At Signifience, our SAS programmers have an extensive experience in CDISC STDM/ADaM standards. Working closely with our Biostatisticans team, they are commited to build compliant databases and produce high quality TFLs to support your regulatory submission to FDA and other agencies.
The CDISC SDTM (Study Data Tabulation Model) domain datasets are a standard format for submitting clinical trial data to regulatory agencies such as the FDA. The SDTM is a set of guidelines and standards that specify how data should be organized and coded in order to be easily understood and analyzable.
We create SDTM domain datasets to provide case report tabulation (CRT) data to regulatory agencies in a standardized format, compatible with available software tools that allow efficient access and proper interpretation of submitted data.
ADaM (Analysis Data Model) specification and datasets are used in clinical trials to organize and structure the data for statistical analysis. They are typically created after the SDTM datasets are completed. ADaM datasets are designed to be used for the analysis of the data and are organized in a way that makes it easier to perform statistical analysis and create TFLs (Tables, Figures, Listing)
The ADaM specification includes information about the dataset structure, variable naming conventions, and data transformation rules.
This allows for the efficient and accurate analysis of the data and the creation of tables, listings, and figures that can be used to support the conclusions and findings of the trial.
Our SAS programmers are closely working with the Study Statisticians to adjust the ADaM dataset specification according to their requirement.
ISS and ISE are key components of regulatory submission package for clinical trials. They provide a clear and concise summary of the key findings from the trial, making it easier for regulatory agencies to understand and evaluate the safety and efficacy of the drug under study.
ISS help to identify adverses events (AE/SAE) and other safety concerns, where ISE provide combined efficacy data from various dataset sources, helping to increase the statistical power of new sub-analyses.
Our SAS programmers have a full experience in ISS/ISE developpement.
We routinely develop SAS macros that allow for automation and simplification of repetitive tasks.
They save time and reduce the risk of errors by allowing us to create standardized code that can be reused across different projects and analyses.
This is especially useful in the context of clinical trial data, where there are often many similar tasks that need to be performed repeatedly.
Using SAS macros also improve our efficiency and consistency of our data analysis and reporting processes, which is particularly important when working with regulatory agencies.