Blog post from BioStata
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…
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Avoid the pitfalls and achieve a swifter approval process
Submitting semi-compliant or non-compliant data is a red flag to the regulatory agencies. At best, it will significantly slow down your approval process – at worst, it can lead to your clinical trial not being approved.
Therefore, data handling is imperative to ensure a smooth progression of your trial.
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How to optimise your data management with minimal effort.
Nothing is as crucial in a clinical trial as ensuring high-quality and valid data – after all, data is the key to a successful (and speedy) approval process.
However, the multiple data sources you collect from the laboratory and from tests during a clinical trial can challenge the data validation and reconciliation phase…
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Observations from the first year in the fields of Biotech and Pharma
We approach the one-year anniversary of the launch of ChatGPT, and in this short span of time, AI has created ripples of innovative opportunities to generate new insights and improve processes in data management across the medicinal product lifecycle …
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