Clinical trials play a fundamental role in establishing the efficacy and safety of medical interventions. However, their real-world informative power depends on several parameters like the number of participants, trial length, uncontrolled variables, and controlled exposure. For instance, an incomplete Statistical Analysis Plan (SAP) may indicate a high-risk trial. If we consider informativeness as the ability of a trial to influence clinical, policy, or research decisions, how can we better evaluate this criterion?
A recent study by Hutchinson et al examined this issue by considering randomized interventional clinical trials in ischemic heart disease, diabetes mellitus, and lung cancer. Surprisingly, only about a quarter of the trials met four established conditions for informativeness!
Transcending this to the Tuberculosis (TB) domain, this raises a concern about the informativeness of TB clinical trials. With Tuberculosis, a disease with wide variance in manifestation and huge treatment cost implications, achieving high informativeness in a TB trial is challenging yet crucial.
To tackle this, Fast Data Science employs machine learning through their Clinical Trial Risk Tool, which can predict the informativeness of a clinical trial right from the protocol text. The tool is trained using past informative and non-informative trials, using the protocol contents to identify key parameters that indicate trial informativeness.
By doing this, the AI tool can offer calculated predictions about trial informativeness to guide research strategy and optimize research costs. Whether a TB trial’s protocol contains a complete SAP, or whether it considers uncontrolled variables such as environmental or genetic factors, the machine learning algorithm scrutinizes these aspects to forecast the potential success of the trial in influencing TB treatment and policy.
Such applications of machine learning in clinical trial design and evaluation demonstrate that AI tools like Fast Data Science’s Clinical Trial Risk Tool could be a transforming step towards determining trial informativeness, thus shaping the future of medical research, especially for complex diseases like Tuberculosis.
