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Accurate forecast of a clinical trial enrollment timeline at the planningphase is of great importance to both corporate strategic planning and trialoperational excellence. While predictions of key milestones such as lastsubject first dose date can inform strategic decision-making, detailedpredictive insights (e.g., median number of enrolled subjects by month for acountry) can facilitate the planning of clinical trial operation activities andpromote execution excellence. The naive approach often calculates an averageenrollment rate from historical data and generates an inaccurate predictionbased on a linear trend with the average rate. The traditional statisticalapproach utilizes the simple Poisson-Gamma model that assumes time-invariantsite activation rates and it can fail to capture the underlying nonlinearpatterns (e.g., up and down site activation pattern). We present a novelstatistical approach based on generalized linear mixed-effects models and theuse of non-homogeneous Poisson processes through Bayesian framework to modelthe country initiation, site activation and subject enrollment sequentially ina systematic fashion. We validate the performance of our proposed enrollmentmodeling framework based on a set of preselected 25 studies from fourtherapeutic areas. Our modeling framework shows a substantial improvement inprediction accuracy in comparison to the traditional statistical approach.Furthermore, we show that our modeling and simulation approach calibrates thedata variability appropriately and gives correct coverage rates for predictionintervals of various nominal levels. Finally, we demonstrate the use of ourapproach to generate the predicted enrollment curves through time withconfidence bands overlaid.

Enrollment Forecast for Clinical Trials at the Portfolio Planning Phase Based on Site-Level Historical Data