{
  "abstract": "Clinical trials usually target average treatment effects, but treatment decisions are made for individuals. This tension motivates a common criticism of evidence-based medicine: a treatment that is beneficial on average may be inappropriate for a particular patient, and skilled physicians may outperform rigid adherence to the strategy that performed best in a randomized trial. We consider how randomized and observational data from the same target population can be used to assess that possibility. Specifically, we study settings in which trial and/or observational data yield estimates of average outcomes under `treat all', `treat none', and usual care strategies in the population of interest. We derive sharp bounds on the proportion of encounters with physicians whose personal strategies outperform the better of `treat all' or `treat none' under the assumption that no physician's strategy is worse than always choosing the worse performing of `treat all' and `treat none'. These results clarify when clinical data support relying on physician discretion over the trial-average recommendation and when stronger justification is required.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.01050",
  "author_total": 2,
  "authors": [
    {
      "name": "Zach Shahn"
    },
    {
      "name": "Mats Stensrud"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f1v1x1rpv7n6m.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.01050",
  "fulltext_url": null,
  "id": "f1v1x1rpv7n6m",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.01050",
  "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.AP"
    ],
    [
      "openalex",
      "2613|Advanced Causal Inference Techniques"
    ]
  ],
  "openalex": "W7160332484",
  "pdf_url": "https://arxiv.org/pdf/2605.01050",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f1v1x1rpv7n6m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:36Z",
      "native_id": "2605.01050",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.01050"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Trust Me, I'm a Doctor?",
  "title_text": "Trust Me, I'm a Doctor?",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:36Z",
  "url": "https://research.draconitic.com/paper/f1v1x1rpv7n6m",
  "venue": "arXiv",
  "versions": []
}