Industry spotlight
LIBRARY BY DIME
An interactive, searchable database cataloging sensor-based digital health technologies, high-quality digital clinical measures, and measurement tools. Evidence can be filtered by validation type.
BEST PRACTICES BY CTTI
Digital health technologies (DHTs) can capture measures as clinical trial participants go about their daily lives. These measures can be used as novel endpoints, defined as (1) new endpoints that have not previously been possible to assess or (2) existing endpoints that can be measured in new and possibly better ways. Because novel
endpoints have the potential to provide high-quality data pertaining to outcomes that are meaningful to patients while enabling broader, more accessible trials with reduced barriers to participation, CTTI created the following set of recommendations [which] focus on driving novel endpoint use by sponsors and review by regulators for greater acceptance across the clinical trial enterprise. (Introduction).
PUBLICATION
This paper discusses key considerations for generating evidence for clinical validity through the lens of the type and intended use of a clinical measure. This paper also briefly discusses the regulatory pathways through which clinical validity evidence may be reviewed and highlights challenges that investigators may encounter while dealing with data from biometric monitoring technologies. (Abstract)
Library of Digital Measurement Products
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
Developing Novel Endpoints Generated by Digital Health Technology for Use in Clinical Trials
Novel digitally-derived endpoints can provide more reliable data, increase trial efficiency, and enhance patient centricity.
Selecting appropriate outcome measures that are meaningful to patients and clinicians is critical to success.
Developing these endpoints requires a resource-intensive, systematic approach to meet stakeholder needs.
Demonstrating validity and utility of novel endpoints poses unique challenges, especially for new measures without established validation standards.
Sharing lessons learned and promoting transparency can advance the field by enabling collaboration and establishing standards.
Recommendations
Focus on measures that are meaningful to patients and clinically relevant by incorporating both patient and clinician perspectives.
Select technology after identifying the appropriate outcome to ensure alignment between the technology and trial objectives.
Engage with regulators early and often to ensure endpoint acceptance and alignment with regulatory requirements.
Include digitally-derived endpoints in early-phase trials and observational studies to validate their fit-for-purpose status.
Encourage knowledge sharing and collaboration among stakeholders to establish shared standards and accelerate adoption.
Regulatory Considerations
Engage with FDA, EMA, or other regulatory bodies during early stages of endpoint development to gather critical input.
Use established regulatory frameworks, such as Investigational New Drug (IND) or Investigational Device Exemption (IDE), for guidance on endpoint use in pivotal trials.
Validate technologies to meet performance characteristics, ensuring outputs correspond to clinical concepts of interest.
Include digitally-derived endpoints in exploratory studies to build evidence for their regulatory approval.
Reference resources such as the FDA and EMA guides for navigating endpoint-related regulatory interactions.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
Considerations for Analyzing and Interpreting Data from Biometric Monitoring Technologies in Clinical Trials
Limited evidence of clinical validity from pilot trials due to cost, time, and regulatory complexities.
Lack of standards for data integration across different tools and platforms.
Potential biases introduced by pre-existing algorithms.
Opaque data processing methods in BioMeTs.
Recommendations
Develop data, hardware, and software standards for BioMeTs.
Improve regulations for data rights, access, privacy, and governance.
Provide guidance on analytical methodologies for BioMeT data validation.
Regulatory Considerations
Early regulatory interactions with agencies like the FDA and EMA.
Ensuring data quality, integrity, reliability, and robustness.
Understanding regulatory pathways for BioMeTs in clinical trials.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
For reference: review the relevant regulatory guidances
Regulatory spotlight
Features essential guidance, publications, and communications from regulatory bodies relevant to this section. Use these resources to inform your regulatory strategy and ensure compliance.