While an “ideal” Statistical Computing Environment (SCE) is highly sought for in the pharmaceutical industry, one is yet to be developed. To encourage tech business leaders to develop such a platform, experts from top global pharma companies have compiled their requirements for the ideal SCE. These requirements were published in December 2021 in a white paper titled “Statistical Computing Environment” (Bynens et al., 2021).
This article summarizes the published white paper and highlights key features that we at Beaconcure believe are vital to the development of new statistical tools.
With new emerging data structures and AI-driven algorithms, current practices and software are outdated and not scalable. There are currently no one-platform solutions that address the full set of pharma end-user requirements.
The SCE is envisioned as a single platform that receives and accesses clinical data at different stages of the drug development process. It allows for analysis, tabulation, validation, and submission of deliverables in line with regulatory requirements.
The following sections dive deeper into specific components that authors of the paper believe the SCE needs to have.
Breakdown of the SCE
User Interface (UI) and User Experience (UX)
The SCE should be efficient, user-friendly, and tailored specifically to the user group needs by following a user-centered design. Users should not be limited in doing things in a particular way. The system should provide a flexible user experience.
It is important for the pharma industry that the SCE is scalable to accommodate different data structures, fluctuations in use, as well as a high performance to allow the analysis of heavy statistical models. Therefore, the SCE should be deployed using cloud computing with high performance and scalable computational engines.
An SCE should be language agnostic and format agnostic, providing users the freedom to develop programs in their preferred language and formats. Clinical and statistical programming languages supported by an SCE in addition to Statistical Analysis System (SAS) can include Python, R, MATLAB, Perl, S+, Julia, shell scripts and more. Standardized structures should be developed that are optimized for all the stages of the drug development process. These structures can be enforced through templates and naming conventions.
Programming Workflow and Processes
Changes and edits to files should be tracked through version control and audit trails capabilities. With version control, multiple users can work on the same project and ensure changes are tracked and can be reverted when needed. Audit trail should “record” who accessed the SCE, when, and what operations/changes were performed. The SCE should also have traceability capabilities allowing users to trace final tables, listings and graphs back to the collected raw data.
The SCE must be able to interact with other systems to enable data to be imported or exported seamlessly. The SCE should have an area accessible to outside vendors (e.g. CROs) so that they can access and submit relevant data directly.
Outsourcing and Collaboration
When deliverables are outsourced to CROs, the SCE should have the functionality to easily import or transfer those deliverables back to the sponsor SCE and vice versa. Internal collaboration can also occur within an organization where it should be easy to work together with Medical Writing, Medical Affairs, Drug Safety, Clinical, Research, etc.
Project Management & Metrics
The SCE should integrate with a planning and tracking tool with the relevant metrics that are suitable for all levels of users. Items can be checked off by team members when certain steps/tasks/deliverables are completed.
It should be possible in the SCE to display different reports or dashboards regarding the metrics collected where these metrics can be compared against a company or industry baseline. Reports should be customizable and standardized, when needed.
For regulatory compliance, the SCE should have these four essential features:
- A robust audit trail
- Access controls Traceability
- Version control combined with end-to-end documentation
- Qualification of personnel and electronic signatures
The SCE itself is a major producer of metadata needed to manage statistical processes and workflow. Therefore, it must incorporate metadata management capabilities within its own environment. The platform should use metadata and existinting standards to enhance productivity and introduce automation to statistical analysis.
Conclusions and Beaconcure’s Role
The key recommendations outlined in the white paper align with Beaconcure’s vision. That’s why, in 2018 Beaconcure created a platform called Verify. Verify facilitates the validation of statistical clinical analysis data using automation.
Verify processed to date more than 100 studies. By taking a user-centric approach, we created a scalable and format agnostic platform that emphasizes the end-user needs for seamless validation processes.
As a leader in clinical data processing, Beaconcure continues to work on improving workflows and contributing to industry standardization efforts.
Bynens et al., (2021). Statistical Computing Environment [White Paper]. Phuse US Connect. https://phuse.s3.eu-central-1.amazonaws.com/Events/2021/US+Connect+2021/SCE_White_Paper_Final_15Dec2021.pdf