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Study Feasibility is a predictive analytic solution that combines Citeline's expertly curated, indexed, and enriched clinical data sets with machine learning (ML) algorithms to predict, model, and optimize site allocation, site scoring, and enrollment duration at the trial, country, and site levels.
The highly intelligent ML engine in Study Feasibility also predicts an overall probability of enrollment success for each trial. These predictions guide and support decisions around which countries to enter for a clinical trial, how many sites are required, and which sites are most recommended based on a given study protocol.
Study Feasibility forms part of Citeline Predict, a scalable, cloud-based clinical analytics and insights platform that generates on-demand insights and predictions, to accelerate clinical development and minimize study risk.
Increase speed to first patient in with data-driven analysis
Select countries and sites that maximize enrollment potential
Model, compare, and optimize feasibility scenarios faster
Make better decisions and know why they are better
The core machine learning engine in Study Feasibility is based on gradient boosted decision trees that are trained on data and proprietary engineered features.
In a single platform, feasibility scenarios can be instantly modeled, optimized, compared, and shared with explain-ability and transparency. Users can see what trial design elements are having a positive or negative impact on predictions and refine their plans accordingly.
Predictive insights include:
Take your feasibility analyses one stage further and maximize the predictive capabilities in Study Feasibility by incorporating your data assets into our ML models.
Combined data sets refine the model predictions to produce the best possible results and deliver further enhanced, bespoke insight specific to your clinical trials.
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