{
  "annotation_id": "59707786-c17f-460b-b639-ded3f3955da1",
  "date_created": "2026-02-17T05:53:19.842000Z",
  "date_modified": "2026-02-17T05:53:19.842000Z",
  "file_hash": "94e26b7fe20e6a6a4780fc31f656c461c40241be92d3c10be4cf95383c5c1df8",
  "private": false,
  "record": {
    "abstract": "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.",
    "arxiv_id": "2601.09126",
    "authors": [
      "Pratim Guha Niyogi",
      "Muraleetharan Sanjayan",
      "Kathryn C. Fitzgerald",
      "Ellen M. Mowry",
      "Vadim Zipunnikov"
    ],
    "categories": [
      "stat.ME"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Scalar-on-distribution regression via generalized odds with applications to accelerometry-assessed disability in multiple sclerosis",
    "url": "https://arxiv.org/abs/2601.09126",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "aae17b06-e69c-4056-8f95-8cd1a04502f0",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}