Summary of Key Differences between each SDTM IG versions

Comparison of SDTM Implementation Guide (IG) Versions: 3.1.1 vs 3.1.2 vs 3.1.3 vs 3.2 vs 3.3 vs 3.4

The Study Data Tabulation Model (SDTM) Implementation Guide (IG) is updated periodically to incorporate new standards and improve existing ones. Below is a comparison of the key differences and updates across the SDTM IG versions from 3.1.1 to 3.4.

SDTM IG 3.1.1

• Initial Introduction: SDTM IG 3.1.1 was one of the earlier versions that laid the foundation for standardizing clinical trial data for regulatory submissions.
• Core Domains: Introduced essential domains like DM (Demographics), AE (Adverse Events), and LB (Laboratory), which became the standard for clinical trial data submission.
• Basic Structure: Established the general structure for SDTM domains, including the use of standardized variable names and controlled terminology.

SDTM IG 3.1.2

• Minor Revisions: SDTM IG 3.1.2 included minor updates and clarifications to existing standards without introducing significant changes.
• Additional Controlled Terminology: Enhanced the controlled terminology lists, improving consistency and standardization across datasets.
• Introduction of New Domains: Introduced new domains such as SC (Subject Characteristics) and MS (Microbiology Specimen), expanding the range of supported data types.

SDTM IG 3.1.3

• Clarifications and Corrections: Addressed ambiguities in the previous versions, providing clearer guidelines on specific domains and variables.
• New Variables: Added new variables in existing domains to capture more detailed information, such as AESEV (Adverse Event Severity) in the AE domain.
• Enhanced Metadata Documentation: Improved the requirements for metadata documentation, emphasizing the importance of the define.xml file.

SDTM IG 3.2

• Significant Updates: SDTM IG 3.2 introduced several new domains and revised existing ones, reflecting the evolving needs of clinical trial data management.
• New Domains: Introduced key domains such as MB (Microbiology), TU (Tumor Identification), TR (Tumor Response), and RS (Response Evaluation), particularly for oncology studies.
• Standardization of Date/Time Variables: Improved standardization for handling date and time variables across domains.
• Introduction of Supplemental Domains: Expanded the use of the SUPP-- (Supplemental Qualifiers) structure to accommodate non-standard data.

SDTM IG 3.3

• Further Domain Expansion: SDTM IG 3.3 introduced additional domains, particularly focused on new therapeutic areas and specific types of clinical data.
• New Domains: Added domains like DD (Death Details), DI (Device In-Use), and RELREC (Related Records) for better data linkage and tracking.
• Refinement of Oncology Domains: Enhanced the oncology-specific domains introduced in IG 3.2, such as TU, TR, and RS, to better capture complex oncology data.
• Improved Examples and Guidance: Provided more detailed examples and guidance on how to implement the standards in various clinical scenarios.

SDTM IG 3.4

• Latest Enhancements: SDTM IG 3.4 is the most recent version, incorporating feedback from previous implementations and further refining the standards.
• New and Updated Domains: Introduced new domains like QS (Questionnaires) and improved existing ones, particularly in the area of device data and pharmacogenomics.
• Digital Health Data: Added guidance on handling digital health data, reflecting the increasing use of digital devices in clinical trials.
• Increased Emphasis on Traceability: Enhanced focus on ensuring traceability from source data to SDTM datasets, emphasizing the importance of clear documentation and metadata.
• Additional Controlled Terminology: Expanded the controlled terminology lists to include new terms relevant to emerging therapeutic areas.

Summary of Key Differences

The evolution of the SDTM Implementation Guide from version 3.1.1 to 3.4 reflects the growing complexity of clinical trials and the need for more detailed and standardized data capture. Each version has built on the previous ones, introducing new domains, refining existing ones, and expanding the use of controlled terminology. The most recent versions, particularly 3.3 and 3.4, have focused on oncology data, device data, and the incorporation of digital health data, ensuring that SDTM remains relevant in the face of technological advancements in clinical research.

As the SDTM IG continues to evolve, it is crucial for clinical programmers to stay updated on the latest standards and best practices to ensure compliance and maintain the integrity of clinical trial data.

