{
  "abstract": "This note introduces FRESH (Fusion of Recent Evidence and Subject Histories), a method for incorporating population-level summary results -- published clinical trials, registry summaries, prior natural-history studies, and peer-reviewed indirect comparisons -- into predictive models trained on patient-level data. This method provides a principled means of combining both patient-level and aggregate-level data types into a unified data-efficient model for clinical decision making. FRESH assumes access to a generative model trained on patient-level data sources (e.g. clinical trial or real-world data). The method produces patient-level predictions from a re-calibrated model that matches a set of specified aggregate statistics for a target population. This can be understood as a patient-level recapitulation of the aggregate source -- with the key property that the recalibration is a minimal perturbation of the original joint distribution in a specific information-geometric sense. The resulting samples can be analyzed directly or combined into a post-training procedure to update the original generative model. This approach enables several applications where rigorously incorporating patient-level data with summary information is valuable, including (i) contextualizing single-arm trial results with respect to recent standard-of-care, (ii) clinical-trial simulations for design and probability-of-technical-success estimation, and (iii) comparative-effectiveness analyses of on-market therapies.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.16246",
  "author_total": 5,
  "authors": [
    {
      "name": "Franklin Fuller"
    },
    {
      "name": "Daniele Bertolini"
    },
    {
      "name": "Samantha Liang"
    },
    {
      "name": "Jason Christopher"
    },
    {
      "name": "Aaron M. Smith"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/21c284ke1eqte.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.16246",
  "fulltext_url": null,
  "id": "21c284ke1eqte",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.16246",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "2613|Statistical Methods in Clinical Trials"
    ]
  ],
  "openalex": "W7161574565",
  "pdf_url": "https://arxiv.org/pdf/2605.16246",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/21c284ke1eqte.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:29Z",
      "native_id": "2605.16246",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.16246"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "FRESH: Information-Geometric Calibration of Patient-Level Models to Aggregate Evidence",
  "title_text": "FRESH: Information-Geometric Calibration of Patient-Level Models to Aggregate Evidence",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:29Z",
  "url": "https://research.draconitic.com/paper/21c284ke1eqte",
  "venue": "arXiv",
  "versions": []
}