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Embracing Open-Source Software
Will open-source programming languages uncover new possibilities when transforming or summarising your clinical trial data?
For a long time, SAS has been the go-to tool for data analysis in clinical trials.
As a result, it is accepted by regulatory agencies today and has built a reputation for robustness and reliability — both essential for managing the extensive datasets associated with a clinical trial. However, times might be changing as open-source alternatives, such as Python and R, have gained increasing popularity over the years. In this blog post, we will take a closer look at how using open-source software benefits the way we transform and summarise clinical trial data and the potential risks of which we must be aware to ensure data integrity.
Open-Source Software Is the Flexible Alternative
There are multiple reasons to consider open-source software, but for most, the primary appeal is flexibility and cost-efficiency.
Unlike proprietary software, open-source platforms are free to download and use, thereby eliminating licensing fees. They also offer significant flexibility and customisation, allowing users to tailor modules to their specific requirements and democratising access to advanced data analytics.
However, incorporating open-source software possesses the potential for further benefits. R and Python have very active and highly skilled user communities, enabling quick development of new modules or programs, making them more dynamic and agile than closed platforms.
A Potential for Addressing the Talent Shortage?
A Potential for Addressing the Talent Shortage?
Are we looking at an even broader scope? The use of open-source software exposes biostatistics to a whole new talent pool.
R and Python are widely known—and used—in other industries and have already become the norm for many educational institutions as they are much more cost-efficient. This will make open-source software an even better-known tool for future graduates and potentially help address the labour shortage, which is only becoming more challenging as the number of clinical trials increases.
Incorporating Open-Source Software Is Not Without Challenges
Despite its numerous benefits, transitioning to open-source software presents challenges, especially regarding compliance, documentation, and validation. Open-source tools lack the controlled environment of proprietary software, posing risks to successful approval processes.
Therefore, it is paramount to establish a programming setup that meets the stringent demands for compliance, validation, and transparency — crucial elements in safeguarding any clinical trial.
A widely recommended strategy involves designating a single administrator with the authority to provide access or add modules, ensuring only company-validated packages are being used. Such control mitigates the risks associated with unverified code that could introduce errors or compromise reproducibility.
Using a Risk-Based Approach Could Be a Good Starting Point to Set Up Your Programming Environments
A risk-based approach would be a good starting point for incorporating open-source software in clinical trial data management. It will allow you to assess potential risks, such as data breaches and non-compliance. Simultaneously, it enables tailoring the programming environment to your company’s specific needs and size.
This approach could involve developing specific validation protocols, ensuring training, establishing guidelines for safe and effective software usage, and setting up regular reviews to ensure software components remain updated and secure.
An R-Based Submission Has Already Been Completed
In 2021 Novo Nordisk completed the first successful R-based submission to the FDA, which might be a catalyst for further heightening the interest in open-source software.
The submission included SDTM and ADaM datasets as well as programs, documentation, and analyses. Despite a few delays — something that is not uncommon by any means within clinical trials — the submission was completed successfully. For further details, you can read the article “Learnings from the First R-Based Submission to FDA by Novo Nordisk” from Appsilon.
With Careful Consideration, Open-Source Software Could Add New Benefits to Our Industry
To successfully implement open-source software, it is crucial to pay attention to reproducibility and documentation practices, as well as to establish validation procedures that ensure the accuracy and reliability of analyses.
Here, you can utilise tools such as R Markdown in R or Jupyter Notebooks in Python to create reproducible analysis documents, including code, results, and interpretations, to accommodate necessary audit trails and ensure regulatory compliance.
Furthermore, you could establish procedures to perform thorough testing of your code and compare results with known benchmarks or SAS outputs to validate the correctness of your implementation.
Like any new tool or technological development, meticulous care is necessary to protect the integrity of your clinical trial. However, despite potential risks, completely rejecting new tools could be a mistake that will stagnate our industry and hinder the development of smarter and more optimal workflows in the future.
After all, our industry is built upon progress.
BioStata Is Your Data Partner
Are you curious to discuss the use of open-source software further, or would you like to learn more about how BioStata could assist you in achieving fully compliant data and a swift approval process? Please complete the form, and we will contact you to arrange a meeting.