Drafting of best practice guides for the development and implementation of sensitive data processing.
Context summary
The pharmaceutical company is going into production of Shiny applications and data analysis scripts with R, after having used SAS for many years. Developments within pharmaceutical companies are highly regulated. They require detailed documentation and processes that are verified, validated and tested at every stage. Migrating from SAS requires rewriting all protocols as part of development with R, in compliance with regulations on transparency of data analysis processes.
Our intervention
- Deciphering the context and expectations
- Reflection on existing or missing tools to meet these traceability needs
- Proposal of a working method adapted to the constraints of the client’s internal tools
- Drafting of best practice guides dedicated to the development process with R
Result & added value
- Four good practice guides for agents and external service providers, whatever their level:
- Development process in package format
- Writing R code (code lisibility, etc.)
- Configuration and use of git for code versioning
- Reproducibility (reproducible examples, documentation and tests)
- Increasing the skills of internal Product Owners on good development practices and git.