Detailed Comparison of SDTM IG Versions 3.2 vs 3.3 vs 3.4

The Study Data Tabulation Model (SDTM) Implementation Guide (IG) is periodically updated to reflect advancements in clinical research and to incorporate feedback from its use in regulatory submissions. This report highlights the key differences and updates between SDTM IG versions 3.2, 3.3, and 3.4, with specific examples to illustrate these changes.

SDTM IG Version 3.2

• Introduction of Oncology Domains: Version 3.2 marked a significant update with the introduction of domains specific to oncology studies:
  • TU (Tumor Identification): Used to identify and categorize tumors.
    • Example: The TU domain includes variables like TUSTRESC (Tumor Identification Standardized Result) and TULOC (Tumor Location), which were not present in earlier versions.
  • TR (Tumor Response): Captures tumor response assessments.
    • Example: The TR domain introduced variables such as TRTESTCD (Tumor Response Test Code) and TRORRES (Tumor Response Original Result) to record response details, like partial response or progressive disease.
  • RS (Response Evaluation): Used for recording the overall response evaluation, particularly in oncology trials.
    • Example: The RS domain includes variables like RSORRES (Response Evaluation Original Result) to capture overall response such as "Complete Response" or "Stable Disease".
• New Domains: Several new domains were introduced, including:
  • MB (Microbiology): Captures microbiological data.
    • Example: The MB domain introduced variables like MBTESTCD (Microbiology Test Code) and MBORRES (Microbiology Original Result), allowing for detailed tracking of microbiological findings such as bacterial culture results.
  • MS (Microscopic Findings): Records findings from microscopic examinations.
    • Example: Variables such as MSTESTCD (Microscopic Test Code) and MSORRES (Microscopic Original Result) were introduced to capture detailed histopathological results.
  • PR (Procedures): Captures information about medical procedures performed during the study.
    • Example: The PR domain included variables like PRTRT (Procedure Name) and PRSTDTC (Procedure Start Date/Time) to document surgical interventions and other procedures.
  • RELREC (Related Records): Establishes relationships between records in different domains.
    • Example: The RELREC domain was enhanced to support complex relationships between datasets, such as linking an adverse event with a concomitant medication record.
• Standardization of Date/Time Variables: Version 3.2 improved the standardization of date and time variables across domains, using ISO 8601 formats for consistency.
  • Example: Variables like --STDTC (Start Date/Time) and --ENDTC (End Date/Time) were standardized to ensure uniform reporting of temporal data.
• Enhanced Metadata Documentation: Emphasized the importance of comprehensive metadata documentation, particularly in the define.xml file, to ensure data traceability and clarity.
  • Example: The define.xml file became more robust in version 3.2, with improved requirements for documenting variable derivations, controlled terminology, and value-level metadata.

SDTM IG Version 3.3

• Further Expansion of Oncology Domains: Building on the oncology domains introduced in version 3.2, version 3.3 further refined these domains, particularly for more complex oncology data:
  • Expanded definitions and examples for TU, TR, and RS domains to better accommodate the variety of tumor assessments and responses encountered in oncology trials.
    • Example: Version 3.3 included additional guidance on managing longitudinal tumor data, such as handling changes in tumor location or size over multiple assessments.
• Introduction of New Domains: Version 3.3 added several new domains to cover additional clinical data types:
  • DD (Death Details): Captures detailed information about the circumstances and cause of death.
    • Example: The DD domain introduced variables like DDTESTCD (Death Test Code) and DDORRES (Death Original Result), allowing for detailed documentation of death-related events, such as "Sudden Cardiac Death."
  • DI (Device In-Use): Records data about medical devices used during the study.
    • Example: The DI domain introduced variables like DITESTCD (Device Test Code) and DIORRES (Device Original Result), capturing information about device usage, functionality, and related findings.
  • RP (Reproductive System Findings): Captures findings related to the reproductive system.
    • Example: The RP domain includes variables like RPTESTCD (Reproductive Test Code) and RPORRES (Reproductive System Original Result), capturing data from reproductive health assessments, such as fertility evaluations or pregnancy outcomes.
• Device Data Standardization: The DI (Device In-Use) domain was introduced to accommodate data related to medical devices, reflecting the growing use of devices in clinical trials.
  • Example: The DI domain included specific guidance on documenting device malfunctions, interventions, and outcomes, ensuring that all device-related data is captured consistently across studies.
• Refinement of Existing Domains: Version 3.3 included updates to existing domains, with more detailed guidance and examples provided to improve consistency and accuracy in data submission.
  • Clarified usage of the SUPPQUAL domain for supplemental qualifiers, ensuring that non-standard variables are correctly linked to their parent domains.
    • Example: Enhanced the documentation for how to properly use QNAM and QLABEL in the SUPPQUAL domain to maintain data consistency and traceability.
  • Enhanced the guidance for the use of RELREC (Related Records) domain to better manage complex relationships between different data points.
    • Example: Provided examples of how to link related records across different domains, such as linking an ECG result with a concurrent medication record in CM.
• Expanded Controlled Terminology: Version 3.3 further expanded the controlled terminology lists, ensuring that emerging clinical data types are adequately captured and standardized.
  • Example: New terms were added to capture advanced diagnostics and treatment modalities, such as immunotherapies and next-generation sequencing results.

