Understanding CDISC Standards: An In-Depth Look at Different Types

The Clinical Data Interchange Standards Consortium (CDISC) plays a crucial role in the standardization of clinical research data. By providing a suite of global, system-independent data standards, CDISC ensures clinical data is consistently formatted and easily interpretable. This consistency is vital for streamlining the regulatory review process, facilitating data sharing, and improving the overall efficiency of clinical research. Keep reading as the experts from Rancho BioSciences, a premier life science data service provider, explore the different types of CDISC standards and their significance in the clinical trial landscape.

Study Data Tabulation Model (SDTM)

The Study Data Tabulation Model (SDTM) is the foundational standard, and perhaps the most widely recognized of all the CDISC standards. It defines a standard structure for clinical trial datasets that are to be submitted to regulatory authorities, such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA).

SDTM consists of over 50 domains, each corresponding to different types of data collected in a clinical trial. These include, for example:

• DM (Demographics) – Data on subject demographics, such as age, sex, and race
• AE (Adverse Events) – Information on adverse events experienced by subjects
• CM (Concomitant Medications) – Details about medications taken by subjects in conjunction with the study treatment
• VS (Vital Signs) – Vital signs measurements, such as blood pressure and temperature

The full set of available domain standards range from the relatively simple data, such as listed above, to complex clinical measurements and assays used to measure the effectiveness and safety of the drug candidates under study. In addition, the standards are continually being reviewed, updated, and extended to account for the ever-changing clinical trial methods and technical capabilities. The SDTM ensures this wide variety of data is organized in a uniform manner, facilitating the review process by regulatory bodies and enabling better data integration and comparison across studies.

Analysis Data Model (ADaM)

The Analysis Data Model (ADaM) focuses on the creation of datasets that are used for statistical analysis. Built on top of SDTM, which standardizes data collection, ADaM standardizes data analysis. The ADaM datasets are created from STDM, extending the primary results from STDM with calculations specified for the trial analysis. The most common calculation is the determination of how a specific measured characteristic of a trial participant changes over the course of the trial. The calculations are described in specific documents providing the required link between the original data in STDM format and the analysis data in the Adam datasets.

Most ADaM datasets align with an SDTM domain. For example:

• ADSL (Subject-Level Analysis Dataset) – Contains one record per subject, including demographic and treatment information
• ADAE (Adverse Event Analysis Dataset) – Designed for analyzing adverse event data

ADaM datasets are critical for ensuring the results of clinical trials are traceable, accurate, and reproducible while also facilitating review by regulatory authorities. They help in clearly demonstrating how the data supports the study’s conclusions.

Standard for Exchange of Nonclinical Data (SEND)

The Standard for Exchange of Nonclinical Data (SEND) is largely overlapping with SDTM but applies to nonclinical studies. Nonclinical studies, such as animal safety and toxicology studies, are an essential part of the drug development process. The SEND set of standards extends SDTM to include domains specific to the results from these studies.

SEND provides a standardized format for nonclinical study data. Some components unique to SEND include:

• BW (Body Weights) – Body weight measurements of the test animals
• MA (Macroscopic Findings) – Data on visual observations of the test animals
• OM (Organ Measurements) – A component of pathology data used to assess safety
• SEND-DART (Developmental and Reproductive Toxicology) – A related set of standards specific to the study of the effects of drug candidates on developmental and reproductive health

SEND ensures nonclinical findings are consistently presented and easily interpretable, which in turn facilitates regulatory agency decisions on the safety of a drug candidate proceeding into human clinical trials.

Clinical Data Acquisition Standards Harmonization (CDASH)

Clinical Data Acquisition Standards Harmonization (CDASH) defines standards for data collection at the clinical trial site. CDASH ensures data collected during a clinical trial is standardized from the very beginning.

CDASH specifies the standard data collection fields and formats that should be used in case report forms (CRFs). These include:

• Demographics – Standard fields for collecting demographic data
• Adverse Events – Standardized fields for recording adverse events
• Concomitant Medications – Standard formats for capturing medication data

By standardizing data collection, CDASH improves the quality and consistency of the data that’s subsequently transformed into SDTM datasets. This standardization minimizes errors and ensures data is comparable across different studies and sponsors.

Controlled Terminology

Controlled Terminology refers to the standardized set of terms used across all CDISC standards. This includes predefined lists of allowable values for certain fields, ensuring consistency in data reporting.

Controlled Terminology includes dictionaries for various data points, such as:

• MedDRA (Medical Dictionary for Regulatory Activities) – Standardized medical terminology for adverse event reporting
• WHO Drug Dictionary – Standardized terms for drug names and classifications

Using Controlled Terminology reduces ambiguity and enhances data quality by ensuring the same terms are used consistently across different studies and datasets.

CDISC standards, including SDTM, ADaM, SEND, and Controlled Terminology, play a pivotal role in the clinical trial process. They ensure data is collected, structured, and analyzed in a standardized way, facilitating regulatory review, data sharing, and comparison across studies. By adopting these standards, the clinical research industry can improve efficiency, enhance data quality, and ultimately accelerate the development of new therapies.

As clinical trials become increasingly complex and global, the role of CDISC standards will continue to grow, underscoring the importance of understanding and implementing these standards in every aspect of clinical research.

Rancho Biosciences has vast experience in helping clients manage their life sciences data, such as building data models for clinical and genomic data, developing data governance guidelines, and application of industry-standard ontologies and standards, including CDISC for various data types. At Rancho Biosciences, our mission in data management is about more than just navigating the complexities of data. It’s about empowering our clients to realize their goals in the life sciences sector. Rancho BioSciences can help you with all your data management and analysis needs. We are a data science services company that can provide you with expert biotech data solutions, bioinformatics services, data curation, AI/ML, and more. Don’t hesitate to reach out to us today to learn how we can help you save lives through data.