Resources designed for you
Use these resources to establish a clear decision process for designing and conducting analytical validation for your digital clinical measure of interest, whether it is for a novel or previously assessed outcome.
DHT developers
Accelerate your analytical validation studies and streamline your interaction with regulators
Biostatisticians
Support your colleagues in developing statistically sound analytical validation studies and access a simulation toolkit to quickly game out more complex reference measure comparisons
Our collaborator
This work was conducted as a Public-Private Partnership (PPP) with the FDA’s Center for Drug Evaluation and Research (CDER).
See how others are using these resources
Leif Simmatis | Postdoctoral Researcher, University of Toronto
Libby Floden | Biostatistician, Head of Endpoints, Evinova
Chen Chen | Senior Research Fellow, Lee Kong Chian School of Medicine Singapore
Eric J. Daza | Founder and Chief Editor, Stats-of-1
Colleen Russell | Senior Principal Statistician, Parexel International
More about this project
Additional resources to support your success
V3+ Framework
DATAcc by DiMe’s V3+ Framework established a robust and widely adopted approach to:
- Modularly conduct verification
- Analytical validation
- Usability validation
- Clinical validation of sensor-based digital health technologies (sDHTs)
Analytical validation can be especially complex because it relies on a fit-for-purpose reference measure, which can be difficult to obtain.
For a quick refresher on the V3+ Framework, enroll in the Digital Medicine Academy’s 30-minute course.
