Project Scope Several discrepancies have been discovered in statistical analysis results between different programming languages, even in fully qualified statistical computing environments. Subtle differences exist between the fundamental approaches implemented by each language, yielding differences in results which are each correct in their own right. The fact that these differences exist causes unease on the behalf of sponsor companies when submitting to a regulatory agency, as it is uncertain if the agency will view these differences as problematic. Understanding the agency’s expectations will contribute significantly to enabling the broader adoption of multiple programming languages in the production of data submission packages for regulatory review. The Clinical Statistical Reporting in a Multilingual World project seeks to clearly define this problem and provide a framework for assessing the fundamental differences for a particular statistical analysis across languages. In this context, the risk of interpreting numerical differences in analysis results due solely to differences in programming language can be mitigated, instilling confidence in both the sponsor company and the agency during the review period. This will be accomplished by:
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Project Leads | |
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Michael Rimler | |
Mike Stackhouse | |
Lauren White (PHUSE Project Coordinator) | lauren@phuse.global |
Key Milestones
Objective | Timeline |
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Recruit team members | Nov 2020 |
Initial collated (extensible) list of common statistical analyses across the industry | Dec 2020 |
GitHub Repository documenting identified differences between statistical analysis implementations (based on R and SAS use cases as a starting point) | March 2021 |
Expand repository to provide comparable syntax across languages (based on R and SAS use cases as a starting point) | June 2021 |
Expand GitHub repository to incorporate Python and/or Julia | Sep 2021 |
White Paper providing framework for addressing language discrepancies in statistical analysis implementations, including specific use cases as examples | Dec 2021 |
Project Members | Organisation |
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Aiming Yang | Merck |
Andy Miskell | Eli Lilly |
Andy Nicholls | GSK |
Brian Varney | Experis |
Chung-kai Sun | Janssen Research and Development |
Clara Beck | Chrestos |
Doug Thompson | GSK |
Joseph Rickert | RStudio |
Kai Sun | Janssen Research and Development |
Kyle Lee | FDA |
Ke Wang | Novartis |
Michael Kane | Yale University |
Matthew Kumar | Bayer |
Mia QI | Janssen Research and Development |
Min-Hua Jen | Eli Lilly |
Steve Walker | Experis |
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Initialized group, began exploration of partnerships on project with PSI AIMS, the R Validation Hub, and the R Consortium. Began initial recruiting of core team. |