“Study Data Tabulation Model” is what SDTM stands for in the context of clinical research and SAS programming. To maintain uniformity and interoperability among various studies and sponsors, clinical trial data are organized and structured using the SDTM standard format. As part of a New Drug Application (NDA) or Biologics License Application (BLA), it dictates the structure and content of datasets that are submitted to regulatory bodies like the U.S. Food and Drug Administration (FDA).
Clinical trial data are represented by certain categories or kinds in SDTM domains. The study’s many components—such as demographics, adverse events, test findings, vital signs, medical history, etc.—are all represented by a specific domain. The variables (columns) and their properties (such as data type, format, and length), which should be contained in the dataset, are predefined in each domain’s predefined structure.
A popular software package for data management and statistical analysis is called SAS (Statistical Analysis System). Clinical SAS programming in the context of SDTM entails converting raw clinical trial data into SDTM-compliant datasets and subsequently running different analytics on these datasets.
For instance, you would use SAS programming to transform and map raw data for laboratory findings into the SDTM LAB domain’s structure, making sure that variables are properly titled, structured, and filled out in accordance with SDTM requirements. To comply with SDTM requirements, data must be cleaned, transformed, and standardized.
An abridged illustration of how SDTM domains could be applied in clinical SAS programming is given below:
Raw Data Collection: Raw data is gathered from a variety of sources, including case report forms and electronic health records.
Data transformation: The raw data are transformed and mapped into SDTM domains using SAS programming. Data from lab tests, for instance, may be converted into the SDTM LAB domain.
SDTM Domain Creation: Using preset SDTM domain structures, SAS code generates the SDTM datasets. For example, DM stands for demographics, AE for adverse events, and LB for laboratory findings. Each dataset will represent a certain SDTM domain.
Data validation: SAS programs may run tests to make sure the converted data complies with SDTM standards, pointing out any discrepancies or mistakes.
Analysis and Reporting: Once the SDTM datasets are ready, SAS may be used for statistical analysis, producing summary tables, lists, and graphs as needed for internal analysis or regulatory filings.
Keep in mind that while working with SDTM data, CDISC (Clinical Data Interchange Rules Consortium) rules and recommendations must be followed to guarantee the accuracy, reliability, and regulatory compliance of the clinical trial data.