Validation is the foundation of any biostatistics team. Whether facilitating study design, analyzing clinical trial or research study data, or supporting data visualization and interpretation, ensuring data integrity and compliance is essential1,2. However, for new and growing biostats teams, implementing and maintaining a robust validation framework can be challenging. Without a structured approach, teams risk inconsistent results, regulatory non-compliance, and inefficiencies that slow down research and development3.
This article highlights the importance of validation in biostats, breaks down the key components of an effective validation strategy, and provides best practices to set your team up for long-term success.
Why Validation Matters
In the clinical and preclinical research space, regulatory compliance is a necessity, not an option. All organizations involved in clinical or preclinical research, from pharmaceutical and biotech companies to contract research organizations, must adhere to Good x Practice (GxP) guidelines. These include Good Clinical Practice (GCP), Good Manufacturing Practice (GMP), and Good Laboratory Practice (GLP), all of which share a core requirement: ensuring data integrity, accuracy, and security, with the ultimate goal of protecting patient safety2.
One of the most important regulations for biostats teams is 21 CFR Part 114. Under this regulation, statistical computing environments (SCEs) and other software used in clinical research must be validated to ensure they produce reliable, reproducible results5.
Without proper validation, biostatistics teams risk several things:
- Data integrity issues that compromise study results.
- Regulatory non-compliance, which can lead to penalties and product delays.
- Inconsistent results that make it difficult to compare or reproduce findings.
- Audit failures, which can delay approvals or trigger regulatory investigations.
A well-defined validation process helps avoid these issues by ensuring that systems function as intended, regulatory requirements are met, and audits are successfully passed.
Building a Robust Validation Framework
Step 1: Define the Validation Scope and Plan
Before beginning validation, teams must establish a validation plan that outlines the intended use of the system, the specific regulatory requirements that must be met, and potential risks and how they will be mitigated6.
A risk assessment should be conducted to determine the level of validation required6. For example, critical systems that directly impact study results may require a higher level of validation than secondary systems used for administrative tasks. Clearly defining the validation scope helps teams avoid unnecessary work while ensuring compliance.
Step 2: Develop Comprehensive Documentation
Clear, well-organized documentation is essential to an effective validation process. This includes:
- Requirements Specification – outlines what the system must do.
- Design Specification – describes how the system will be built to meet requirements.
- Installation Qualification (IQ) – verifies that the system is installed correctly.
- Operational Qualification (OQ) – ensures that the system functions as intended.
- Traceability Matrix – maps out each requirement to specific tests, ensuring that all system functionalities are validated properly.
Step 3: Execute Testing and Verify Compliance
Testing is a crucial component of validation. Each function of the system must be rigorously tested, as defined in the IQs and OQs, to confirm it meets the predefined requirements. All test results, including any deviations or failures, should be documented.
Key testing strategies include7:
- White Box Testing – Tests the structural components and internal design.
- Black Box Testing – Tests the end-to-end functionality.
Step 4: Finalize Validation and Implement the System
Once testing has been completed, the results should be compiled into a validation summary report. This report provides a comprehensive record of the validation process, including the validation strategy and approach, test execution results and outcomes, any issues encountered, and their resolution. A release memo is then issued, allowing the system to be used in production.
Best Practices for Validation Success
For new and growing biostats teams, setting up a validation framework can seem overwhelming. However, implementing best practices can streamline the process and prevent common pitfalls.
Use Standardized Templates
Developing pre-approved templates for validation documentation helps maintain compliance by embedding required elements upfront. It can also help teams to reduce errors, save time, and maintain consistency between projects.
Ensure Clear Documentation
Clear documentation is crucial to prevent audit failures. Every step of the validation process must be clearly recorded, including decisions, test results, and deviations. Comprehensive documentation not only ensures compliance but also simplifies troubleshooting.
Regularly Review and Update Validation Processes
Regulatory guidelines evolve, and software updates can impact validated systems. Regularly reviewing and updating validation documentation helps maintain compliance and system reliability.
A Smarter Solution: Pre-Validated Systems
For biostats teams looking to simplify validation, pre-validated cloud-based solutions like Accel™ offer an efficient alternative. By adopting a pre-validated SCE, biostats teams can eliminate months of validation work, reduce the risk of errors, and ensure a compliant, scalable workflow. The platform provides pre-built, pre-validated environments following robust validation and change control processes, best practice security and technical controls, and audit trailsforseamless regulatory readiness, as well as secure, centralized access to prevent inconsistencies and unauthorized modifications.
Conclusion
Validation is a critical component of any biostats team’s success. Establishing a structured validation process ensures compliance, enhances data integrity, and reduces inefficiencies that slow down research and regulatory submissions. By following best practices – such as standardizing documentation, regularly reviewing validation processes, and using pre-validated solutions where available – biostats teams can build a strong foundation for long-term success.
Want to learn more about streamlining your bio stats operations? Download our full whitepaper to explore the challenges new teams face and the strategies to overcome them, or reach out here to talk to a validation expert.
References
1. Welty LJ, Carter RE, Finkelstein DM, et al. Strategies for Developing Biostatistics Resources in an Academic Health Center: Acad Med. 2013;88(4):454-460. doi:10.1097/ACM.0b013e31828578ed
2. Good Clinical, Laboratory, and Manufacturing Practices (GxP) – Microsoft Compliance. February 1, 2024. Accessed January 14, 2025. https://learn.microsoft.com/en-us/compliance/regulatory/offering-gxp
3. Thiese MS, Arnold ZC, Walker SD. The misuse and abuse of statistics in biomedical research. Biochem Medica. Published online 2015:5-11. doi:10.11613/BM.2015.001
4. Commissioner O of the. Part 11, Electronic Records; Electronic Signatures – Scope and Application. January 10, 2024. Accessed January 14, 2025. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/part-11-electronic-records-electronic-signatures-scope-and-application
5. Shabir GA. Step-by-Step Analytical Methods Validation and Protocol in the Quality System Compliance Industry.
6. Carter RE. Systems validation: application to statistical programs. BMC Med Res Methodol. 2005;5(1):3. doi:10.1186/1471-2288-5-3
7. Verma A, Department of Computer Science, Amity University, Gurgaon, India, Khatana A, Department of Computer Science, Amity University, Gurgaon, India, Chaudhary S, Department of Computer Science, Amity University, Gurgaon, India. A Comparative Study of Black Box Testing and White Box Testing. Int J Comput Sci Eng. 2017;5(12):301-304. doi:10.26438/ijcse/v5i12.301304
