Header image for the post titled SDTM vs. CDASH: Why do we need two standards?

In the realm of clinical research and data management, adherence to standardized formats and structures is paramount. Two such standards, CDASH (Clinical Data Acquisition Standards Harmonization) and SDTM (Study Data Tabulation Model), play crucial roles in ensuring consistency and interoperability across clinical trial data. There is a lot of overlap between CDASH and SDTM, which often creates a false impression that they serve the same purpose, or even compete with each other. In reality, the two standards are designed to complement each other, while addressing similar but distinct needs. In thi note, we delve into the key differences between these two standards to better understand their respective roles in the clinical research landscape.

CDASH: Streamlining Data Collection

CDASH focuses primarily on data collection during the clinical trial process. It provides a standardized set of data collection fields, forms, and guidelines to facilitate the uniform capture of clinical trial data across different studies and sites. CDASH aims to improve efficiency, reduce errors, and enhance data quality by establishing a common language for data collection.

Key Features of CDASH:

  1. Standardized Data Collection Forms: CDASH provides standardized data collection forms known as Case Report Forms (CRFs), which outline the specific data points to be collected during a clinical trial. These CRFs are designed to capture essential information relevant to the study protocol.

  2. Data Collection Guidelines: CDASH offers guidelines for data collection, defining terminology and specifying how data should be recorded and organized. By adhering to these guidelines, researchers ensure consistency and clarity in their data collection processes.

  3. Interoperability: CDASH promotes interoperability by enabling data exchange and integration between different systems and platforms. This interoperability facilitates data sharing and collaboration among researchers, sponsors, and regulatory authorities.

SDTM: Standardizing Data Tabulation and Submission

While CDASH focuses on data collection, SDTM comes into play during the analysis and submission phases of a clinical trial. SDTM provides a standardized framework for organizing and formatting clinical trial data to support regulatory submissions to agencies.

Key Features of SDTM:

  1. Tabulation Model: SDTM defines a tabulation model that structures clinical trial data into standardized domains, variables, and datasets. This structured format facilitates data analysis, review, and regulatory evaluation.

  2. Data Mapping: SDTM requires mapping of collected data from CDASH CRFs to SDTM domains and variables. This mapping process ensures that data collected according to CDASH standards are transformed into a format compatible with SDTM.

  3. Regulatory Compliance: SDTM compliance is essential for regulatory submissions. Regulatory agencies expect clinical trial data to be organized and formatted according to SDTM standards to facilitate review and analysis.

Key Differences

SDTM is geared towards organizing finalized CRF data in a structured format conducive to data transmission, review, and reuse. In contrast, CDASH emphasizes user-friendly data collection methods to enhance data quality and seamless integration with SDTM.

For instance, when considering missing data, SDTM assumes cleanliness and doesn’t include placeholders for missing data. Conversely, CDASH acknowledges the absence of evidence is not evidence of absence, necessitating verification of missing data. This is exemplified in CDASH’s inclusion of indicator questions like “Were any adverse events experienced?”

Furthermore, non-standard variables are managed differently. While SDTM segregates them into separate datasets linked via RELREC, CDASH incorporates them within the parent data. Additionally, CDASH prioritizes human-readable data formats, while SDTM focuses on machine-readability for enhanced reusability.

Data organization also varies; CDASH allows flexibility in grouping variables across domains on CRFs, while SDTM mandates strict domain-based organization. Similarly, SDTM favors vertical data structures, whereas CDASH may require horizontal data structuring. This stems from the fact that it may not be possible to generate all encompassing codelists during the study, before all of the data has been collected. Hence, CDASH allows horizontal data structures which maintain information relationships without the need for coding or normalization. In SDTM, proper data coding and normalization is expected.

Overall, while CDASH ensures data traceability and quality, it seamlessly integrates with SDTM. Utilizing both standards optimizes data flow, ensures consistency, and enhances data quality from collection to analysis and beyond the initial study.