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From cars to computers, with every product we use, a tipping point comes when it becomes ineffective or obsolete and needs replacement with a shinier and better performing version. Most of us resist change, and would rather plod along, using the old and underperforming version until it breaks down on us and refuses to go any further. In this process, we generally lose out on improved productivity and superior results.
Let’s consider how this scenario translates to modern clinical trials, with specific reference to an SCE (Statistical Computing Environment)?
Have you outgrown your SCE?
Technology keeps evolving, with continuous change and improvements occurring at a rapid pace. Such changes are in tune with developments in operating processes as well as regulatory requirements. When we refuse to make the switch, we lose out on many of these beneficial changes that potentially improve efficiencies. When an SCE outgrows its usefulness, we see statisticians, data scientists, and programmers struggling to keep up with the expectations. Let’s look at some tell-tale signs which tell us when our SCE has outgrown its usefulness and must be replaced:
What can you expect from an SCE?
Clinical development teams using an SCE should enjoy seamless collaboration, controlled compliance, and powerful computing that empowers the programmers, statisticians, and data scientists on the team to be more productive. Complex data, a myriad of sources and formats, and varying requirements for different stakeholders make this a difficult task. Yet process optimization is essential for teams to realize time and cost savings.
In other words, an effective SCE enables teams to work collaboratively and offers an integrated and accessible environment where the teams have clear visibility into the lifecycle of statistical programming. The environment should offer audit trails, version control, and traceability while also enabling distributed and remote work teams. It also offers powerful computing to create and manage data structures quickly to gain insight into complex tasks.
Ensure that your Statistical Computing Environment (SCE) is a controlled and collaborative workspace where Biostatisticians, Clinical Programmers, and Data Scientists can manage tools, and share best practices, analytics, and validation plans. It needs to be a metadata-driven, scalable computing environment built upon an integrated data repository that supports exploratory analysis and the production of review-ready deliverables for regulatory submission.
From its intuitive user interface to its nimble product architecture, a modern SCE needs to deliver a robust data science and analytics functionality that Life Sciences R&D teams demand, along with some of these specific functions:
An integrated repository:
The SCE’s metadata-driven environment is an integrated data repository that supports regulatory as well as exploratory analysis without constraints. Access to such an integrated platform offers numerous benefits to biostatisticians, programmers, and other clinical trial team members. They gain version control with a single source of truth for clinical trial data, an integrated computing experience, and a controlled and compliant environment that offers traceability, transparency, and auditability.
The robust integrated platform supports global access and enables collaboration in a secure and compliant environment. Teams can manage sequences of steps, assess progress, and use automated workflows for review and validation. They can use familiar, industry-standard tools such as SAS, R, and Python to efficiently clean, analyze, and present more meaningful data visualizations. As the complexity of clinical trials grows, data scientists, biostatisticians, and statistical programmers need to use a variety of tools to support complex analyses. SCE’s powerful web-based computing needs to support such regulatory and exploratory analyses.
Metadata-driven workflows must support collaboration, review, and finalization of data deliverables. By accelerating analysis and productivity, the technology offers time and cost savings. With configurable workflow-driven automation, it increases the speed and accuracy of production and lifecycle management of CDISC-compliant submission data packages, including SDTM and ADaM datasets, as well as the Tables, Listings, and Figures (TLFs), and patient narratives incorporated into the clinical study report (CSR). Robust analytics enable teams to make faster, more informed decisions earlier in the data lifecycle.
Other beneficial features:
Using complex data, a myriad of sources and formats, and varying requirements for different stakeholders makes deriving insights a difficult task. Environmental controls, ongoing visibility into the process, and automated workflows supporting reviews and validation are essential for success. An effective SCE would enable seamless collaboration and offer these additional benefits.
With such an SCE, life sciences teams get to improve collaboration at each stage of the clinical data lifecycle including data ingestion, integration, transformation, review, and analysis. As biostatisticians and clinical programmers collaborate with data management and clinical operations, teams can help answer critical safety and efficacy questions about the research product and quickly and accurately create submission-ready data deliverables for the products under investigation. So, would you say you have outgrown your SCE?
At MaxisIT, we clearly understand strategic priorities within clinical R&D, as we implement solutions for improving Clinical Development Portfolios via an integrated platform-based approach. For over 17 years, MaxisIT’s Clinical Trial Oversight System (CTOS) has been synonymous with timely access to study-specific, standardized and aggregated operational, trial, and patient data, enabling efficient trial oversight. MaxisIT’s platform is a purpose-built solution, which helps the Life Sciences industry by empowering business stakeholders. Our solution optimizes the clinical ecosystem; and enables in-time decision support, continuous monitoring over regulatory compliance, and greater operational efficiency at a measurable rate.