Python for Clinical Study Reports and Submission
Chairs: Yilong Zhang and Jonathan Tisack
Industry Heterogeneity
- Medical Devices vs. Pharma: Different use cases and adoption patterns
- Medical device engineers more familiar with Python as standard language
- Creates challenges managing multiple languages within organizations
- Some companies choosing Python-only approach despite challenges
Primary Use Cases
- Medical Devices:
- Large datasets from device data
- Product development beyond just data analysis
- Engineering-driven adoption
- Pharma Applications:
- ETL processes for speed advantages
- Machine learning implementations
- Data engineering tasks
Validation Challenges
- R Validation Hub Principles: White paper was meant to be language-agnostic, principles apply to Python
- Trusted Sources: Questions about what constitutes trusted sources in Python ecosystem
- Anaconda, PyPI with RSPM scrutiny mentioned as possibilities
- Python ecosystem seen as “wild west” compared to R
- Statistical Packages: Python statistical libraries still developing; roughly 8 years behind R in maturity
- Containers: Discussion of containerization for validation approaches
Regulatory and Submission Status
- Submission Gap: No Python submissions to regulators yet (unlike R which has established precedent)
- Regulatory Acceptance: Need for groundwork similar to what was done for R submissions
- Pilot Programs: Reference to submission pilots (pilot 15) for future development
Python vs. R Considerations
- GenAI Integration: Python more prevalent in generative AI development, potentially driving adoption
- Large Dataset Handling: Python advantages for genetic datasets and other large-scale data due to memory limitations in R
- Statistical Maturity: R still ahead in statistical package validation and regulatory acceptance
Future Directions
- Need for Python-specific validation frameworks
- Development of regulatory submission pathways
- Balancing language choice with organizational capabilities