Data Management for Clinical Studies
For homogeneous data collection in clinical studies, the DZD has defined a standardized core data set for diabetes research. This data set is recorded in DZD studies and enables trans-study evaluation of clinical data. In addition, the DZD Research Data Management supports the DZD study centers in the uniform recording of clinical data and samples while at the same time complying with data privacy. In order to harmonize data from different sources, the DZD uses the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).
DZD Core Data Set
The partners in the DZD have agreed on a common DZD Core Data Set (DZD CDS) for clinical parameters, which is based on international IT and terminology standards. This is a central requirement for comprehensive joint data collection and use.
The DZD Core Data Set consists of 8 modules and a total of 147 parameters that are defined interdisciplinary. There are also optional parameters that are subject-specific and can be included in data analyses depending on the research question.
The DZD basic dataset establishes definitions for the collection of diabetes-relevant clinical parameters and thus makes a valuable contribution to diabetes research and forms the basis for future progress in the treatment and prevention of this widespread disease.
FAIR criteria and update: The DZD Core Data Set was revised in 2022 according to the FAIR criteria. These criteria emphasize the importance of data that is findable, accessible, interoperable and reusable. The update ensures that the dataset meets the highest standards and fulfills current research requirements.
In 2024, the Core Data Set of the German Centers for Health Research (DZG) was integrated into the DZD Core Data Set. Basic clinical parameters are thus recorded in all DZGs according to a definition, regardless of indication.
The focus of the DZD CDS is on prediabetes, type 1 and type 2 diabetes, adults, metabolic issues and pathophysiology of metabolism.
Collection of Data from Clinical Studies
The DZD Research Data Management accompanies clinical studies in diabetes and metabolic research from planning to evaluation.
Structured Data Collection
Competent planning and structuring of data collection at the start of a study is crucial to its success. The DZD Core Data Set is a central component in this process. The DZD Research Data Management also supports the preparation of study documents that are relevant for data management. Examples include detailed data dictionaries (data catalogs) and templates for standard operating procedures that ensure systematic and complete data collection. These documents are essential for the standardization of processes and ensure that all steps required while conducting a study meet the necessary quality and compliance standards.
Implementation of Quality Controls
Consistency and comparability of research data are the keys to efficient primary use of research data and to its far-reaching subsequent use. The optimal conditions that are needed are achieved by defining and adhering to data and quality standards. The DZD Research Data Management works with established electronic data collection software such as REDCap to implement automated validation. This allows potential errors to be detected and corrected as early as during data entry, which significantly improves data integrity.
FAIRification
For translational research, data should not only be visible, but also compatible and reusable. The key word in efficient data processing is FAIRification. FAIR stands for the central characteristics of data processing, which are “Findable”, “Accessible”, “Interoperable”, and “Reusable”. Applying these principles ensures that research data is not only carefully collected and managed, but can also be used in the long term and in a variety of ways. The transparent description of the DZD study projects and study results, together with the publication of the DZD basic dataset under an open license, contributes to the expansion of research possibilities and supports the use of secondary data.
In order to relate data to each other in a comprehensive and translational way, it is important to harmonize the heterogeneous data from the different sources and thus make it interoperable. To achieve this, the DZD uses the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The OMOP standard guarantees increased research efficiency through the ability to analyze data from different sources.
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