Recent advancements have undoubtedly enhanced the formats, regulations, and the overall process of clinical trials, such as those for Tuberculosis (TB). Notably, these innovations have also brought increasing complexity in the execution and monitoring of these trials. The question remains, however, are these complex trials necessary, and is there a way to predict this complexity before the trial begins?
In groundbreaking research by Markey et al, titled “Clinical Trials are Becoming More Complex: A Machine Learning Analysis of Data From Over 16,000 Trials”, significant features of these trials such as the number of endpoints, inclusion-exclusion criteria, and more were assessed. Gathering such features, they then crafted the ‘Trial Complexity Score’, connected to the overall duration of the clinical trial. This measure showed a considerable increase over time, confirming that clinical trials are indeed growing in complexity.
In TB clinical trials, complexity matters and burning out resources without meaningful payoff is a genuine risk. This is where Fast Data Science’s Clinical Trial Risk Tool can fill an essential gap. Using Machine Learning methodologies, it estimates the ‘Trial Complexity Score’ from text in the clinical trial protocol itself. Let’s quickly delve into how this works.
When a new trial for TB is in the planning phases, the protocol can be inputted into the Clinical Trial Risk Tool. This document, laid with complex medical methods and jargon, is then read by machine learning algorithms. These algorithms, having been trained on over 16,000 trials, assign weights - essentially importance indicators - to the trial features. This information is then combined into the ‘Trial Complexity Score’. The larger the score, the higher the complexity.
The Clinical Trial Risk Tool essentially equips researchers with a look into the future, providing estimates of resources and durations needed for the trial. Time and resources can then be allocated wisely, sponsors can have a reliable timeline to work with, and the most strategically efficient methods can be taken up to combat Tuberculosis, one trial at a time.
In conclusion, while clinical trials like those for Tuberculosis are becoming more complex, tools such as Fast Data Science’s Clinical Trial Risk Tool can help, offering a glimpse into their potential complexity before they start. With such tools, we can continue to strive further in the field of medical research, without drowning in complexity.
