Clinical Data Management (CDM) is a crucial aspect of clinical research, ensuring the generation of high-quality, reliable, and statistically sound data from trials. It plays a key role in the biopharmaceutical industry, where effective management of clinical data can enhance the speed of drug development and commercialization, providing a competitive advantage. CDM is also essential for ensuring data integrity and quality, particularly in the context of evolving regulations and guidelines.
The evolution of clinical data management has been marked by the integration of legacy systems with electronic health record applications. Before the advent of new technological advancements in data storage, researchers relied heavily on paper-based systems to record clinical trial data. This system of data entry posed a lot of significant challenges in the areas of data entry, data storage, data retrieval, data cleaning, data review, data analysis, data presentation and data security.
The advent of the Electronic Data Capture (EDC) system changed clinical data management as it revolutionised the way trial data is collected, processed and analysed. This gold standard of clinical data management is a core service offered by Zilla Clinicals.
Clinical Data Management in 2024
In 2024, clinical data management is ever-increasing in relevance as there is an increasing demand for data as a corporate asset in the biopharmaceutical industry. Asides relevance, the role of the clinical data manager has now expanded from simply ensuring data validity to partaking partially or fully in various operational aspects of the clinical trial such as randomization, safety monitoring; generation of tables, listings and figures (TLFs), monitoring data trends and timelines monitoring.
Additionally, there has been an expansion in the quantity and quality of technical tools at the disposal of the clinical data manager. This expansion is important to keep up with the increased technical sophistication of clinical trials as a whole. In other words, the clinical data manager in 2024 has much more to learn and more to do. With these challenges and opportunities, the clinical data manager remains ever relevant especially in 2024. The reasons for this relevance are given below:
- Increase in volume of data collected in clinical trials: Per Medidata and a study by the Tufts Center for the Study of Drug Development, the volume of data generated in clinical trials has increased by over 30% per decade, over the last two decades. If this trend holds true (and it will), it implies that more data will be generated at a much quicker rate. This is good news as statistically stronger insights will be derived much quicker. However, to keep up with the ever-increasing data volume, the role of the Clinical Data Manager is ever more relevant to ensure that data quality and data integrity is not sacrificed.
- Heightened sophistication of data collected in clinical trials: Going beyond traditional safety and efficacy eCRF collected data like labs, vital signs, physical examination, adverse events etc., data sources outside of the EDC like eCOA (electronic Clinical Outcomes Assessment), wearables, sensors, and eSource solutions will gain more relevance in 2024. Per a Society for Clinical Data Management (SCDM) 2022 paper, wearables and sensors are able to generate data at higher velocity and continuously per time-point compared to the EDC. Therefore, a different approach is needed to be able to monitor the data for safety signals. The clinical data manager, which has been historically responsible for monitoring and validating voluminous clinical trial data, remains in the best position to optimise existing solutions and harness new technologies to manage these new kinds of data.
- Evolving Clinical Trial Designs: To increase the rate at which subjects are enrolled in studies, and also to adjust for a reduction in the pool of eligible participants in certain therapeutic areas, different innovative trial designs have been proposed. Per a 2019 paper by the SCDM, these evolving study designs include:
- Adaptive study design: In an adaptive study design, different study phases (1,2,3,4) could be combined into one protocol, with the ultimate aim of using data collected in earlier stages of the study to adapt the study design in later stages based on predefined rules in the protocol. The CDM is best fitted to understand implications of such a design on the CRF creation, data review and data cleaning. Other study designs include the umbrella design in which multiple interventions are used to test a single indication, basket design in which a single intervention is used to test multiple indications and the platform design in which multiple interventions are used to test multiple indications. The rise of Decentralised Clinical Trials (DCT) will also require the role of CDM to address the unique challenges to data collection, data review, data analysis and data presentation that they pose.
From the reasons above, it is seen that the clinical trial industry will experience the adoption of new technologies and processes in 2024, and Clinical Data Management remains ever crucial in managing this evolution and growth.