9 SDTM Mapping Scenarios you need to know

//9 SDTM Mapping Scenarios you need to know

9 SDTM Mapping Scenarios you need to know.

One of the most challenging programming problems in a clinical trial involves SDTM mapping. That is mapping datasets from a non-CDISC structure to the CDISC SDTM structure. An example is mapping datasets from the structure used in your clinical data management system, or from another similar database to a CDISC SDTM structure.

If CDISC standards haven’t been implemented from the start, it’s going to take a lot more time and effort when it comes to SDTM mapping later on. In order for clinical trial data to be analyzed and accepted by regulatory reviewers, CDISC standards must be adhered to. The best practice is always to align with CDISC standards before you collect any patient data.

If you’re interested you can read more on All you need to know about SDTM and Mapping challenges between CDASH and SDTM.

The SDTM Implementation Guide (SDTMIG)

If you’re attempting SDTM mapping and converting data to SDTM, you should have a basic knowledge of how SDTM works. However, if you don’t, the SDTM Implementation Guide (SDTMIG) is an essential resource. The implementation guide gives a detailed overview of SDTM specifications and metadata for all SDTM domains. It includes guidance for producing SDTM datasets. If you get familiar with the SDTMIG before you start SDTM mapping, it’ll make the whole process much smoother!


What’s a typical SDTM mapping scenario?

A typical SDTM mapping scenario contains five steps:

  1. Identify the datasets you want to map
  2. Identify the SDTM datasets that correspond to those datasets
  3. Gather the metadata of the datasets, and the corresponding SDTM metadata
  4. Map variables in the datasets from step 1 to SDTM domain variables
  5. Create custom domains for other datasets that don’t have corresponding SDTM datasets

According to a paper published at PharmaSUG, there are 9 possible scenarios in the SDTM mapping process. If you master these, SDTM mapping becomes much more achievable.


The direct carry forward

These are variables that are already SDTM compliant. These can be directly carried forward to the SDTM datasets, and don’t need to be modified.


The variable rename

Some variables need to be renamed in order to map to the corresponding SDTM variable. For example,  if the original variable is GENDER, it should be renamed SEX to comply with SDTM standards.


The variable attribute change

As well as variable names, variable attributes must be mapped. Attributes such as label, type, length, and format must comply with the SDTM attributes.


The reformat

The value that is represented doesn’t change, but the format it’s stored in does. For example, converting a SAS date to an ISO 8601 format character string.


The combine

In some cases, multiple variables must be combined to form a single SDTM variable.


The split

A non-SDTM variable might need to be split into two or more SDTM variables to comply with SDTM standards.


The derivation

Some SDTM variables are obtained by deriving a conclusion from data in the non-SDTM dataset. For example, using date of birth and study start date to derive a patient’s age, instead of manually entering the age upfront.


The variable value map and new code list application

Some variable values need to be recoded or mapped to match with the values of a corresponding SDTM variable. This mapping is recommended for variables with a code list attached that has non-extensible controlled terminology. It’s also advisable to map all values in the controlled terminology, rather than just for the values present in the dataset. This would cover values that are not in the dataset currently but may come in during future dataset updates.


The horizontal-to-vertical data structure transpose

If the structure of the non-CDISC dataset is completely different from its corresponding SDTM dataset, you may need to transform its structure to one that is SDTM-compliant. The Vital Signs dataset is a key example. When data is collected in wide form, every test and recorded value is stored in separate variables. As SDTM requires data to be stored in lean form, the dataset must be transposed to have the tests, values, and unit under three variables. If there are variables that cannot be mapped to an SDTM variable, they would go into supplemental qualifiers.



CDISC Mapping and Supplemental Qualifiers | PharmaSUG 2014 | https://www.lexjansen.com/pharmasug/2014/PO/PharmaSUG-2014-PO04.pdf