SDTM IG Version 3.4

• Focus on Digital Health and Wearable Data: Reflecting the increased use of digital health technologies, version 3.4 introduced new guidance on handling data from wearable devices and other digital health technologies:
  • Guidance on incorporating digital health data into existing SDTM domains or creating new domains where necessary.
    • Example: Provided guidelines for integrating continuous glucose monitoring data into the LB (Laboratory) domain, including how to handle high-frequency data points.
• Introduction of New Domains and Updates: Version 3.4 continued the trend of expanding and refining SDTM domains:
  • QS (Questionnaires): Expanded to include more detailed guidelines for handling complex questionnaire data, especially in therapeutic areas like mental health.
    • Example: The QS domain now includes guidance on managing multi-part questionnaires, where different sections may have different scaling or scoring methods.
  • DD (Death Details): Refined to capture even more detailed data on death events, including timing relative to study treatment and follow-up periods.
    • Example: Enhanced documentation on how to capture death events that occur during long-term follow-up, ensuring that the context of the death (e.g., treatment-related, post-treatment) is clearly documented.
• Enhanced Traceability: Version 3.4 emphasized the importance of traceability from source data to SDTM datasets, providing more detailed guidance on maintaining clear and consistent documentation throughout the data lifecycle:
  • Included additional requirements for metadata and define.xml files to improve the transparency and traceability of data transformations.
    • Example: Provided specific examples on documenting derivations in define.xml, ensuring that each variable’s origin and transformation process are fully transparent to reviewers.
• Further Refinement of Oncology Domains: Continued to refine oncology-specific domains (TU, TR, RS) to ensure they meet the needs of increasingly complex oncology trials:
  • Improved guidance on managing tumor response data, particularly in studies involving multiple treatment lines or combination therapies.
    • Example: Updated the TR domain with guidance on how to handle tumor response in cases of crossover study designs or when a subject receives multiple therapies sequentially.
• Expanded Guidance on Controlled Terminology: Further expanded controlled terminology to include new terms relevant to emerging therapeutic areas and technologies.
  • Example: Added terms related to digital biomarkers, pharmacogenomics, and other advanced therapeutic areas to ensure that these data types can be standardized across studies.

Summary of Key Differences Between Versions 3.2, 3.3, and 3.4

Each subsequent version of the SDTM IG has built upon the previous one, introducing new domains, refining existing ones, and expanding the scope to accommodate the latest trends in clinical research. The examples provided illustrate the specific changes and enhancements that have been made in each version.

• Version 3.2: Focused on introducing new domains, particularly for oncology studies, and improving standardization across datasets. Introduced key oncology domains and improved standardization of date/time variables.
• Version 3.3: Expanded the range of domains, particularly for device data and reproductive system findings, and further refined the oncology-specific domains introduced in 3.2. Also introduced detailed guidance on the use of supplemental qualifiers and related records.
• Version 3.4: Emphasized digital health and wearable data, enhanced traceability, and continued to refine oncology domains, making it the most comprehensive and up-to-date version. Focused on the integration of digital health data and the further expansion of controlled terminology.

As clinical research evolves, the SDTM IG will continue to be updated to ensure that it remains relevant and useful for capturing the increasingly complex data generated by modern clinical trials.