Open-Source Tools for Clinical Data Analysis
The landscape of data analysis and statistical programming is now evolving along such a path that it can be done many different ways. Analyses which once required purely proprietary frameworks can be done using either proprietary technology, open-source analytics tools, or a combination of both. Open-source tools such as the R statistical programming language and Python are maturing, and these languages are now becoming mainstream in the life sciences industry, driving nonclinical, clinical, and statistical programming into a multilingual activity.
Atorus’ deep roots in the clinical trial space allow for a thorough understanding of the traditional clinical programming process. These engrained procedures and process flows cannot and should not be abandoned overnight. Rather, each organization needs to identify the right opportunities for how these modern, forward-thinking tools fit in.
Our team at Atorus is built with industry leaders who have anticipated and embraced the evolution of open-source analytics solutions. These team members are highly qualified experts who are recognized within the open-source community as key contributors. Atorus’ team is here to support our clients with their data engineering and analytics applications from initial exploration through production. We will help your teams familiarize themselves with Git-based workflows and repository managers such as GitHub to start adopting the software development practices making their way into the clinical research industry. Whether you’re taking your first steps in seeing what open source has to offer, developing internal or open-source packages, or building production-level Shiny dashboards, Atorus provides the support you need.
Analytics Engineering Consulting Services
- Analysis pipeline consulting, utilizing open-source analytics solutions across all phases and functional areas
- Guidance on establishing or updating version control and Git-based workflows
- Bringing exploratory or developmental code for use in production
- R package development, including testing and documentation
- R package validation — internal packages
- R package validation — open-source packages (Comprehensive R Archive Network [CRAN], Bioconductor, GitHub, etc.)
- Customized R Shiny app and dashboard development
- Statistical computing environment consultation, installation, and validation
- Support for creating continuous integration and continuous deployment (CI/CD) pipelines
- SOP development