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A Quick Guide To SDTM Mapping 

A Quick Guide To SDTM Mapping

Clinical trials follow the standards of the Clinical Data Interchange Standards Consortium (CDISC). These standards are required when submitting clinical trials for analysis and acceptance to the US Food and Drug Administration (FDA) and Japan Pharmaceuticals and Medical Devices Agency (PMDA). 

Study Data Tabulation Model (SDTM) is one of the standards accepted by the CDISC for streamlining data collection, analysis, management, and reporting. But STDM mapping poses programming challenges, specifically when mapping a non-CDISC dataset structure that would be acceptable by the standards of the CDISC.  

Improper implementation of the CDISC standards can result in more difficult and longer STDM mapping. But SDTM mapping in SAS can simplify this process. Statistical Analysis System (SAS) is an industry standard for managing and analyzing large amounts of data.  

Read this quick guide to STDM mapping using SAS to understand better how it works in compliance with CDISC standards.  

The SDTM Mapping Process 

The SDTM mapping process includes the following steps: 

  • Step One: Dataset Identification  

Identifying the datasets for SDTM mapping is crucial. It’s important to generate raw variables for SDTM variables. But data is different across studies, and unusual raw data types might be present. There might also be limited or unavailable raw metadata. Dataset identification, which involves sorting raw data, can help successfully proceed to the next step.  

  • Step Two: SDTM Dataset Identification 

This step involves identifying the SDTM datasets corresponding to raw datasets. Matching the raw datasets to SDTM standardizes the clinical trial data for review. The first six columns from the SAS are raw variable metadata. 

A dedicated spreadsheet allows a single reviewer to ensure SDTM variable data corresponds to the CDISC’s SDTM Implementation Guide. This spreadsheet records raw variables to SDTM variable link, SDTM supplemental info label, variable metadata, and other useful data for programmers. 

  • Step Three: Metadata Gathering  

This step involves metadata collection of the raw datasets and their corresponding SDTM metadata. 

  • Step Four: Dataset Variable Mapping 

This step involves mapping raw dataset variables to SDTM domain variables. SDTM mapping is easier using the open-source solution, SAS Clinical Standards Toolkit, which supports CDISC standards. In addition, running edit checks on the resulting datasets is important to ensure the proper population of all required variables. Reviewing the results generates further input metadata updates. 

  • Step Five: Domain Customization  

This step involves creating custom domains for raw datasets without matching SDTM datasets. 

STDM Mapping Scenarios  

Clinical trials are critical in securing public health, ensuring people receive safe and effective drugs and treatment for their conditions. But clinical trials, to be successfully recognized and accepted, must comply with CDISC standards, such as the SDTM. 

There are different SDTM mapping scenarios for clinical trials to comply with CDISC standards. Here are some examples: 

  • Direct Carry Forward: The variables are already SDTM-compliant, which can be moved to the SDTM datasets without any modifications.   
  • Variable Rename: Renaming some variables is necessary to map them to the matching SDTM variables.   
  • Variable Attribute Change: This mapping scenario involves changing the variable names and attributes that must comply with the CDISC SDTM. 
  • Reformat: In this mapping scenario, the value represented doesn’t change, only the format.   
  • Combine: This mapping scenario combines multiple variables to create a single SDTM variable. 
  • Split: A non-SDTM variable is a necessary split into two or more SDTM variables for CDISC compliance. 
  • Derivation: This mapping scenario involves obtaining data variables using the non-SDTM dataset to derive a conclusion.  

Other mapping scenarios include recoding or mapping some variables to match with corresponding SDTM variables and transforming the non-CDISC dataset to one that’s SDTM-compliant. 

Utilizing Both CDASH And SDTM Standards  

Applying the Clinical Data Acquisition Standards Harmonization (CDASH) standards in raw data collection eliminates the work required in mapping raw data. This CDISC standard can benefit all clinical trial stakeholders.  

The CDISC explains that clinical trial submissions both require CDASH and SDTM mapping. CDASH standards contribute to data quality, integrity, and traceability. It smoothly guides data collection into SDTM by adding the CDASH-to-SDTM mappings across standard variables. 

Therefore, a dataset in a non-standard format is easier to map using CDASH as it resembles common data collection approaches. Afterwhich, the next step is mapping the data to SDTM based on the CDASH Implementation Guide. 

Below are the benefits of using CDASH and SDTM standards in data mapping:  

  • Utilizing both CDISC standards ensures asking the same questions and utilizing the same answer lists from data collection via analysis. 
  • It supports data traceability. 
  • It optimizes the structure for data transmission, analysis, and reuse. 
  • It reduces programming and validation data resources. 
  • It increases data quality during the transfer from data capture to tabulation. 
  • It helps future-proof the data for warehousing

Conclusion 

Clinical trials must comply with CDISC standards before regulatory reviewers, like the FDA and CDISC, can analyze and accept them. However, mapping non-CDISC datasets to an acceptable CDISC SDTM structure can be challenging. However, following the simplified guide above can help. That way, clinical trials obtain approval from regulatory agencies and benefit more people.  

 

 

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Scott Brennan is the publisher of this newspaper and founder of Access Publishing. Connect with him on Paso Robles Daily News on Google, Twitter, LinkedIn, or follow his blog.