To accelerate time to first patient in and help clinical teams reduce the chance of investing in under- or non-enrolling sites, Informa Pharma Intelligence created Citeline Study Feasibility.
Citeline Study Feasibility is a predictive analytic solution that combines Informa'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 Citeline 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.
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Citeline Study Feasibility
About the ML models in Study Feasibility
The ML engine in Citeline Study Feasibility is based on gradient boosted decision trees that are trained on Informa’s data and proprietary engineered features. Predictions are made stable by reducing the impact of variability and local overfitting through multiple regression models.
Clinical trials are notoriously subject to external factors that lead to variable enrollment rates. Designing a trial in such a way that minimizes the degree of variability is equally as important as targeting an overall reduction in cycle times - something that Citeline Study Feasibility makes great strides toward.
Continue reading to learn how you can maximize the predictive power of Citeline Study Feasibility by feeding your data into its ML engine, alongside Informa's data.