Kate Lyden, Chief Science Officer at VivoSense, and Vanessa Sarrechia, Data Standards Lead at argenx, delivered an informative presentation at the 2024 CDISC + TMF Europe Interchange. Their talk, titled “Considerations for Driving High-Quality DHT Datasets and Aligning with CDISC Standards,” focused on key strategies for integrating digital health technologies (DHTs) in clinical trials. Additionally, they used a first-hand case study to show the importance of collaboration, detailed planning, and meticulous execution. These elements help Data Teams manage, organize, and analyze data from DHTs effectively.
The presentation highlighted several critical areas:
Validation and “Fit for Purpose” Digital Endpoints
Validating digital endpoints for their specific context of use is crucial. This involves rigorously testing hardware, software, and algorithms to ensure they perform as intended within the target population and setting. Furthermore, endpoints must be meaningful to patients and clinically relevant.
Next, selecting measurement technologies and data collection protocols:
The selected technology and measurement protocol must align with the patient’s needs and meet the clinical trial requirements. For instance, consider how comfortable the technology is for patients to wear or use, any additional patient interactions necessary to collect the data (such as charging and synchronizing), and how long patients are willing to comply with the data collection protocol.
Operationalization for Precise Datasets
Generating accurate datasets with minimal missing data is vital. This involves near-real-time compliance monitoring, robust technology oversight, and meticulous data cleaning processes to ensure data integrity and reproducibility.
Compliance and Data Security
Ensuring that all data handling and processing procedures meet stringent regulatory requirements is non-negotiable. This includes maintaining data security and privacy, documenting all data manipulations, and preserving all iterations of the data. Thus, this ensures datasets are reproducible back to their source and can withstand regulatory scrutiny.
Aligning with CDISC Standards
Organizing data from DHTs into datasets that align with CDISC standards is required for regulatory submissions. Indeed, this task is best accomplished by collaborative efforts from individuals with expertise in DHT data, including raw sensor data, derived measures, and metadata, and individuals with expertise in clinical trial data management. Therefore, understanding the meaning and size of each data element is key to ensuring DHT datasets conform to regulatory requirements, avoiding delays or rejections in regulatory review.