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Important Software Tools for Clinical Study Data Management

From collection and analysis to publishing and archiving, software tools for managing clinical study data exist for the entire lifecycle of a study. Some tools manage the full study process, while others tackle specific challenges. While designing and selecting software for your study, you should look at the strengths and weaknesses of each tool you consider, while considering the specific requirements of your study and any gaps in your data management resources.

Each study is unique, and so are the corresponding data management needs. Depending on your organization, you may have custom built study resources already in place, you may be starting study management from scratch, or you may find you only need to adopt one or two software tools to fulfill the data management needs of your study. More holistic software solutions such as Clinical Data Management Systems (CDMS) cover the full spectrum of data management for clinical studies, combining many tools into a single integrated system.

During your evaluation, ensure that the tools and resources you choose are appropriate for the regulations your study falls under.


Contents: Data Capture | Analysis and Visualization | Data Repositories | CDMS


Data Capture Tools

Data capture tools are crucial for the effective management of clinical trial data, serving as the foundational step in ensuring data accuracy, integrity, and timeliness. They enable the direct and efficient capture of clinical data, significantly impacting the overall success of clinical trials.

  • Electronic Case Report Forms (eCRF): This tool allows participants to submit digital, often web-based, case report forms used in clinical trials, with features like form validation, data security, and data standardization.
  • Electronic Data Capture (EDC) Systems: EDC systems are software that capture and collect clinical trial data in electronic form, typically from an eCRF. These systems streamline data collection and can significantly reduce the time to collect data.
  • Electronic Patient Reported Outcomes (ePRO): Tools that allow patients to report outcomes directly via electronic devices, improving the accuracy and immediacy of patient-reported data.
  • Interactive Response Technology (IRT): This technology is used to manage patient interactions and drug supply in clinical trials, often integrating with other systems to ensure study integrity and efficiency.


Analysis and Visualization Tools

For the analysis phase, leveraging the right software tools for clinical study data management are crucial for extracting meaningful insights from the data collected during the trial. The choice should be driven by the specific requirements of the clinical study, including the complexity of data analysis, regulatory considerations, the technical proficiency of the team, and budget constraints. Integrating the right mix of analysis and visualization tools into the clinical study data management plan can significantly enhance the efficiency and insights gained from the study data.

  • R Studio: An integrated development environment (IDE) for R, a programming language for statistical computing and graphics. It is particularly favored in academic and research settings for its extensive range of packages and flexibility in handling complex statistical analyses. R Studio facilitates data manipulation, calculation, and graphical display.
  • Tableau: A powerful data visualization tool that enables users to create interactive and shareable dashboards. It excels in transforming complex data sets into visually appealing and easily understandable charts and graphs. Tableau is particularly useful for presenting data findings to stakeholders who may not have a technical background.
  • PowerBI: A suite of business analytics tools from Microsoft that offers comprehensive data analysis and visualization capabilities. It allows users to connect to a wide array of data sources, simplify data prep, and drive ad hoc analysis.
  • Other options include: Python with Pandas and Matplotlib/Seaborn, SAS (Statistical Analysis System), SPSS (Statistical Package for the Social Sciences), Qlik Sense, KNIME


Clinical Data Repositories

Clinical data repositories, warehouses and lakes are real-time databases that consolidate data from a variety of sources such as patient demographics, radiology reports and images, test results and more. Data lakes are used when data needs to remain unstructured, while warehouses are employed for inherently structured data. These repositories play a critical role in the storage, integration, analysis, and secure sharing of both aggregated and raw data from clinical trials. Some solutions can be found open source, like LabKey Community Edition. In general, data repositories are foundational in supporting the data’s lifecycle, ensuring that data is accessible, secure, and ready for analysis.

  • Red Hat Ceph Storage: An open-source, highly scalable and flexible data storage solution that can act as a data lake for clinical studies. It’s designed to handle vast amounts of data, including unstructured data, which is common in clinical research.
  • Microsoft Azure Data Lake: Provides a highly scalable and secure data lake solution that integrates with Azure’s analytics services. With capabilities to process and analyze large quantities of data using AI and machine learning models, it’s suitable for clinical studies needing advanced data analysis.
  • Other options include: Dell EMC Isilon, Oracle Health Sciences Data Management Workbench, SAS Clinical Data Integration


Clinical Data Management Systems (CDMS)

A Clinical Data Management System is a tool specifically focused on managing the broad data needs of clinical trials. CDMS software facilitates the collection, cleaning, and management of study data, ensuring its quality and integrity for analysis. The scope of a CDMS is greater than other tools like EDC.

  • Data Collection and Capture: Advanced features for the efficient collection and electronic capture of clinical trial data, including integration with electronic data capture (EDC) systems and electronic case report forms (eCRFs).
  • Data Validation and Quality Control: Automated validation rules, ontology enforcement and quality control processes ensure data accuracy, completeness, and consistency, minimizing errors and discrepancies in clinical trial databases.
  • Query Management: Tools like SQL for generating, tracking, and resolving queries to address data inconsistencies or missing data, facilitating clear communication between data managers and clinical sites.
  • Data Cleaning and Curation: Capabilities for cleaning and curating data to prepare it for analysis, including automating deduplication, normalization, and transformation processes.
  • Database Locking and Audit Trails: Features for securing the database at the end of the data collection phase, along with comprehensive audit trails for tracking data changes, ensuring data integrity and regulatory compliance.
  • User Management and Security: Robust security protocols like role-based access and user management features to control access to sensitive data, protect patient privacy, and comply with data protection regulations.
  • Reporting and Data Export: Flexible reporting and analysis tools and data export options to generate interim and final reports, facilitating data interpretation and sharing with stakeholders.



LabKey CDMS is a toolset designed to organize and manage study data in a central, secure environment for data curation, analysis, collaboration and publishing. This product aims to address the specific needs of clinical study data management, from data harmonization to secure compliance to convenient collaboration.

Schedule a demo if you are interested in seeing LabKey CDMS in action.

Learn more about Clinical Study Data Management:

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CDMS vs EDC: What’s Right for Your Clinical Trial?

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