TITLE: SDTM datasets management


Data collection is the required procedure in each clinical trial observing humans or animals. To systemize this gathered data properly, standardized models such as STDM are widely used. Even though it might seem easy to organize the data from clinical trials, the utilization of dedicated software could considerably make it easier. This article provides an overview of SDTM datasets and the effective ways for their management.

What are SDTM datasets?

Previously, the data collected within clinical trials was recorded differently by various groups of researchers. Each of those used variables, tables, and other components for describing the course as well as the outcome of the clinical trial. It was sometimes difficult to understand those records for external specialists who were also interested in the research data.

More than a decade ago, the organization named Clinical Data Intercache Standards Consortium (CDISC) adopted new standards for clinical data records. One of those is SDTM, which stands for Study Data Tabulation Model. It defines the dataset structure and attributes for each clinical trial.

As during each clinical trial, lots of observations and subject characteristics are collected, they form up a dataset. In order to structure and systemize it properly, SDTM datasets are used. In fact, those are datasets with standard variables and names designed according to the SDTM specifications and rules.

Mapping data in SDTM datasets

Owing to the standard set of options and domains available under the SDTM datasets, the data mapping can be done faster. Using standard variables for describing the study observations, its scope and focus, along with the additional factors allow for effectively using data further.

When the data is mapped correctly in SDTM datasets, it could be easily identified, decoded, and analyzed by reviewers onwards. Moreover, creating SDTM datasets ensures consistency of the results provided by different clinical trials. Thus, the gathered data could be further reused in future clinical trials or complement the findings on the existing ones.

Software for creating and managing SDTM datasets

While the creation of SDTM-compliant datasets is theoretically possible, it might be a bit challenging within the practical realization. In some cases, coding skills would be even required for the creation and modification of SDTM datasets. However, there is dedicated software such as ryze that has all the implemented features needed to make the creation and management of the SDTM datasets.

Even if you are used to creating custom tables for recording the findings within the clinical trial, software tools will help to transform them into SDTM-compliant datasets. Also, it could help with the creation of SDTM datasets and managing data in it from the very start of the clinical trial observations.

Furthermore, such software programs for managing SDTM datasets usually offer an extended set of features useful for clinical trials. For instance, they provide the most recent data on the CDISC standards for data management in clinical trials. Also, it offers cloud solutions that make it possible to easily find and share information with your colleagues or other researchers about the clinical trial findings.

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