Speaker
Description
There are many validation approaches for R and its packages that has a foundation in classic software and application validation, specifically how it relates to statistical analysis applications within regulated industries like Life Science. The introduction of risk-based validation approaches in the last decade has provided additional tools, but as we now approach R as a language, packages as language extensions and that we seldom independently validate languages as part of Computer System Validation, there is an inherent conflict with expectations and industry standards, guidelines and regulations, such as ICH Good Clinical Practice..
We explore a risk-based validation approach that is based on the risk that a package functions and computational methods produces an incorrect result that is not or cannot be caught by the mandated quality controlled processes, i.e. quality checks and output validation. The result is a simplified validation process that is more robust, much easier to implement, lighter on the documentation and simple to automate.
Additional Material or Paper
https://www.linkedin.com/pulse/validating-r-python-gxp-use-life-sciences-magnus-mengelbier-maw2f
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| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | R, package, validation, GxP, Life Science |
|---|---|
| Virtual Option | This submission is for onsite presentation primarily, but I would also like it to be considered for pre-recorded virtual presentation if I don't get an onsite slot |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |