When it comes to running a clinical trial, costs can be a major concern. The cost of a trial depends on numerous factors, including the type of disease, the methods used to conduct the trial, and the location of the trial. For diseases like polio, which has been a global health concern for many decades, understanding the potential cost of clinical trials is crucial in planning and budgeting.
According to a 2017 study by Martin, Hutchens and Hawkins, much of these costs come from factors such as the protocol design, the number of subjects, sites, and visits. Also, the type of intervention planned can significantly affect the cost. For instance, gene and cell therapy are usually more expensive. According this study, the average cost of a phase 3 clinical trial is estimated to be around $30 million.
To help better predict these costs, Fast Data Science has developed a Clinical Trial Risk Tool. This tool employs machine learning algorithms to predict the cost of a clinical trial from the protocol text. But how does this work?
The tool assesses the protocol text of the clinical trial, which includes detailed descriptions of the study’s design, objectives, methodology, statistical considerations, and organization. By analyzing this data, the machine learning model, informed by a large dataset of historical trials, can identify patterns and relationships between specific features of a trial and its associated costs.
For example, the model may learn that trials utilizing certain types of interventions, or trials held in certain regions, typically involve higher costs. This allows it to provide estimates for new trials based on their respective protocols.
Despite the strides made in clinical trials for polio, the costs continue to climb, making it necessary for organizations to better understand and predict these expenses. Fast Data Science’s Clinical Trial Risk Tool is a step in that direction, providing a valuable resource for those planning and budgeting for these vital studies.
While it cannot eliminate the costs of running a clinical trial for a disease like polio, it can help to anticipate them more accurately, leading to more efficient allocation of resources and potentially saving time in the fight against this debilitating disease.
