Clinical trial data has become increasingly complex over the past decade. Studies now combine information from electronic data capture (EDC) systems with laboratory results, imaging outputs, genomics datasets, wearable device data, and real-world evidence, often managed across multiple contract research organisations (CROs).
This expansion has increased both the volume and fragmentation of data. As a result, clinical teams typically spend significant time integrating, validating, and preparing datasets before analysis can begin. At the same time, sponsors are under pressure to shorten development timelines, improve data traceability, and ensure inspection readiness across global studies.
SAS Viya is a cloud-native analytics platform designed to support these requirements by providing a unified environment for data management, analytics, and governance across the clinical development lifecycle.
In Brief
- SAS Viya is a cloud-native analytics platform that can support clinical data management, analysis, governance and controlled collaboration.
- In clinical research, it acts as the underlying analytics infrastructure rather than a standalone clinical trial application.
- The article explains where SAS Viya fits alongside SAS Clinical Acceleration, clinical data repositories and statistical computing environments.
- Key limitations include the need for sponsor validation, quality processes, access controls and proportionate governance around AI or synthetic data.
- Readers should come away with a practical view of how SAS Viya supports traceable workflows across complex clinical development programmes.
What is SAS Viya in clinical research?
SAS Viya is a cloud-native analytics platform that supports data management, statistical analysis, machine learning, artificial intelligence (AI), and governance capabilities.
In clinical research, SAS Viya acts as the underlying analytics and data infrastructure rather than a standalone clinical trial application. It enables clinical data to be integrated, prepared, analysed, and governed within a controlled environment.
This allows organisations to manage increasingly complex datasets while maintaining regulatory and operational oversight.
In practice, SAS Viya provides the core analytics and data platform, while clinical research applications built on SAS Viya deliver study-specific workflows and user interfaces.
This separation allows organisations to standardise analytics infrastructure while still supporting specialised clinical workflows.
What is SAS Clinical Acceleration?
SAS Clinical Acceleration is a clinical research solution built on SAS Viya. It applies the platform’s analytics and governance capabilities specifically to clinical development workflows.
The solution combines a centralised clinical data repository with a statistical computing environment, supporting end-to-end data and analysis activities within a controlled framework.
Key capabilities include:
- Consolidation of clinical data from multiple sources into a single governed repository
- Standardised workflows for data review and statistical analysis
- Collaboration across programming, statistics, data management, and clinical review teams
- Traceability from source data through to outputs and reporting
- Support for regulatory submission preparation
By centralising data and analysis within a governed environment, the solution aims to reduce fragmentation and improve consistency across studies.
Why clinical trial teams use SAS Viya for complex data and fragmented workflows
Clinical research environments are often distributed across multiple systems, teams, and vendors. This can create challenges in maintaining consistency, transparency, and efficiency across the study lifecycle. Several factors contribute to this complexity.
Key drivers of fragmentation include:
- Increasing data volume from EDC, laboratories, imaging, genomics, and wearable technologies
- Multi-vendor and CRO-led study delivery models
- Legacy systems with limited interoperability
- Demand for faster access to study-level insights
- Increased use of real-world and biomarker-driven data
- Greater use of multimodal data, trial feasibility assessments, and external control approaches
These factors can lead to duplicated effort, delayed reconciliation processes, and inconsistent datasets.
SAS Viya addresses this by providing a unified analytics environment where data integration, processing, and analysis can occur within a controlled and governed framework.
How SAS Viya supports clinical data management, analysis, and submission workflows
SAS Viya supports the key parts of the clinical data lifecycle, from ingestion through to regulatory submission. It enables structured and repeatable processes across key stages of clinical research.
Data integration
Clinical data from multiple internal and external sources can be brought into a central environment for harmonisation and access.
Data preparation and standardisation
Datasets can be cleaned, transformed, and standardised using repeatable workflows to ensure consistency across studies.
Statistical analysis
The platform supports statistical programming in SAS as well as integration with R and Python, enabling flexible analytical approaches.
Data review and study monitoring
Interactive dashboards and visual tools support ongoing data review, enabling earlier identification of quality or operational issues.
Reporting and submission support
Outputs can be generated in structured and traceable formats to support regulatory submissions and internal review processes.
