{
  "abstract": "A subgroup analysis searching a trial's covariates or many prespecified subgroups often singles out a subgroup for its extremity, interpreted as if prespecified. Selection inflates the estimate; for a given selection rule, a correction removes the inflation. Two questions belong to the rule: which candidate to return, and whether to declare none. A third is whether inference extends to the patients left outside. We treat all three for interpretable subgroups, defined by conjunctions of covariate cuts, generated by forest search, a causal forest or a cross-fit surface estimator under one set of rules, each a modelling choice trading size against effect. Forest search screens candidates on splitting consistency, the rate at which effects replicate across half-splits; the screen is complementary-pairs stability selection in closed form, and the law of the maximum over the family gives the family-wise false-declaration rate when every candidate's effect is at least a clinically specified benefit. Refit-free multiplier resampling gives adjusted one-sided bounds (subgroup and complement), joined in a two-subgroup claim, with coverage asymptotically exact under separated selection, characterized under local competition, and conditional on a family proposed from the outcomes. Two trial applications, and simulations built from them, cover survival, binary and continuous endpoints.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.28379",
  "author_total": 2,
  "authors": [
    {
      "name": "Larry F. Leon"
    },
    {
      "name": "Keaven M. Anderson"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3xeqmeevx5ns1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.28379",
  "fulltext_url": null,
  "id": "3xeqmeevx5ns1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.28379",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Inference"
    ]
  ],
  "openalex": "W7223088901",
  "pdf_url": "https://arxiv.org/pdf/2610.28379",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3xeqmeevx5ns1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:13Z",
      "native_id": "2610.28379",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.28379"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Interpretable subgroup identification across survival and generalized-linear outcomes",
  "title_text": "Interpretable subgroup identification across survival and generalized-linear outcomes",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:13Z",
  "url": "https://research.draconitic.com/paper/3xeqmeevx5ns1",
  "venue": "arXiv",
  "versions": []
}