Coming out soon
We have developed a machine learning and rule based tool called the Clinical Trial Risk Tool using natural language processing. The Clinical Trial Risk Tool allows a user to upload a trial protocol and which categorises the protocol as high, medium, or low risk of ending uninformatively.
When a pharmaceutical company develops a drug, it needs to pass through several phases of clinical trials before it can be approved by regulators.
Before the trial is run, the drug developer writes a document called a protocol. This contains key information about how long the trial will run for, what is the risk to participants, what kind of treatment is being investigated, etc.
The tool is open-source under MIT licence and it does not save any of your data.
Currently, professionals at a funding organisation read the protocols and perform a subjective assessment of the trial’s cost, complexity, and risk of ending uninformatively.
One of the most common causes of a trial ending uninformatively is underpowering. There are several indicators of high risk of uninformativeness which can be identified in a protocol, such as a lack of and or an inadequate statistical analysis plan, use of non-standard endpoints, or the use of cluster randomisation. Low-risk trials are often run by well-known institutions with external funding and an international or intercontinental array of sites. These indicators can be referred to as features or parameters.
If you would like to cite the tool alone, you can cite:
Wood TA and McNair D. Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness. Gates Open Res 2023, 7:56 doi: 10.12688/gatesopenres.14416.1.
A BibTeX entry for LaTeX users is
@article{Wood_2023,
doi = {10.12688/gatesopenres.14416.1},
url = {https://doi.org/10.12688%2Fgatesopenres.14416.1},
year = 2023,
month = {apr},
publisher = {F1000 Research Ltd},
volume = {7},
pages = {56},
author = {Thomas A Wood and Douglas McNair},
title = {Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness},
journal = {Gates Open Research}
}Blog
This post originally appeared on Fast Data Science’s blog on LinkedIn. In clinical research, the journey from a compelling idea to a well-executed study begins with crafting a robust research question and developing a meticulous study plan. This process ensures the study’s feasibility, ethical integrity, and potential impact. Here’s how to transform your research ideas into actionable study plans. Defining the Research Question A research question is the keystone of any clinical study, representing the specific uncertainty the investigator aims to resolve.
This post originally appeared on Fast Data Science’s blog on LinkedIn. A well-constructed study plan is the backbone of any clinical research project. It guides the research process and ensures that the study is feasible, ethical, and capable of generating valid results. Here’s a step-by-step guide to developing an effective study plan. The Research Question The core of your study plan is the research question. It must be specific and significant and address an unresolved issue.
This post originally appeared on Fast Data Science’s blog on LinkedIn. Formulating a robust research question is the foundation of any successful clinical research study. This fundamental step shapes the entire project’s direction, feasibility, and impact. Let’s explore the essential aspects of crafting an effective research question. Origins of a Research Question The best research questions for seasoned investigators often stem from their prior studies or field observations. However, new investigators, although lacking extensive experience, can bring fresh perspectives that might lead to innovative approaches.