Scalar-on-distribution regression via generalized odds with applications to accelerometry-assessed disability in multiple sclerosis
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912822982606848 |
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| author | Niyogi, Pratim Guha Sanjayan, Muraleetharan Fitzgerald, Kathryn C. Mowry, Ellen M. Zipunnikov, Vadim |
| author_facet | Niyogi, Pratim Guha Sanjayan, Muraleetharan Fitzgerald, Kathryn C. Mowry, Ellen M. Zipunnikov, Vadim |
| contents | Distributional representations of data collected using digital health technologies have been shown to outperform scalar summaries for clinical prediction, with carefully quantified tail-behavior often driving the gains. Motivated by these findings, we propose a unified generalized odds (GO) framework that represents subject-specific distributions through ratios of probabilities over arbitrary regions of the sample space, subsuming hazard, survival, and residual life representations as special cases. We develop a scale-on-odds regression model using spline-based functional representations with penalization for efficient estimation. Applied to wrist-worn accelerometry data from the HEAL-MS study, generalized odds models yield improved prediction of Expanded Disability Status Scale (EDSS) scores compared to classical scalar and survival-based approaches, demonstrating the value of odds-based distributional covariates for modeling DHT data. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_09126 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scalar-on-distribution regression via generalized odds with applications to accelerometry-assessed disability in multiple sclerosis Niyogi, Pratim Guha Sanjayan, Muraleetharan Fitzgerald, Kathryn C. Mowry, Ellen M. Zipunnikov, Vadim Methodology Distributional representations of data collected using digital health technologies have been shown to outperform scalar summaries for clinical prediction, with carefully quantified tail-behavior often driving the gains. Motivated by these findings, we propose a unified generalized odds (GO) framework that represents subject-specific distributions through ratios of probabilities over arbitrary regions of the sample space, subsuming hazard, survival, and residual life representations as special cases. We develop a scale-on-odds regression model using spline-based functional representations with penalization for efficient estimation. Applied to wrist-worn accelerometry data from the HEAL-MS study, generalized odds models yield improved prediction of Expanded Disability Status Scale (EDSS) scores compared to classical scalar and survival-based approaches, demonstrating the value of odds-based distributional covariates for modeling DHT data. |
| title | Scalar-on-distribution regression via generalized odds with applications to accelerometry-assessed disability in multiple sclerosis |
| topic | Methodology |
| url | https://arxiv.org/abs/2601.09126 |