<img height="1" width="1" style="display:none" src="https://www.facebook.com/tr?id=905310923417895&amp;ev=PageView&amp;noscript=1">

3 things you should know about ADaM standards

Apr 14, 2020 12:00:00 AM




Author: Ed Chappell

3 things you should know about ADaM standards.

One of the most important standards when it comes to clinical trial submission, the Analysis Data Model (ADaM) outlines how to create analysis datasets and associated metadata. This in turn allows a statistical programmer to generate figures, listings, and tables more easily and ensures traceability, which means that reviewers are able to review and approve a submission more quickly.

Developed as part of the wider family of CDISC standards, ADaM carries this set of five fundamental principles:

Analysis datasets and their associated metadata must:

  • 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.

So far, so good. But, digging a little deeper into the standard, here are three key themes that emerge.



1. ADaM goes hand in hand with SDTM

They might have different functions, but ADaM ties in super-closely with the Study Data Tabulation Model (SDTM). While SDTM is used to create and map collected data from raw sources, ADaM is all about creating data that’s ready for analysis. SDTM is ALWAYS the source of the ADaM data. It could be the domain, it could be a supplemental qualifier, but the source is always the SDTM datasets.


Formedix's free guide on how to overcome SDTM implementation problems




Because SDTM data is standardized, standardized programs and codes can be used to derive ADaM data, leading to increased efficiency in that process.




Some columns in ADaM are derived or calculated assigned values, but all of the general data points must come from SDTM. Take dates, for example – in SDTM, just the date would be recorded. In ADaM, you’ll have the date, its numeric version, and the analysis date which is derived from the original date. But that single date must be able to trace back to an SDTM variable, which in turn traces back to a CRF/EDC variable. This is important because the FDA reviewer can see not just the analysis dataset, but also trace exactly where the data has come from.

You can read more about CDISC standards in our blog Introduction to CDISC standards.



2. Traceability is key

Traceability is one of the most important aspects of the ADaM dataset. According to CDISC, it is “the property in ADaM that permits the user of an analysis dataset to understand the data’s lineage and/or the relationship between an element and its predecessor, the SDTM column. You should provide details of any derivations so that the FDA review can examine the maths."

Traceability facilitates transparency, which is an essential component in building confidence as a result. Ultimately traceability in ADaM permits the understanding of the relationship between the analysis results, the analysis datasets, and the SDTM domains.

Traceability is built by clearly establishing the path between an element and its immediate predecessor. The full path is traced by going from one element to its predecessors, then on to their predecessors, and so on, back to the SDTM domains, and ultimately to the data collection instrument.


common-file-text-edit Note

The CDISC Clinical Data Acquisition Standards Harmonization (CDASH) standard is harmonized with SDTM and therefore assists in ensuring end-to-end traceability. And applying CDASH to eCRFs makes it easier to align the CRF variables to SDTM.


Based on the metadata and the content of the analysis dataset, the reviewer can trace how the primary and secondary efficacy analysis values were derived from the SDTM data for each subject.



3. Subject-Level Analysis Datasets are important

Regulatory agency staff has stated that the Subject-Level Analysis Dataset (ADSL) is very helpful in the review of a clinical trial,” states the ADaM guidelines. “ADSL and its related metadata are required in any CDISC-based submission of data from a clinical trial even if no other analysis datasets are submitted.” ADSL is used to provide the variables that describe the attributes of a subject. The structure of the Subject-Level Analysis Dataset (ADSL) is one record per subject, regardless of the type of clinical trial design. This structure allows simple merging with any other dataset, including SDTM and analysis datasets.

ADSL columns can be imported into other ADaM domains. A strange quirk of the standard is that it lets you combine ADSL with all the other ADaM domains. This is a bit of a shortcut – the alternative would be to combine demographics records with the adverse events column, but you already do that when you create the ADSL. It’s a bit odd in terms of traceability in that it creates two levels: one back to the ADSL and one to the SDTM domain.


Similar blogs you might like...