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Hazard and Beyond: Exploring Five Distributional Representations of Accelerometry Data for Disability Discrimination in Multiple Sclerosis by Niyogi, Pratim Guha; Sanjayan, Muraleetharan; Volfson, Dmitri; Fitzgerald, Kathryn C.; Mowry, Ellen M.; Zipunnikov, Vadim is a scholarly article available to read on EtoBox.

What is Hazard and Beyond: Exploring Five Distributional Representations of Accelerometry Data for Disability Discrimination in Multiple Sclerosis about?

Research on modeling the distributional aspects in sensor-based digital health (sDHT) data has grown significantly in recent years. Most existing approaches focus on using individual-specific density or quantile functions. However, there has been limited exploration to assess the practical utility of alternative distributional representations in clinical contexts collecting sDHT data. This study is motivated by accelerometry data collected on 246 individuals with multiple sclerosis (MS)representing a wide range of disability (Expanded Disability Status Scale, EDSS: 0-7). We consider five different individual-level distributional representations of minute-level activity counts: density, survival, hazard, quantile, and total time on test functions. For each of the five distributional representations, scalar-on-function regression fits linear discriminators for binary and continuously measured MS disability, and cross-validated discriminatory performance of these linear discriminators is compared across. The results show that individual-level hazard functions provide the highest discriminatory accuracy, more than double the accuracy compared to density functions. Individual-level quan

Author

Niyogi, Pratim Guha; Sanjayan, Muraleetharan; Volfson, Dmitri; Fitzgerald, Kathryn C.; Mowry, Ellen M.; Zipunnikov, Vadim

Published

2024

Language

EN