Drug development delays are common throughout the course of clinical studies, and clinical programmers and biostatisticians experience their fair share of bottlenecks.
Even simple things like working in different integrated development environments (IDE) can cause hiccups that have a direct impact on productivity and study performance.
All that’s changing with a new collaborative workspace called the Smart Programming and Analysis Computing Environment (SPACE).
SPACE offers multiple use cases for programmers and biostatisticians along the submission pathway. It can be used to develop QC, SDTM, ADaM, and TLF, perform cross trial and PK/PD analyses, and mine data for predictive analytics using pre-trained and contextually aware deep learning models.
SPACE is much more than a standard statistical computing environment (SCE), because it allows users to bring in any tool/IDE (containerized or non-containerized) to access structured or unstructured data and perform analytics tasks to achieve submission deliverables.
This new interface streamlines workflows so clinical study design, management, and regulatory review can proceed much more smoothly. Because SPACE offers a whole new level of convenience, we like to call it a playground for designing experiments and constructing visualizations.
Use Your Favorite Editing, Reporting, & Analytics Tools: SPACE Supports Them All
One of the key benefits of SPACE is that programmers and biostatisticians can use their favorite tools, regardless of what anyone else is using.
The animation below shows how a user can open a workspace using a preferred RDP or Web Editor. Once a user works on a project using their favorite tools, they can save the project to SPACE so other users can work on it in the ways they want, too.
The GIF below shows a user opening a Python script in Visual Studio, making changes, and saving the work to SPACE.
Save Time Using Data That’s Already Pre-Filtered
In the visuals above, you can see that SPACE uses a simple, customizable folder structure to hold your projects.
What’s more, these projects can all be connected to Saama’s Clinical Data Hub, a central repository that integrates and updates data from EDC, CTMS, IxRS, labs, and other sources in real time. This valuable data integration ensures that clinical programmers and biostatisticians are always working with data that’s as clean and current as possible.
There’s More to SPACE than a Great Workspace
In addition to a single, shared interface connected to a central, secure data repository, SPACE offers so much more that makes programming, research, and administrative tasks more simple, effective, and scalable:
- File and folder management with version control
- Search and catalog support for structured and unstructured data
- Controlled external tool access for data provisioning
- Configurable status management for files, programs, and datasets
- Intuitive workflow for enabling document lifecycle status across stakeholders
Pipeline and Integration
- CDH integration for clinical data and metadata (structured and unstructured)
- Auto-generation of transformation specifications from unstructured documents (Protocol, SAP, etc..)
- Library management for SDTM/ADaM/TLF
- Management of global study, study snapshot, and study pool metadata
- Configurable job builder with workflow and notifications
- Pre-built integration to standard clinical programming editors and IDEs
- Machine learning and models management (AutoML)
- Clinical models library for predictive use cases
- Resource and workload monitoring for improved utilization
- Document exchange for managing collaboration
- Web services framework for inbound and outbound integration
- File browser with access control
- IDE integration (containerized and non-containerized)
- Plugins to integrate quality check, compliance (e.g., P21 tool) and anonymization tools
- Template management for folder structure set up
- Study lineage and impact analysis for metadata change management
- Task request management for improved workflow
- QC framework for submission pathway review
- Programs, dataset, output dependency analysis for metadata change management
- Roles and user groups for enhanced security management
- Job configuration templates for improved job management
- Security framework at both global and study levels
- Self-serve reporting (usage, lineage, and dependency analysis)
- Audit trails (21 CFR Part 11 adherence)
- Self-serve notification and task management
To learn more about how this powerful, collaborative environment can accelerate the work of your clinical programming and biostatistics teams, contact us.