Optimal Cut-Point Estimation for functional digital biomarkers: Application to Continuous Glucose Monitoring
arxiv(2024)
摘要
Establish optimal cut points plays a crucial role in epidemiology and
biomarker discovery, enabling the development of effective and practical
clinical decision criteria. While there is extensive literature to define
optimal cut off over scalar biomarkers, there is a notable lack of general
methodologies for analyzing statistical objects in more complex spaces of
functions and graphs, which are increasingly relevant in digital health
applications. This paper proposes a new general methodology to define optimal
cut points for random objects in separable Hilbert spaces. The paper is
motivated by the need for creating new clinical rules for diabetes mellitus
disease, exploiting the functional information of a continuous diabetes monitor
(CGM) as a digital biomarker. More specifically, we provide the functional cut
off to identify diabetes cases with CGM information based on glucose
distributional functional representations.
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