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EnForeSys

Precision Forecasting to Conquer the Uncertainties of Enrollment

Forecast Enrollment Reliably

EnForeSys is a user-friendly decision tool that leverages simulation methods to predict recruitment milestones with high accuracy. Armed with a reliable probability of success, you can rest assured that your trial will reach its targeted enrollment on time and on budget.

EnForeSys puts you in control of the many factors affecting enrollment. EnForeSys incorporates realistic trial assumptions, based on historical data, into your predictions. You can make data-driven decisions about recruitment, quantifying your confidence in various enrollment strategies with high accuracy. 

Multi-Cohort Enrollment Forecasting functionality coming soon. Contact us to learn more!

 

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Benefits

Data Driven Forecasts

Using historical site-level data and other elements known to affect enrollment, you can make accurate predictions of your enrollment milestone.

Improved Trial Predictability and Success Rate

Use simulations to calculate a numerical probability of success for all of your strategies.

Reduced Planning and Reporting Time

Explain enrollment strategy to clinical teams with analytics that are easy to communicate.

Enhanced  Strategic Insight

Choose the best strategy given your resources, flag potential challenges, and pave the way to a successful trial.

Guaranteed Feasibility

Assess whether feasibility projections are realistic using simulation.

Reliably Assess Feasibility

Leverage thousands of simulations that factor in real-world assumptions and uncertainty to understand the probability of achieving milestones.

Resources

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Monte Carlo Simulations for Patient Recruitment

Download your copy to learn more about the advantages of model-based enrollment forecasting.
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The Model-Based Approach: A Better Way to Forecast Enrollment

Learn about model-based approaches to Phase 3 clinical trial development and the benefits for patient enrollment.
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How to Overcome Common Challenges to Patient Recruitment Projections

A simulations-driven and model-based approach to tackle one of the key causes of clinical trial discontinuity: the..
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