Welcome to the sDHT Adoption Library, featuring NaVi
NaVi is a closed-environment AI research assistant that leverages a carefully curated library of more than 300+ vetted documents, including FDA guidance and industry best practices. NaVi helps you search and explore content across the sDHT Adoption Library and Roadmap using natural language questions.
The Library is intended to serve as a living resource. Content is added periodically as new guidance, standards, and peer-reviewed research are released.
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Library scope and selection
To ensure high-quality, relevant results, the Library follows a predefined scoping approach:
- Inclusions: FDA guidance, non-commercial standards, and peer-reviewed research (2018–Present) focused on sDHTs being used as measurement tools for medical products in U.S.-based clinical trials.
- Exclusions: Materials from single commercial entities, non-U.S. regulatory bodies (except select EMA guidances with direct U.S. cross-relevance), and conference proceedings, and conference proceedings.
Inclusion in the Library does not imply endorsement, completeness, or regulatory acceptability.
Library Resources
Library scope
Resources in the sDHT Adoption Library are identified using a predefined scoping approach and include publicly available FDA guidance, non-commercial standards and guidance, and peer-reviewed research relevant to sDHT use in U.S.-based clinical trials. Materials from single commercial entities, non-U.S. regulatory bodies, conference proceedings, and studies conducted exclusively outside the United States are excluded; inclusion does not imply endorsement or regulatory acceptability.
Last updated 2026: Library content is reviewed and updated on a periodic basis as new eligible materials become available.
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Complex clinical trials – Questions and answers
This document provides a comprehensive guide for the planning, conduct, and regulatory submission of complex clinical trials (CCTs) under the EU Clinical Trials Regulation (CTR). It covers key aspects such as protocol design, risk management, use of master protocols, data transparency, and stakeholder communication. It also emphasizes the integration of biomarkers, innovative methodologies, and Bayesian approaches in trial design and evaluation, offering practical guidance to sponsors and regulators.
Complex clinical trials – Questions and answers
2022
EMA
Recommendations / best practices
Authors
European Medicines Agency
This document provides a comprehensive guide for the planning, conduct, and regulatory submission of complex clinical trials (CCTs) under the EU Clinical Trials Regulation (CTR). It covers key aspects such as protocol design, risk management, use of master protocols, data transparency, and stakeholder communication. It also emphasizes the integration of biomarkers, innovative methodologies, and Bayesian approaches in trial design and evaluation, offering practical guidance to sponsors and regulators.
Findings
Complex clinical trials involve unique challenges in design, operational feasibility, and regulatory compliance, necessitating early engagement with stakeholders.
Master protocols streamline trial processes by integrating shared scientific frameworks across sub-protocols, enhancing efficiency and data integrity.
Bayesian approaches, while promising, require transparency and rigorous validation to ensure robustness in trial outcomes.
The use of biomarkers and related assays in CCTs introduces added complexity, particularly concerning regulatory status and performance validation.
Effective risk-based quality management systems are essential to safeguard participant safety and maintain trial reliability.
Recommendations
Develop clear and detailed master protocols to define the shared framework, communication plans, and statistical methodologies for CCTs.
Employ risk-based quality management strategies, including robust risk assessment and targeted training for site personnel.
Ensure early and continuous engagement with regulators, investigators, and patients to address design complexities and operational challenges.
Pre-specify statistical plans and evaluation frameworks for Bayesian methods, adaptive designs, and biomarker integration.
Establish mechanisms for transparent reporting and management of safety data across sub-protocols while safeguarding trial integrity.
Regulatory Considerations
Adhere to EU CTR and IVD regulations, ensuring compliance in the use of biomarkers, companion diagnostics, and related assays.
Include comprehensive documentation of trial design, including shared frameworks, sub-protocols, and statistical methodologies, in submissions.
Implement robust data governance frameworks to ensure ALCOA++ (attributable, legible, original, accurate, complete, consistent) standards for regulatory submissions.
Plan for periodic reassessment of benefit-risk ratios during the trial, particularly when modifications or new data emerge.
Establish independent Data Monitoring Committees (DMCs) for long-term and complex trials to oversee safety and interim analyses.
Complex clinical trials involve unique challenges in design, operational feasibility, and regulatory compliance, necessitating early engagement with stakeholders.
Master protocols streamline trial processes by integrating shared scientific frameworks across sub-protocols, enhancing efficiency and data integrity.
Bayesian approaches, while promising, require transparency and rigorous validation to ensure robustness in trial outcomes.
The use of biomarkers and related assays in CCTs introduces added complexity, particularly concerning regulatory status and performance validation.
Effective risk-based quality management systems are essential to safeguard participant safety and maintain trial reliability.
