Keep track of clinical trial cost and risk

Coming out soon

Clinical Trial Risk Tool Version 2.0

Signup to receive updates on the release, see tool video preview.

Please select all the ways you would like to hear from Fast Data Science:

You can unsubscribe at any time by clicking the link in the footer of our emails. For information about our privacy practices, please visit our website. We use Mailchimp as our marketing platform. By clicking below to subscribe, you acknowledge that your information will be transferred to Mailchimp for processing. Learn more about Mailchimp’s privacy practices.

Clinical Trial Risk Tool

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.

Clinical Trial Risk Tool

The problem is that each protocol is up to 200 pages long and the structure can vary.

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.

This project shows what is possible with natural language processing. The tool may be extended in future to identify a more complete set of cost, complexity, or uninformativeness risk factors.

Benefits or details include

The tool assists a human in assessing the cost, complexity or risk of uninformativeness of a trial, and understanding which factors contribute to the cost, complexity and risk of uninformativeness.
Reviewers are able to assess trials more rapidly.
The tool may augment certain processes in the approval and funding of clinical trials.
The tool could be used to inform stakeholders about the most impactful features for complexity, cost, and informativeness or risk of uninformativeness.
The tool can assist reviewers in assessing trials more consistently.
At present the tool is limited to 2 pathologies: HIV and TB, but it may be extended in future.
The current tool is designed primarily with trials in LMIC countries in mind but will work on trials globally.
Phases 1, 2, 3 and 4 covered.
The tool has been coded in Python and the source code is available on Github under MIT licence.

The risk factors the tool identifies

Pathology
Phase
Is a SAP (statistical analysis plan) present?
Has the effect estimate been disclosed?
Number of subjects?
Number of arms?
Countries of investigation
Trial uses simulation for sample size?
The features are then passed into a scoring formula which scores the protocol from 0 to 100, and then the protocol is flagged as HIGH, MEDIUM or LOW risk
The risk factors the tool identifies

How to cite the Clinical Trial Risk Tool?

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

Articles About NLP

Clinical trial cost calculator

Clinical trial cost calculator

Understand your clinical trials Are your clinical trials risky? Are costs running away? It’s very tricky to estimate clinical trial costs before a trial is run. Try our free cost calculator. This is a regression model, trained on real clinical trial data. Trial is for condition HIV Tuberculosis COVID Influenza Malaria Enteric and diarrheal diseases Neglected tropical diseases Polio Diabetes Pneumonia Hypertension (see full product) Motor neurone disease (see full product) Multiple sclerosis (see full product) Obesity (see full product) Sickle cell anemia (see full product) Stroke (see full product) Cystic fibrosis (see full product) Cancer (see full product) Other (see full product) Phase Early Phase 1 1 1.

Video interview about Clinical Trial Risk Tool

Video interview about Clinical Trial Risk Tool

This month, I presented the Clinical Trial Risk Tool at the Plotly Dash in Action webinar. I was interviewed by Plotly’s Community Manager Adam Schroeder. You can watch the relevant part of the webinar below. The Clinical Trial Risk Tool was one of four interactive apps presented as part of the webinar. The speakers at the webinar were: Matteo Trachsel, Head of Sustainability at Thermoplan AG, Switzerland, who presented his Product Environmental Report Dash App, which calculates the carbon footprint of coffee machine usage.

Plotly Dash challenge webinar on 7 June, 2023

Plotly Dash challenge webinar on 7 June, 2023

Natural language processing is becoming ever more important in research. The explosion in interest in LLMs is sparking many opportunities to analyse text data in healthcare, social sciences, and other areas. We’re proud to announce that the Clinical Trial Risk Tool has been selected as a winner of the Plotly Dash Example Apps Challenge (2023), out of 25 amazing apps submitted by the community. Funded by the Bill and Melinda Gates Foundation, this app uses natural language processing, Plotly Dash, and the spaCy and Scikit-Learn libraries to calculate the risk of a clinical trial failing to deliver informative results.

Try The Clinical Trial Risk Tool

Clinical Trial Risk Tool