the performance of our proposed enrollment model
and compare the results with other frameworks on 7 curated studies.

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