Controlled programming workflows
In clinical research settings, controlled programming workflows may include managed SAS Studio workspaces, checkout and check-in processes, job execution, workflow manifests, and repeatable analytics runs. This helps teams document how code, data, and outputs move through the analysis process.
SAS Viya, clinical data traceability, and regulatory controls
Regulated clinical research requires not only accurate analysis, but also clear documentation of how results were generated. SAS Viya supports this through governance and traceability features.
Key governance and traceability capabilities include:
- Centralised clinical data repositories that can act as controlled ‘single source of truth’ for study data and outputs
- Source-to-output traceability
- Version control for datasets and analytical code
- Audit trails for data and process changes
- Role-based access and permission controls
- Electronic signature support where required
- Reproducible analytical workflows
The platform also supports widely used clinical standards, including CDISC SDTM, ADaM, Dataset-JSON, and CDISC CORE validation approaches.
These capabilities can support inspection readiness when used within appropriate sponsor validation, governance, and quality processes.
This means the platform still needs to sit within the sponsor’s validated environment, standard operating procedures, and quality management processes.
SAS Viya for clinical programming and cross-functional collaboration
Clinical development relies on coordinated work across programming, statistics, data management, and clinical operations teams, often across multiple organisations and regions.
SAS Viya supports this collaboration through a shared and governed analytics environment.
Collaboration capabilities include:
- Shared access to controlled clinical datasets
- Integration of SAS, R, and Python workflows within one environment
- Support for open formats, REST APIs, and common developer environments such as VS Code and Jupyter, where these are part of the organisation’s setup
- Consistent analytical setups across teams and studies
- Self-service dashboards for non-technical stakeholders
- No-code and low-code access for users who need to review data or outputs without writing programmes
- Reduced reliance on manual data transfers between systems
- Improved alignment between sponsors and CROs
This helps reduce duplication of work and supports more consistent decision-making across study teams.
AI, synthetic data, and the wider SAS Viya life sciences analytics stack
Artificial intelligence and advanced analytics are increasingly being explored in clinical development, particularly for operational efficiency and data insight generation. However, these approaches require appropriate validation, governance, and regulatory oversight.
Common use cases include:
- Identification of data quality issues and operational risks
- Support for risk-based monitoring approaches
- Use of generative AI for document and knowledge workflows
- Development of synthetic datasets for testing and simulation
- Scenario modelling for trial design and recruitment planning
- Exploration of digital twin methodologies in research contexts
For regulated clinical use, these approaches also require attention to explainability, model interpretability, bias monitoring, human review, and clear documentation of how outputs are used.
SAS also offers complementary capabilities within its life sciences portfolio, including SAS Viya Workbench, Life Sciences Analytics Framework (LSAF), and Clinical Enrolment Simulation tools.
These tools extend the SAS Viya ecosystem into broader clinical development planning and operational decision support.
Conclusion
Clinical trials are becoming increasingly data-intensive, distributed, and operationally complex. SAS Viya provides a scalable foundation that supports this work through integrated data processing, statistical analysis, and governed traceability.
When used alongside solutions such as SAS Clinical Acceleration, it can help sponsors and CROs to reduce fragmentation, improve consistency, and strengthen oversight across the clinical development lifecycle.
FAQs
Is SAS Viya still supported?
SAS Viya is currently positioned by SAS as its cloud-native analytics platform and as the foundation for newer SAS solutions, including those designed for life sciences and clinical research. Support status should be checked against current SAS product information before publication.
What is the difference between SAS and SAS Viya?
Traditional SAS environments focus primarily on statistical programming and data analysis. SAS Viya extends these capabilities through a cloud-native architecture that supports scalable computing, integrated data management, AI and machine learning, open-source integration, and collaborative workflows.
Is SAS Viya suitable for large global clinical trials?
SAS Viya is designed to support complex clinical data environments involving multiple studies, vendors, and regions. Suitability still depends on the sponsor’s data standards, validation approach, integrations, user access model, and governance processes.
Quanticate’s statistical programming team can support sponsors using SAS Viya in clinical research by helping ensure clinical data workflows are controlled, traceable, and fit for regulated analysis. To discuss how we can support your clinical trial, request a consultation below.
