I am pleased to announce the Clinical Trial Risk Tool, which is now open to the public to use.
The tool is available at https://clinicaltrialrisk.org/tool.
Screenshot of the tool
The tool consists of a web interface where a user can upload a protocol in PDF or Word format, and ultimately a number of features were extracted, such as number of subjects, statistical analysis plan, effect size, number of countries, etc.
The tool which can estimate the risk of HIV and TB trials ending uninformatively and will soon be extended to cover other metrics such as trial complexity and cost.
The NLP model was developed as an ensemble of components which extracted different aspects of information from the text, including rule-based (hand-coded) and neural network designs.
The model’s output features were then condensed down via a clinical trials risk model which ultimately produces a three-level risk traffic light score. The full analysis can be exported as XLSX or PDF.
Creating clinical trial budgets from protocols Creating a clinical trial budget is a fiddly and time consuming process. The playbook for running the clinical trial is a document called the protocol. You can find examples of protocols here. The protocol states how many participants will take part in the trial and also what visits and procedures will take place. Above: a protocol. Source: NCT04128579 A clinical trial manager must read the protocol and look for all pieces of information in the protocol that is relevant to the budget, in particular the Schedule of Events (also called Schedule of Assessments or Schedule of Activities), which is a table or series of tables which indicate which procedures and assessments will take place on which the visits.
We have improved the Clinical Trial Risk Tool in the last 6 months, making it more user friendly and taking on board the feedback that we’ve received. We’ve improved the accuracy of the machine learning components too. The tool now outputs its key figures such as risk levels and estimated cost in easily readable cards, so you can see at a glance the key takeaways from your protocol: The risk factors are now organised into collapsible categories, so you can explore them easily without an information overload.
Guest post by Safeer Khan, Lecturer at Department of Pharmaceutical Sciences, Government College University, Lahore, Pakistan Introduction The success of a clinical trial is strongly dependent on the structure and coordination of the teams managing it. Given the high stakes and significant impact of every decision made during the trial, it is essential for each team member to collaborate efficiently in order to meet strict deadlines, comply with regulations, and ensure reliable results.
