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One of the essential components in the pharmaceutical and biotechnology industries that researchers and programmers use when analyzing data during clinical trials is the Study Data Tabulation Model (SDTM). SDTM is the standards set by the Clinical Data Interchange Standards Consortium (CDISC) that aims to organize and structure datasets and submissions to the United States Food and Drug Administration in the United States and Pharmaceuticals and Medical Devices Agency in Japan.
In this article, we’ll discuss SDTM and a quick guide to its implementation.
What Is STDM?
As mentioned, CDISC created STDM to provide clinical research organizations with a standard for formatting and organizing data. Aside from clinical data, it is also used as a standard in non-clinical data, medical devices, and genetics/pharmacogenomics studies.
Essentially, implementing SDTM aims to streamline data collection, analysis, and reporting. This can help provide clear descriptions and attributes of each dataset to the regulating bodies, especially since these datasets or domains are collected in clinical trials following different approaches, depending on the preferences of contract research organizations, agencies, and sponsors. With SDTM, data collected can be easily identified and reviewed since it follows a standard format.
The STDM Implementation Guides
The Study Data Tabulation Model supports several implementation guides (IG). This means that each implementation guide references the SDTM version it’s associated with. Multiple IGs can reference one SDTM version, depending if the IG content requires a model update. Therefore, SDTM and IG versions have a direct relationship.
The most recently published SDTM is version 3.4, last November 2021. In this most recent SDTM version, two domains were added, the Genomics Findings (GF) and Cell Phenotype Findings (CP), and the morphology (MO) has been decommissioned. The Subject Visits (SV) domain contains additional variables and information for each subject’s visit.
The SDTM Implementation Approaches
SDTM implementation involves different approaches and relevant technology. The three alternatives to SDTM standard implementation in data collection include the following:
- Pure SDTM Approach
This SDTM approach is the most comprehensive, using the same terminology within and outside the company. Because data is compatible across clinical trials, combining the data for submission and analysis is easier. Moreover, pure SDTM centralizes controlling and upgrading the standard to one organization.
However, this approach makes data issue detection harder because data resides in different domains. Developing an internal database and data collection system is expensive and time-consuming. There’s also a risk of losing a company-specific submission advantage.
- Submission-Only Approach
This approach is the most cost-effective way to implement SDTM. The advantages of a submission-only approach include that most clinical staff members don’t need to deal with SDTM’s complexity, and the current systems and processes remain intact. It only requires clinical trials that are ready for submission.
However, delays in submission may occur, and it requires maintaining two sets of datasets, one for submission and another for internal use. Furthermore, potential miscommunication between the sponsor and agencies may occur if the sponsor’s staff is unfamiliar with SDTM terminologies.
- Database-Only Approach
This approach is a hybrid SDTM implementation because it allows using any cost-effective process and system to collect clinical trial data. Database-only approach also supports medical terminology standards to industry standards. In addition, this approach also controls and upgrades standards in a centralized, single organization, facilitating data combinations from several sources.
However, building a new database can be expensive and time-consuming. There’s also a need to re-engineer processes for new database enabling. There’s also a risk of losing the company-specific submission advantage built for years.
The SDTM Implementation Process
It’s important to understand the basic terms SAS and CDR before discussing the actual SDTM implementation process.
The role of the Statistical Analysis System (SAS) in clinical studies is critical. This has been the industry’s data management and analytics standards when handling large data volumes. SAS is a flexible platform providing users with different ways to analyze, process, handle, and report on data. On the other hand, a clinical data repository (CDR) is an aggregation of patient-centric health data gathered from several IT systems, supporting multiple uses.
Here’s the general SDTM implementation process:
- Start the SDTM implementation with the electronic case report form (eCRF) for electronic data capture (EDC).
- The next step is mapping the patient data to the SDTM format before loading it into the Clinical Data Repository (CDR).
- The CDR generates derived variables depending on business logic or rules, which are stored in the system as metadata (e.g., vaccine formulation stored as metadata).
- The metadata application comes next, and the data is converted from the CDR to SAS SDTM and SDTM+ datasets, each representing an SDTM domain.
- Once data extraction is complete, analysis and reporting on a SAS platform follow.
Implementing SDTM involves following the right format, approach, and process based on the datasets and the preferences of the agencies and sponsors. The process requires standardization and collaboration to avoid confusion and errors. In that way, clinical trials will become more successful, benefiting more patients with different medical conditions in the shortest possible time.