2.1 New Domains and Variables Introduced in Version 3.3

Version 3.3 introduces new domains such as Pharmacogenomics (PGx) and Genetics, focusing on biospecimen-related data. It also expands Morphology and Physiology domains, adding variables like ETHNIC and RACE for enhanced data capture. These updates align with regulatory requirements and improve data standardization in clinical trials, ensuring better organization and submission processes for regulatory authorities;

2.2 Enhanced Dataset Structures and Formats

SDTMIG 3;3 introduces improved dataset structures, including standardized formats for new domains like Pharmacogenomics (PGx) and Genetics. Core variables are clearly defined, ensuring consistency across datasets. Enhanced permissible values and data types provide clarity, while updates to ARM and ACTARM variables improve data derivation. These changes ensure datasets are more organized, facilitating regulatory submissions and promoting data standardization across clinical trials;

History and Development of SDTMIG

Developed by CDISC, SDTMIG 3.3 was released on November 20, 2018, as part of the organization’s efforts to standardize clinical trial data. It supersedes prior versions, ensuring consistency and compliance in data submissions.

3.1 Evolution from Previous Versions (e.g., SDTMIG 3.2)

SDTMIG 3.3, released on November 20, 2018, builds upon version 3.2, published in 2013. It introduces expanded morphology and physiology domains, enhanced dataset structures, and updated ARM and ACTARM variables. The guide now exceeds 600 pages, providing detailed derivation rules and improved clarity for regulatory submissions. These enhancements ensure better standardization and compliance in clinical trial data management.

3.2 Release Date and Relevance of SDTMIG 3.3

SDTMIG 3.3 was released on November 20, 2018, superseding previous versions. It provides updated standards for clinical trial data, ensuring compliance with regulatory requirements. The guide is essential for standardized data submission, offering enhanced clarity and detailed rules for dataset development. Its relevance lies in its ability to streamline data interchange and improve efficiency in clinical trial reporting, making it a critical resource for the industry.

Key Changes in SDTMIG 3.3

SDTMIG 3.3 introduces new morphology and physiology domains, expands pharmacogenomics datasets, and updates ARM and ACTARM variables. It enhances dataset structures and improves regulatory compliance standards significantly.

4.1 Expansion of Morphology and Physiology Domains

SDTMIG 3.3 introduces expanded morphology and physiology domains, enhancing the ability to capture detailed clinical data. These domains now include standardized variables for physiological measurements and morphological observations, ensuring consistency across studies. This update supports more precise data analysis and reporting, aligning with regulatory expectations for clinical trial submissions. The expanded domains also facilitate better integration of diverse datasets, improving overall study outcomes and compliance with global standards.

4.2 Updates to ARM, ACTARM, and Related Variables

SDTMIG 3.3 introduces refined rules for deriving ARM (Analysis Reference Month) and ACTARM (Actual Analysis Reference Month) variables. These updates ensure consistency in dataset creation, with ARM/ARMCD values restricted to those defined in the TA domain or set to null. The exception applies to study-specific scenarios, enhancing data accuracy and regulatory compliance. These changes streamline data submission processes and improve traceability in clinical trial analyses.

Technical Specifications of SDTMIG 3.3

SDTMIG 3.3 provides detailed data type definitions and permissible values, ensuring consistency in dataset development. It distinguishes core and non-core variables, guiding precise data structuring and standardization.

5.1 Data Type Definitions and Permissible Values

SDTMIG 3.3 defines specific data types and permissible values to ensure consistency across clinical datasets. These specifications standardize variables, reducing errors and enhancing data quality. By adhering to these rules, datasets align with regulatory expectations, facilitating seamless data exchange and submission. This section is crucial for maintaining uniformity and accuracy in clinical trial data, ensuring compliance with global standards and supporting efficient analysis.

5.2 Core and Non-Core Variables in Dataset Development

SDTMIG 3.3 distinguishes between core and non-core variables, ensuring datasets meet regulatory standards. Core variables are essential for all datasets, while non-core variables are conditional, depending on study-specific requirements. This distinction aids in standardizing data collection, ensuring completeness and consistency. It also facilitates efficient data review and analysis, aligning with regulatory expectations and promoting accurate submissions. This approach streamlines dataset development, enhancing overall data quality and compliance.

Implementation Guidelines

SDTMIG 3.3 provides detailed guidance on dataset development, including derivation rules for ARM and ACTARM variables, and best practices for submitting compliant datasets to regulatory authorities.

6.1 Derivation Rules for ARM and ACTARM Variables

SDTMIG 3.3 details derivation rules for ARM and ACTARM variables, ensuring consistency in dataset creation. ARM/ARMCD and ACTARM/ACTARMCD in DM must use values from TA domain or be null, with exceptions for study arms not covered by TA. The guide provides clear instructions to maintain data integrity and regulatory compliance, aiding in accurate dataset submissions.

6.2 Best Practices for Dataset Submission

New Datasets in SDTMIG 3.3

SDTMIG 3.3 introduces new datasets, including Pharmacogenomics (PGx) and Genetics data, as well as Biospecimen-related datasets, enhancing the scope of clinical trial data representation and standardization.