Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs)

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作者
Jennifer C. Goldsack
Andrea Coravos
Jessie P. Bakker
Brinnae Bent
Ariel V. Dowling
Cheryl Fitzer-Attas
Alan Godfrey
Job G. Godino
Ninad Gujar
Elena Izmailova
Christine Manta
Barry Peterson
Benjamin Vandendriessche
William A. Wood
Ke Will Wang
Jessilyn Dunn
机构
[1] Digital Medicine Society (DiMe),Biomedical Engineering Department
[2] Elektra Labs,Computer and Information Sciences Department
[3] Harvard-MIT Center for Regulatory Science,Center for Wireless and Population Health Systems
[4] Philips,Department of Electrical, Computer and Systems Engineering
[5] Duke University,Department of Medicine
[6] Takeda Pharmaceuticals,Department of Biostatistics & Bioinformatics
[7] ClinMed LLC,undefined
[8] Northumbria University,undefined
[9] University of California,undefined
[10] Samsung Neurologica,undefined
[11] Curis Advisors,undefined
[12] Koneksa Health,undefined
[13] Independent Consultant,undefined
[14] Byteflies,undefined
[15] Case Western Reserve University,undefined
[16] University of North Carolina at Chapel Hill; Lineberger Comprehensive Cancer Center,undefined
[17] Duke University,undefined
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摘要
Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field.
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