Clinical trial feasibility is a fundamental contributor to the overall success of a development program. However, selecting the right countries, sites and investigators and starting up as quickly as possible remains a challenge, leading to greatly variable performance and inaccurate predictions for important trial milestones.
Current approaches can best be described as a blend of art and science, requiring time-intensive data analysis, deep contextual understanding, and a healthy slice of good fortune to meet enrollment targets.
Citeline Study Feasibility is a predictive analytic solution that instantly delivers insights to improve clinical trial decisions and cycle times, by combining Informa Pharma Intelligence's expertly curated, indexed, and enriched clinical data sets with machine learning algorithms. The highly intelligent machine learning engine in Citeline Study Feasibility dynamically forecasts enrollment predictions at the site, country, and overall trial level so users can:
Plan for optimal trial participation
Accelerate speed to first patient in
Reduce the chance of investing in non-performing sites
The platform employs a combination of human and artificial intelligence to deliver predictive insights across three key aspects of feasibility – site allocation, site scoring, and enrollment duration, including probability of enrollment success. These 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.
Considering the model’s computational power, these predictive analytics can all be achieved at considerable speed and scale, enabling reduction in the time between protocol finalization, site selection, first site initiation, and first patient in.
In addition, by generating visual analyses of these feasibility scenarios, Citeline Study Feasibility makes it easy to understand what elements of a trial design are having a positive or negative impact on enrollment durations, so trial operators can compare various scenarios and share with colleagues to collaborate.
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Citeline Study Feasibility