One of the most important standards when it comes to clinical trial submission is the Analysis Data Model (ADaM). ADaM standards outline how to create analysis datasets and associated metadata. This allows a statistical programmer to generate figures, listings, and tables more easily and ensures traceability, which means that reviewers are able to assess and approve a submission more quickly.
- facilitate clear and unambiguous communication
- provide traceability between the analysis data and its source data (SDTM)
- be readily usable by common software tools
- be accompanied by metadata
- be analysis-ready
1. ADaM goes hand in hand with SDTM
SDTM (Study Data Tabulation Model)
ADaM (Analysis Data Model)
CDISC’s ADaM is a bit different to SDTM. It still has a core model and an implementation guide, but the model is not as prescriptive. Additional variables can be added within certain constraints defined by the model. This gives it the flexibility to be used for any type of analysis while providing a level of standardization that allows it to be easily understood by reviewers.
ADaM datasets example
SDTM datasets are grouped to ease visual review. For example, a reviewer could filter a vital signs dataset for body temperature for a subject across the entire trial and spot any patterns or anomalies.
ADaM datasets are organised for a different purpose. ADaM is the arrangement of variables so that you can easily perform a calculation for a specific result presented in the Table, Listings and Figure (TLF). It may be something as simple as providing a breakdown of the number of male and female subjects in the study and which age groups they fall into. To achieve that, you need to bring together all the required variables from the SDTM sources, and rearrange them into a different grouping to get your result.
Because SDTM data is a standardized structure, standardized programs can be used to derive ADaM data, leading to increased efficiencies. For example, if you need a value from your vital signs dataset, such as the vital signs start date, you can import it from SDTM directly into ADaM. You don’t need to reinvent the wheel, and this ensures you have traceability back to the source data.
2. Traceability is key
3. Subject-Level Analysis Datasets are important
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Author's note: this blog post was originally published in April 2020 and has been updated for accuracy and comprehensiveness.
About the author
Solutions Consultant | Formedix
Ed Chappell has been working as a Solutions Consultant with Formedix for over 15 years, and has 22 years’ experience in data programming. He authored and presents our training courses for SEND, SDTM, Define-XML, ODM-XML, Define-XML and Dataset-XML.
Ed was heavily involved in the development of our ryze dataset mapper, and works closely with customers on SDTM dataset mapping. As an expert in clinical data programming, Ed also supports customers with Interim Analysis (IA) SDTM and FDA SDTM clinical trial submissions.