In the search for effective treatments and vaccines for COVID-19, clinical trials play a crucial role. However, not all trials are created equal. Some have the potential to guide critical clinical, policy, or research decisions, making them highly informative. Other trials might not provide such valuable insights. Understanding how informative a trial might be before it’s conducted can save valuable resources and time.
At the heart of a clinical trial’s informativeness are several key factors such as the number of participants, length of trial, number of uncontrolled variables, and the ability to effectively control exposure. Additionally, the presence of a comprehensive Statistical Analysis Plan (SAP) is often a strong indicator of a low-risk, high-quality trial.
But how can we estimate the informativeness of a trial before it takes place? Fast Data Science’s Clinical Trial Risk Tool provides the solution. Utilizing the power of machine learning, the Clinical Trial Risk Tool analyzes the text in the trial protocol to predict its informativeness.
The tool works by automatically analyzing scores of historical trial protocols and their outcomes. By comparing informative trials with less helpful ones, the machine learning model learns to identify patterns and key features that predict a trial’s potential value.
For example, it might learn from the data that trials with a larger participant pool have historically led to more informative results. Or it might identify patterns in the ways the trial controls for variables that point to a higher chance of useful outcomes. These algorithmically-derived insights are then used to predict the informative potential of new trial protocols.
In the face of the ongoing pandemic, tools like this have been instrumental in prioritizing high-potential trials, preventing the duplication of effort and ensuring scarce resources are deployed where they can make the most difference.
Fast Data Science’s Clinical Trial Risk Tool thus serves as a critical part of the armoury in the ongoing fight against COVID-19, underlining the immense potential of machine learning in transforming the world of clinical research and beyond.
