The drive to develop effective and universally-applicable influenza vaccines has encouraged many organisations to conduct clinical trials. However there is a considerable risk of trials ending without delivering informative results.
Clinical trials that end uninformatively don’t provide valuable insights to guide clinical, policy, or research decisions. This is concerning, as research by Hutchinson et al delivered alarming statistics – only 26.4% of the 125 clinical trials they studied met the conditions for informativeness. This unfortunate statistic reveals the need to estimate and manage the risk of failure in influenza clinical trials better.
We are using the concept of an “informative” clinical trial proposed by Zarin et al in Harms From Uninformative Clinical Trials, JAMA 2019. You can read more about how the Clinical Trial Risk Tool quantifies the risk of a trial ending without delivering informative results in our article in Clinical Leader.
Enter Fast Data Science’s Clinical Trial Risk Tool.
Fast Data Science’s Clinical Trial Risk Tool processes your clinical trial protocol and identifies and quantifies design issues that could lead to uninformative outcomes in influenza clinical trials. With this tool we can reduce the risk of ending uninformatively.
Key factors such as the number of participants, the length of the trial, the number of uncontrolled variables, and the ability to control exposure contribute heavily to a trial’s failure to deliver informative results. A key cause of uninformative trials is underpowering. These and many more factors are what the machine learning models of Fast Data Science’s Risk Tool consider when making predictions.
For instance, the tool can flag a trial as high risk if its protocol is missing a completed Statistical Analysis Plan (SAP), which can indicate a higher potential for failure.
Fast Data Science’s Clinical Trial Risk Tool is a significant step forward in our ability to manage influenza clinical trials. Through leveraging machine learning, we can better predict and manage the risk of uninformative outcomes, thereby enhancing the efficiency and success rate of these critical trials.
