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SDTM refers to the Study Data Tabulation Model. It’s the standardized format required in the pharmaceutical and clinical research industry to organize and present patient data from human clinical trials to regulatory authorities, like the Food and Drug Administration (FDA). In 2004, SDTM was declared as the required standard format for data submission in clinical research. Proving data in line with the SDTM structure ensures consistency and enables more efficient analysis and greater transparency in clinical research.

SDTM datasets, or Study Data Tabulation Model datasets, are tables of data that are produced in line with SDTM standards. SDTM is the required format for tabulation data submission to the FDA and PMDA. SDTM datasets contain information relating to clinical trial patients, including demographics, treatments, adverse reactions etc. Find out all you need to know about SDTM in our helpful blog >>

SDTM specifications describe how raw data is to be converted. They’re used by biostats programmers to convert data to the SDTM format. The process of developing SDTM datasets starts with identifying the required SDTM domains from your CRFs and raw data. Next, map the raw dataset variables (with a non-CDISC structure) to the relevant CDISC SDTM domain variables. That way, you’ll know which raw dataset will provide the data for your SDTM domain. Finally, list all the variables against each SDTM domain and describe how they should be programmed. Want to know more? Read our helpful best practice guide to CDISC SDTM Mapping >>

SDTM mapping specifications describe how source data is transformed into SDTM datasets for regulatory submission. ADaM specifications define how SDTM data is further transformed into analysis-ready datasets which are used for statistical analyses, tables, listings, and figures (TLFs). Together, they provide traceability from collected data through to study results.

ADaM specifications define the structure, derivations, and metadata required to create analysis-ready datasets. Typically, teams begin by identifying analysis requirements, defining dataset structures and variables, documenting derivation logic, and establishing traceability back to source data and SDTM datasets. Using a specification management platform can help standardize this process, improve consistency, and reduce manual effort. Read our blog to learn 3 things you should know about ADaM standards

Yes. Modern specification management platforms, like Pinnacle 21, can help teams create, manage, and reuse ADaM specifications while maintaining consistency, traceability, and version control. This reduces manual effort and accelerates the development of analysis-ready datasets.

If you’re new to Pinnacle 21 and want to get a feel for its basic validation capabilities, you can access Community here: https://help.pinnacle21.certara.net/en/articles/9736610-download-pinnacle-21-community