Introduction
As innovation in clinical development continues to accelerate and decentralized trial (DCT) models become more widely adopted, the volume of clinical trial data from a variety of sources continues to proliferate, creating both opportunities and challenges for life sciences organizations. While more advanced trial modalities such as DCT make trials more accessible and relieves patient burden, the volume and variety of study data collected adds to the complexity of managing clinical data workflows throughout the trial lifecycle. More complex multi-arm trials used for oncology products can also require enormous data and content stores, challenging the capabilities of legacy clinical data stores. Leveraging a modern technology solution is one way sponsors can get in front of these new clinical development challenges. This is the first in our series of e-books to highlight how life sciences organizations can utilize an integrated statistical computing environment to streamline the production of submission deliverables while increasing programming and analysis efficiencies.
What is a Statistical Computing Environment (SCE)?
A Statistical Computing Environment (SCE) is a set of tools for computational processing of clinical data that provides a foundation for demonstrating rigor — which requires transparency, reproducibility, and adequate documentation — in the analysis and reporting of clinical trial results.
Computational Processing of Clinical Data
There are a variety of factors driving the need for an advanced computing environment within life sciences organizations. As computational complexity continues to increase — driven by factors including larger data sets, more sophisticated methods and the emergence of data science techniques — a high-performance computing environment is critical to support output generation. Moreover, a modern statistical computing environment provides the flexibility required to support highly specialized analytic needs and language-agnostic approaches. Additionally, because many organizations support global cross-functional teams, cloud-based solutions are becoming table stakes to ensure the availability of, and access to, real-time clinical data.
Historical Approaches to Statistical Computing
For organizations that do not yet have a statistical computing environment, managing statistical analyses is a manual, disjointed, and highly regimented SOP-driven process for clinical programmers, statistical programmers, data scientists and statisticians. Historically, electronic content-driven approaches for statistical analysis relied upon a dedicated server folder structure, controlled by a network administrator. These approaches require manual sign-off, validation of programs in disparate systems, and often resulted in difficult, time-consuming audits and cumbersome rerun processes.
The first SCEs were able to remove part of the administrative burden. Access control, validation status and version management were very helpful, although advanced features such as programming run order, control of data source changes, and linkage to metadata sources and change history were not yet available in these systems.
In more recent years, large sponsors and CROs have implemented customized SAS-based tools to solve the challenges associated with statistical analysis. While these bespoke solutions can improve analysis and programming workflows, they have been heavily process-driven with long implementation timelines and do not have the flexibility to support additional programming languages — such as R & Python — that are continuing to gain traction among data scientists and statisticians.
SCE Adoption in Today’s Market
In a recent poll , 40% of respondents from mid-market and large sponsors have either purchased an SCE, are in the process of purchasing an SCE, or are building their own validated system. These results indicate that the value of a statistical computing environment is resonating within the market. As DCT and digital trials continue to evolve with larger and more complex data sets, we anticipate these dynamics shifting in the near future. Given the rapidly changing landscape, it seems likely that the 52% of organizations using standalone SAS and/or R will start moving towards purchasing an SCE in order to reduce administrative burden and keep up with the digital evolution of moder clinical trials.
Taking a Modern Approach
In order to modernize their approaches, organizations have recently been turning to consortia like Transcelerate Biopharma, in addition to stalwarts like CDISC. Transcelerate’s Modernization of Statistical Analytics Framework 2 aims to increase the use of modern statistical analysis technologies within the industry, enabling the faster delivery of innovative treatments to patients. Within this framework, three core principles for regulatory compliance are identified as follows: 3
Accuracy
Reproducibility
Traceability
Leveraging an Integrated SCE to Streamline Digital Trials
Demonstrating rigor in the analysis and reporting of clinical trial results requires transparency, reproducibility, and adequate documentation. With the increasing amount of clinical data available, more sophisticated methods, and an emergence of data science techniques, a high-performance computing environment is necessary in order to produce accurate and consistent results in support of regulatory submissions.
Automate & Accelerate Statistical Analyses with an Integrated Statistical Computing Environment Watch Now
Breaking silos with integrated workflows and interoperability
DATAREPOSITORY CLINICALDATA
AREPO T I O NAL
METADATA REPOSITORY
REP O S I YROT
Automation and standardization at scale to unlock value
More companies are considering leveraging an e-clinical platform as they optimize and modernize their analytics and infrastructure strategy. Gartner defines an e-clinical platform as “an integrated suite of technologies connected to a platform architecture that provides services and solutions to manage clinical trial planning and execution”. 4 Utilizing a Statistical Computing Environment within an e-clinical platform that provides access to metadata, clinical data, and operational data can provide even greater benefits for programmers and statisticians. With access to these key elements, data, standards, and mappings can be leveraged for maximum reuse and increased programming and analysis efficiencies.
elluminate Statistical Computing Environment (SCE)
About eClinical Solutions
eClinical Solutions’ products and services are helping life sciences companies adopt a more modern data infrastructure to break down data silos, automate processes and accelerate the speed of drug development. With the elluminate Clinical Data Cloud, life sciences organizations can begin to maximize the value of their clinical data, help their teams accelerate development processes, and serve patients more efficiently throughout the clinical trial lifecycle.
For more information or to request a demonstration of elluminate SCE, visit us at eclinicalsol.com/products/statistical-computing-environment/ or email us at info@eclinicalsol.com
Footnotes
elluminate SCE Fact Sheet Download
1 eClinical Solutions industry poll with 25 respondents from large and mid-size sponsors in February 2021 to gain illustrative insights.
2 Transcelerate, Modernization of Statistical Analytics Framework, June 2021 https://www.transceleratebiopharmainc.com/ initiatives/modernization-statistical-analytics/
3 Transcelerate, Modernization of Statistical Analytics Framework Infographic, June 2021 https://www.transceleratebiopharmainc. com/wp-content/uploads/2021/04/TransCelerate_MSA-Infographic_April-2021-1.pdf
4 Gartner, Market Guide for Life Science E-Clinical Platforms, 26 April 2022, By Jeff Smith