Recommendations
Develop clear and detailed master protocols to define the shared framework, communication plans, and statistical methodologies for CCTs.
Employ risk-based quality management strategies, including robust risk assessment and targeted training for site personnel.
Ensure early and continuous engagement with regulators, investigators, and patients to address design complexities and operational challenges.
Pre-specify statistical plans and evaluation frameworks for Bayesian methods, adaptive designs, and biomarker integration.
Establish mechanisms for transparent reporting and management of safety data across sub-protocols while safeguarding trial integrity.
Regulatory Considerations
Adhere to EU CTR and IVD regulations, ensuring compliance in the use of biomarkers, companion diagnostics, and related assays.
Include comprehensive documentation of trial design, including shared frameworks, sub-protocols, and statistical methodologies, in submissions.
Implement robust data governance frameworks to ensure ALCOA++ (attributable, legible, original, accurate, complete, consistent) standards for regulatory submissions.
Plan for periodic reassessment of benefit-risk ratios during the trial, particularly when modifications or new data emerge.
Establish independent Data Monitoring Committees (DMCs) for long-term and complex trials to oversee safety and interim analyses.
Keywords
Adaptive design
Biomarkers
Clinical trial endpoints
Clinical trials
Complex clinical trials (CCT)
Data management
EMA guidance
Good Clinical Practice (GCP)
Investigational new drug application (IND)
Marketing authorization application
Master protocols
Platform trial
Premarket submissions
Quality management system (QMS)
Regulatory guidance
Risk management
Trial analysis
Trial design
Validation
Technologies
Open Resource
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.
Choosing a Mobile Sensor Technology for a Clinical Trial: Statistical Considerations, Developments and Learnings
An overview of key considerations for selecting mobile sensor technology (MST) for use in clinical trials, including expectations around technology specifications, verification, regulatory clearance, and statistical methods needed to establish agreement between the MST and a clinical 'gold' standard technology. It also discusses how to handle potential sources of variability in the data and provides guidance on combining data from different MST sources.
Choosing a Mobile Sensor Technology for a Clinical Trial: Statistical Considerations, Developments and Learnings
2020
Ther Innov Regul Sci
Recommendations / best practices
,
Review
Authors
Colleen Russell, Marie Mccarthy, Joseph C Cappelleri, Susan Wong
An overview of key considerations for selecting mobile sensor technology (MST) for use in clinical trials, including expectations around technology specifications, verification, regulatory clearance, and statistical methods needed to establish agreement between the MST and a clinical 'gold' standard technology. It also discusses how to handle potential sources of variability in the data and provides guidance on combining data from different MST sources.
Findings
The complexity of selecting appropriate technology due to an increasing array of devices and sensors.
Risks associated with choosing inappropriate MSTs, including susceptibility to missing data or erroneous data transmission.
The need for both manufacturers and clinical trial sponsors to ensure analytical validation supports MST use.
Recommendations
Identify a digital outcome that meets an unmet need for the planned trial or population.
Determine whether the technology is fit-for-purpose based on the measure, context of use, and classification as a medical device.
Ensure devices are reliable and reproducible for collecting required data.
Conduct statistical analysis according to a predefined analysis plan.
Consider adaptive designs to reduce resource requirements and increase study success.
Regulatory Considerations
Compliance with medical device classifications such as 510(k)s and CE marks.
Ensure devices and platforms comply with HIPAA, GDPR, and data privacy regulations.
Be aware of potential updates to technology or software that could impact trials.
The complexity of selecting appropriate technology due to an increasing array of devices and sensors.
Risks associated with choosing inappropriate MSTs, including susceptibility to missing data or erroneous data transmission.
The need for both manufacturers and clinical trial sponsors to ensure analytical validation supports MST use.
Recommendations
Identify a digital outcome that meets an unmet need for the planned trial or population.
Determine whether the technology is fit-for-purpose based on the measure, context of use, and classification as a medical device.
Ensure devices are reliable and reproducible for collecting required data.
Conduct statistical analysis according to a predefined analysis plan.
Consider adaptive designs to reduce resource requirements and increase study success.
Regulatory Considerations
Compliance with medical device classifications such as 510(k)s and CE marks.
Ensure devices and platforms comply with HIPAA, GDPR, and data privacy regulations.
Be aware of potential updates to technology or software that could impact trials.
Keywords
Accuracy
Adaptive design
Analytical validation
Biometric monitoring technologies
Clinical trial design
Clinical trials
Connected sensors
Data collection
Digital endpoints
Digital health technologies (DHTs)
FDA
FDA guidance
Feasibility studies
Neurodegenerative diseases
Precision
Regulatory compliance
Software as a medical device (SaMD)
Statistical validation
Verification
Wearables
Technologies
Open Resource
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.