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Building Effective Clinical Data Strategies for Complex Clinical Trials

The landscape of clinical trials is gaining complexity by the day, driven by sophisticated trial designs, massive volumes of data, and the integration of broadly diverse data sources. This complexity necessitates innovative strategies and technologies to manage and leverage data effectively. During a recent Xtalks webinar, the Biometrics Services team at eClinical Solutions shared insights on navigating these challenges through strategic data management, advanced technology, and cross-functional collaboration.

Understanding Complexity in Clinical Trials

What is driving this increasing complexity in clinical research? The team discussed a few key reasons, including:

  1. Protocol Complexity: Aside from therapeutic areas like rare diseases that commonly require more complex study designs, modern clinical trials can also involve:
    • Master protocols (testing multiple therapies and/or patient populations under a single protocol)
    • Adaptive designs (protocols that can change based on new data without reducing the quality or validity of findings)
    • Multiple study arms

Or some combination of all these, necessitating more intricate data collection approaches.

  1. Data Volume and Variety: The sheer volume and variety of data, including novel data types like biomarkers and real-world evidence, as well as an increasing amount of data collected remotely (e.g., ePROs, wearable device data, etc.) have increased significantly.
  2. Operational Complexity: Longer trial durations, increasing number of sites, and an increasingly diverse vendor ecosystem add another layer of complexity​ for teams to navigate.

Addressing Study Complexity with Technology

Leveraging advanced technologies is playing an increasingly important role in addressing the growing complexity of clinical trials. For example, flexible and scalable data platforms like elluminate® allow study teams to handle external data workflows more seamlessly. For electronic data capture, taking a strategic and agile approach to database design to accommodate increasingly common protocol amendments can help teams improve both efficiency and data quality.

Real-time data access is also beneficial. Having a robust clinical data platform in place allows for ongoing data cleaning and review and near immediate access to insights and analytics. This capability is particularly useful for adaptive trials that require rapid decision-making based on high-quality data.

A shift towards more sophisticated statistical methods, including Bayesian and adaptive designs, is becoming increasingly necessary to manage patient-specific trials. These methods, combined with real-time data integration, enable more precise and timely analyses.

Operational Complexity Matters

Operational complexities are increasing as other aspects of research also become more complicated. Put simply, the more challenging the research process, the more challenging it will be to keep everyone on the same page. It is helpful to clearly define – and sometimes redefine – the roles of each team member. Think ahead about what is needed to make sure that all stakeholders can communicate clearly and easily, and that everyone is aligned from the outset. This approach helps mitigate risks and streamline processes, ensuring that trials stay on track despite their complexity.

For example, close collaboration between data management and statistical teams is crucial for proactive issue resolution. Involving statisticians as early as possible in the process can significantly reduce the kinds of last-minute surprises that can grind studies to a halt, while improving the overall quality of data analysis.

Clinical programmers are a good example of roles that are evolving as the needs of trials change. Modern studies require them to have a deeper understanding of protocol designs and programmers must now work more closely with other functions to ensure data is collected and managed efficiently. This cross-functional engagement is essential for building robust data systems that can handle the intricacies of more complex modern trials.

Integration of AI and ML for Complex Trials

Looking ahead, the use of technology to address trial complexity will only increase, particularly with the growing use of artificial intelligence (AI) and machine learning (ML) as we continue to discover new ways for these technologies to make what we do more efficient. As an example, data managers will likely work alongside “virtual” AI data managers, leveraging the technology to handle routine tasks so that they can focus on the more strategic aspects of data management.

Tackling Trial Complexity is Less Complex with the Right Mindsets and Technologies

The complexities of modern clinical trials demand robust and innovative data strategies. By leveraging advanced technologies, fostering cross-functional collaboration, and implementing strategic data management practices, the challenges posed by intricate trial designs and diverse data sources can be effectively addressed. Solutions that combine the ability to access data in real time, integrate automation of non-strategic data management tasks, and the integration of sophisticated statistical methods are great places to start for researchers navigating these complexities.

Gain insights from our Biometrics Services team on their experiences handling trial complexity by catching up on the webinar recording of ‘Clinical Data Strategy for Complex Clinical Trials’.

Learn how the elluminate Clinical Data Cloud and the eClinical Solutions Biostatistics experts can help you address clinical trial complexity.

ENGAGE 2024- October 10-11, Boston.

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