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Demystifying CDISC, SDTM, and ADaM

The world of clinical study data can be challenging and confusing. For those in the pharmaceutical industry, understanding and managing CDISC standards is essential to collecting quality and compliant clinical study data.  

Below, we take a look at three of CDISC’s essential standards, CDASH, SDTM and ADaM. We break down the basics of these models, to give you a better understanding of how they work and how, by adhering to these standards, you can elevate your data management practices. 

Key definitions 

Before we begin, a short list of relevant abbreviations: 

  • ADaM = Analysis Data Model 
  • CDASH = Clinical Data Acquisition Standards Harmonization 
  • CDISC = Clinical Data Interchange Standards Consortium 
  • SDTM = Study Data Tabulation Model 


Before CDASH, SDTM and ADaM; brief history of clinical study data 

Clinical trials have been conducted since biblical times (Book of Daniel, chapter 1, verses 12-15) and likely even earlier. At the heart of every clinical trial is a scientific hypothesis. And to answer each hypothesis, we collect data for analysis. Therefore, the data collected during a clinical trial is the tangible results of the hard work of the entire clinical team. That clinical trial data can then be analyzed multiple times and in multiple ways to address the study hypothesis or generate new hypotheses for future examination. Without effective data collection, we can’t effectively analyze the results and make decisions around the safety and efficacy of the treatment. 

Throughout history, we have constantly improved our data collection techniques, accuracy, and precision to minimize data errors in clinical trials. These improvements were often a result of innovations from scientists and entrepreneurs. These advances include: 

  • Clinical monitoring 
  • Case report forms (CRFs) 
  • Electronic data capture (EDC) 
  • Clinical study databases 

With the vast improvements in data collection techniques and the increased speed at which we acquire new data, similar advances in data analysis have occurred. Despite all these advances, for a long time, improvements in data collection and analysis were not harmonized across pharmaceutical companies, therapeutic areas, or countries of the world. So, before the establishment of clinical data standards, clinical trial data was not interchangeable or accessible to researchers with new hypotheses. 

The creation of CDISC 

The Clinical Data Interchange Standards Consortium (CDISC) was formed in 1997 to develop global standards and innovations to streamline medical research and ensure a link with healthcare. The CDISC mission is to “enable the accessibility, interoperability, and reusability of data for more meaningful and efficient research that has greater impact on global health.” 

The consortium includes members from pharmaceutical companies, medical device manufacturers, regulatory authorities, and service providers. This consortium publishes standards that are recommended for clinical trial data to further the goal of interoperability. Now let’s look at some of the key standards. 

Clinical Data Acquisition Standards Harmonization (CDASH) 

CDASH was initiated to standardize the data collection process. When data is collected from a clinical trial, it is entered into an electronic database (like a large spreadsheet). Each item placed in the database normally includes unique identifying information. For example, if body weight is measured, the data is body weight and the unique identifying information includes patient ID, date, time, study, study visit, etc. Each piece of information is put into a spot in the database called a “field” or “database field”. Values for a specific “field” or all body weights can then be extracted from the database for analysis.  

The CDASH standards specify the name and type of fields that can be used. For example, to record weight, one company might use “Weight” while another may use “WT”. The CDASH standards specify the “field” names and how the data is organized. The CDASH standards are used when developing CRF and EDC systems. 

Study Data Tabulation Model (SDTM) 

After the data is collected into a clinical database, it must be converted into standard data tables to be used for analysis. SDTM defines the way in which individual observations from a clinical study are compiled. The basic concept is that each piece of data can be uniquely identified based on corresponding information (eg, patient ID, date, time, study, study visit, procedure, measurement unit, etc.). So, each row contains one piece of data and many columns of identifying information. While this method may lead to bloated files due to many blank columns, it is comprehensive and consistent across studies.  

The data in SDTM is broken into multiple “domains” such as demographics (DM), subject visits (SV), concomitant medications (CM), exposure (EX), adverse events (AE), ECG results (EG), laboratory results (LB), PK concentrations (PC), PK parameters (PP), and vital signs (VS). Each domain usually is constructed as a single file with the domain as the filename (e.g., CM.xpt). These SDTM datasets can be used directly for analysis if no further calculations are necessary. 

SDTM domains

Learn how to get on-demand SDTM datasets and make informed decisions, earlier. 

Analysis Data Model (ADaM) 

Analysis datasets are created to enable the statistical and scientific analysis of the study results. ADaM specifies the fundamental principles and standards to ensure that there is clear lineage from data collection to analysis. The ADaM datasets are the “authoritative source for all data derivations used in statistical analyses.”  

For example, if change from baseline in body weight was the primary efficacy variable, the SDTM would contain each body weight measurement. An ADaM dataset would include the derived change from baseline body weight for each time point to be included in the statistical analysis. The ADaM datasets are not required unless data derivations are performed based on SDTM data. In addition, ADaM datasets should only be derived from SDTM datasets. 

The impact of CDASH, SDTM and ADaM 

Data standardization allows for interoperability between software, analysts, and organizations. The CDASH standards have been applied to data collection to standardize the variable names in clinical databases. The SDTM datasets provide standards for organizing clinical trial data following database lock. And the ADaM datasets provide a connection between the SDTM datasets and final statistical analyses.  

In conclusion, these concepts are simply standards for clinical trial data. Just like we have standards for electrical outlets that permit developers to create safe and effective electric equipment, the CDISC standards are intended to permit drug and device developers the opportunity to analyze clinical trial data and make important healthcare discoveries.  

There you have it – the basics of CDISC’s CDASH, SDTM and ADaM standards. To find out more about SDTM, read our comprehensive blog on everything you need to know about SDTM

Ready to start mapping to SDTM? Why not download our free, best practice guide to SDTM mapping.

This article was originally written by Nathan Teuscher in October 2013, and was updated in May 2024 for accuracy and comprehensiveness. 

About the author

By: Nathan Teuscher