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The Study Data Tabulation Model (SDTM) is a standard created by the Clinical Data Interchange Standards Consortium (CDISC) about 25 years ago. It helps organize clinical trial data into a format that regulatory agencies can review. SDTM specifications guide how raw study data is turned into SDTM datasets. When these specifications are wrong, submissions face delays or rejections. This article covers common mistakes in SDTM specification and how to avoid them. (1)
Mistake 1: Misunderstanding the Role of SDTM Domains
To begin, it’s important to know how SDTM domains organize different parts of a clinical study.
Many teams assign data to the wrong domain. They may mix interventions, findings, and events in ways that don’t fit domain models. This creates problems during review because the structure doesn’t match expectations. The best way to prevent this is to map study data to the right domain before writing specifications. Careful domain mapping makes datasets more consistent with the SDTM Implementation Guide.
Mistake 2: Incorrect Use of Controlled Terminology
Another frequent mistake comes from using controlled terminology the wrong way.
Controlled terminology ensures consistency across studies. If teams use outdated codes or incorrect values, submissions may be rejected. In the March 28, 2025 update from CDISC, there were a total of 1051 new terms introduced. Coding adverse events or medical history with the wrong terms can cause compliance problems. These errors also slow down the review process since they require time-consuming corrections. One practical way to reduce this burden is to use tools that help optimize SDTM specification workflow. Adding structured checks early makes it easier to catch issues before they affect submissions. (2)
Mistake 3: Mixing Up Dataset Names and Variable Names
Clear naming is key to creating SDTM datasets that are easy to review.
One common issue is confusing dataset names with variable names. This mistake makes the data harder to understand and slows down submission. To avoid this, teams should create a standard naming guide and stick to it. Checking specifications against this guide helps keep naming consistent. Reviewers will then find the data easier to follow.
Mistake 4: Ignoring the SDTM Implementation Guide
The SDTM Implementation Guide (IG) is the main source for applying SDTM standards, yet many teams skip it.
When the IG is ignored, datasets may have missing variables, wrong formats, or misplaced data. These errors often require rework before submission. To avoid them, teams should use the IG as a daily reference. Creating checklists based on it is also helpful. Following the guide closely improves data quality and makes regulatory review smoother.
Mistake 5: Overlooking General Observation Classes
General observation classes form the base of SDTM datasets. These include Events, Interventions, and Findings.
A common mistake is putting data into the wrong class. For example, placing adverse events under Findings instead of Events. Misclassification creates confusion and makes reviews longer. To avoid this, teams should confirm that data fits the purpose of the chosen class. Short training sessions can also help team members understand the differences between classes.
Mistake 6: Poor Handling of Adverse Events Data
Adverse events (AE) data is one of the most important parts of a clinical trial.
Errors in AE data often include missing dates, inconsistent coding, or weak links between AE datasets and other related datasets. These problems can raise concerns during safety review. The best way to avoid them is to apply controlled terminology, capture timing variables, and double-check links across domains. Well-structured AE data makes reviews faster and more reliable.
Mistake 7: Not Validating Against CDISC Standards
Skipping validation checks is another mistake that delays submissions.
Validation tools such as Pinnacle 21 detect missing variables, wrong formats, or values that don’t meet CDISC standards. It basically helps with data standardization, contributing to quick drug development. If validation is skipped, many errors remain hidden until the final stage. That can lead to costly delays. The best approach is to build validation into every stage of dataset development. Regular checks improve compliance and reduce rework. (3)
Mistake 8: Mismanaging Non-Clinical Data Integration
Clinical studies often include both clinical and non-clinical data. Bringing these datasets together is a challenge.
Problems occur when non-clinical data follows different standards or naming rules. This creates mismatches and slows down submission. To avoid issues, define integration rules before work begins. Apply consistent dataset names, variable names, and controlled terminology. Planning early ensures that clinical and non-clinical data align properly.
Mistake 9: Failing to Account for Regulatory Agency Expectations
Different regulatory agencies may have slightly different expectations for SDTM datasets.
If teams ignore these differences, submissions may need major corrections. Some agencies require extra variables or stricter dataset structures. To avoid this, review submission guidelines for each agency before building specifications. Preparing for these expectations early reduces rework and improves acceptance.
Mistake 10: Lack of Documentation in Specification Development
Finally, missing documentation weakens the quality of SDTM specifications.
Without records of how specifications were developed, it becomes hard to update or troubleshoot datasets later. It also creates confusion for new team members or outside reviewers. Good documentation should include domain mapping choices, naming conventions, controlled terminology references, and validation steps. Organized records support consistent work across multiple studies.
Stronger Clinical Trial Data Through Better SDTM
SDTM specification errors can slow down or even block regulatory submissions. Mistakes such as misusing domains, ignoring the IG, or mishandling adverse events data often cause delays. Problems also arise when validation is skipped or documentation is missing.
By using updated controlled terminology, applying observation classes correctly, and preparing for agency expectations, teams can avoid these pitfalls. Careful SDTM specification creates stronger datasets that meet CDISC standards and support successful clinical trial submissions.
References:
- “Navigating data standards in public health: A brief report from a data-standards meeting”, Source: https://pmc.ncbi.nlm.nih.gov/articles/PMC10995743/
- “Controlled Terminology”, Source: https://www.cdisc.org/standards/terminology/controlled-terminology
- “Virtual Clinical Trial Firm Certara Buying Pinnacle 21”, Source: https://www.bloomberg.com/news/articles/2021-08-05/virtual-clinical-trial-firm-certara-is-said-to-buy-pinnacle-21