Master best practices for software validation in research settings: 7-step guide
Get validation in clinical data management right first time with this actionable 7-step guide. Master the essentials – from early issue detection through to submission-ready datasets.
Why this guide is a must-have:
- A key step in understanding what FDA and PMDA reviewers expect from your SDTM/ADaM datasets, and:
- how validation in clinical data management supports submission readiness
- Explains how to avoid common errors, including treating validation as a final pre-submission check
- Defines a step-by-step approach for building an ongoing quality process for data validation in clinical research
Top 3 critical failures related to validation in clinical data management
❌ Inconsistent standards across study teams – leading to data quality and submission readiness issues
❌ Late validation of clinical study data (causing delays at database lock)
❌ Manual, spreadsheet-based checks that can’t scale as studies grow more complex
This resource addresses these failings and equips you with how to build a risk-based approach to data validation in clinical research that holds up under regulatory scrutiny. Simply complete the form to download your guide.
What you’ll learn in this guide
This guide bridges the gap between understanding why validation matters, and knowing how to embed best practices for software validation in research settings into your everyday process. Specifically, you’ll learn:
- What regulators expect beyond passing technical validation checks:
- how CDISC SDTM and ADaM datasets are assessed for structural compliance and controlled terminology
- Common sources of data inconsistency across EDC, labs, imaging, and ePRO systems
- The documentation needed to demonstrate traceability across a submission package
- How poor data quality ripples across programming, statistics, and regulatory review
- What ‘risk-based’ prioritization means in the context of regulatory submissions:
- How to prioritize findings – distinguishing critical issues from acceptable exceptions
- The difference between one-time validation checks and continuous validation monitoring
- How to implement scalable validation in clinical data management across your portfolio
Download this free resource and master validation of clinical study data ➡️
About the author
Jen Manzi
Subject Matter Expert and User Advocate, Pinnacle 21 by Certara
Jen Manzi is a Subject Matter Expert and User Advocate at Pinnacle 21. She has over 20 years of Pharma/Life Sciences industry experience in Clinical Trials and Safety Data Management. Jen has held various roles within these areas, including eCRF Programmer, SDTM Delivery Lead, Product Owner and Programmer of Batch Processes, Vendor Relationship Manager, Program and Process Improvement Manager, and Validation Lead.
