What do you mean the Regulator does not trust my Clinical Trials Submission?
Welcome to the world of reanalysis, skepticism, and safeguarding patient safety. Inside the “trust but verify” mindset shaping clinical trial reviews
Modernizing Clinical Submission Pipelines: A Strategic Shift to Cloud-Native Governed Analytics
The Regulatory Landscape and the Imperative for Transformation
The global regulatory environment for life sciences is undergoing a period of unprecedented intensification.
SAS Viya is your perfect partner to accompany you on this journey. Here is why.
Agencies such as the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have moved beyond simple data review to a mandate for total data transparency and algorithmic reproducibility.
Agencies and their filing requirements governing electronic submission for clinical trials
Select any image to see a larger version.
Mobile users: To view the images, select the "Full" version at the bottom of the page.
Adherence to International Council for Harmonization (ICH) standards—specifically ICH E6 (GCP) for ethical conduct, ICH E3 for clinical study reports, and ICH E9 for statistical principles—is no longer a passive requirement but a rigorous technical hurdle.
Organizations must ensure that electronic records and signatures are strictly governed by 21 CFR Part 11 (see figure 1 above).
Automated validation pipelines and real-time safety reporting, aligned with ICH E2A, provide immediate visibility into safety signals.
For IT and clinical leads, maintaining legacy, siloed data environments is a liability; these architectures create bottlenecks that jeopardize 60-day filing decisions. The strategic shift to unified cloud-native platforms is a prerequisite for any organization seeking to avoid the catastrophic financial and reputational impact of a Refuse-to-File (RTF) letter and loss of 25% of the submission cost.
Takeaway 1: The FDA Doesn't Trust Your Summary (They Rebuild Your Study)
When Regulators Meet Pharma Data: “Trust, But Recalculate Everything”
A common misconception in HealthTech is that regulators like the FDA or EMA simply review a company’s final Clinical Study Report (CSR) to grant approval.
The regulatory review process is essentially an adversarial search for truth.
Regulators do not just consume the final analysis; they demand the raw datasets in the eCTD (electronic Common Technical Document or Module 5) format and use XPT (SAS Transport Files) to independently recreate every table and figure from scratch.
Module 5 or the electronic common technical document (ectd)
This "reproducibility" requirement is the ultimate safeguard for public health. It is enforced through a Bi-directional Traceability Model—a digital thread that must remain unbroken from the protocol to the final output and back again.
- bidirectional traceability model
To ensure this, the industry employs "Double Programming," a rigorous standard where two programmers independently generate the same outputs to compare results.
If their code produces even a single discrepancy, the system halts.
- Protocol: The original objective and endpoint definition.
- Endpoint: The specific metric being measured (e.g., change in systolic blood pressure).
- CRF (Case Report Form): The initial capture of patient-level data.
- SDTM (Study Data Tabulation Model): The standardized, raw data format.
- ADaM (Analysis Data Model): The derived datasets where variables like "Change from Baseline" are calculated.
- Output: The final Tables, Listings, and Figures (TLFs) submitted for review.
"Regulators like the Food and Drug Administration are not just reviewing results—they are verifying: 'Can we independently reconstruct your conclusions from your data?'"
Takeaway 2: Clinical Data Has Its Own Universal Grammar (SDTM & ADaM)
For regulators to review submissions from thousands of disparate companies, data must speak a common language.
This is where CDISC standards act as the "universal grammar."
SDTM organizes raw data into domains, while ADaM creates analysis-ready datasets.
Structuring evidence into tables using CDISC standards
Crucially, this architecture is governed by define.xml, a machine-readable metadata layer that describes every variable’s origin and derivation logic.
Without a precise define.xml, the data is essentially "dark"—unsearchable and unusable by the regulator.
| Domain | Description | Key Variables |
| DM | Demographics | USUBJID (Unique Subject ID), AGE, SEX |
| AE | Adverse Events | AEDECOD (Standard Term), AESEV (Severity), AESER (Seriousness) |
| LB | Labs | LBTESTCD (Test Code), LBSTRESN (Numeric Result) |
| VS | Vital Signs | VSTESTCD (e.g., SYSBP), VSSTRESN (Result Value) |
| EX | Exposure | EXDOSE (Dose Amount), EXSTDTC (Start Date/Time) |
The Strategic Value of Integration: Avoiding the RTF
The "So What?" factor in this transformation is the aggressive mitigation of regulatory risk.
Manual, fragmented reporting processes are inherently prone to human error and inconsistent data handling, which significantly increases audit risk and the likelihood of a Refuse-to-File (RTF) designation.
By contrast, an integrated compliance framework provides a "single version of truth" that ensures Audit Readiness.
The SAS Viya architecture creates such a system, the fully filing compliance operating system.
This system is not merely about avoiding errors; it is about reducing the time spent responding to FDA Information Requests (IRs) by having reproducible evidence readily available.
Transitioning to a standardized data architecture is the only way to transform compliance from a bottleneck into a strategic accelerator.
SAS Viya: Everything you need for creating acceptable submissions to the regulator, nothing you don't.
Learn more here:
Find more articles from SAS Global Enablement and Learning here.
