Evaluating Phase and Amplitude in Functional Data-Based Biomarkers: Cluster and Reliability Analyses

Music City Center 

Adopting a new biomarker requires rigorous evaluation of its discriminability and reliability. Discriminability assesses how well a biomarker differentiates individuals with varying disease risks, while reliability measures its ability to reproduce measurements under the same conditions. With advances in medical technology, biomarkers increasingly take the form of functional data, where each observation consists of dense measurements over time, treated as smooth functions. Amplitude (vertical) and phase (horizontal) variations in functional data often provide key insights into disease mechanisms, yet existing evaluation tools, such as cluster and reliability analyses, often rely on the L2 metric, which fails to separate these variations. We introduce cluster and reliability analysis methods that assess functional data-based biomarkers based on amplitude and phase features. Specifically, for cluster analysis, we introduce a K-means-type algorithm that groups functional data using amplitude and phase distance metrics. For reliability analysis, we propose agreement indices that measure how well the amplitude and phase features of functional data are reproduced on the same unit.

agreement

amplitude variation

biomarker evaluation

functional data clustering

phase variation

reliability 

Section on Medical Devices and Diagnostics