{
  "abstract": "Adaptive enrichment trials that identify responsive subpopulations allocate a fixed sample across them and estimate each one's effect from the resulting data. For a synthetic control design with adaptive recruitment and borrowing of control outcomes, we ask whether a doubly robust family of targeted maximum likelihood estimators should replace its sample mean; the choice can be made largely at the design stage. A crossing condition weighs the bias removed and the efficiency gained by the doubly robust family against the finite-sample inflation of nuisance estimation; an equal-error boundary, located by simulation, separates the families in the plane of sample size and bias. A consistency principle prices recruiting with the wrong family's variance functional. With three and eight simulated subpopulations the boundary's sign prediction for the mean squared error difference holds in $99.6\\%$ of $10{,}302$ comparisons decided at two standard errors. A pre-specified selector commits to the doubly robust family where the squared anticipated bias exceeds the net variance term. Recruiting along that family's path costs up to $0.035$ in the area under the receiver operating characteristic curve; recruiting with the sample mean functional and routing only the analysis removes this cost and about nine tenths of its error excess.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.00549",
  "author_total": 3,
  "authors": [
    {
      "name": "Xin Luo"
    },
    {
      "name": "Maria Kudela"
    },
    {
      "name": "Margaret Gamalo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/48fj0fgbfvak7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.00549",
  "fulltext_url": null,
  "id": "48fj0fgbfvak7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.00549",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.ME"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.00549",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/48fj0fgbfvak7.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:21Z",
      "native_id": "2611.00549",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.00549"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Characterizing Estimator-Regime Transitions in Adaptive Enrichment Trials",
  "title_text": "Characterizing Estimator-Regime Transitions in Adaptive Enrichment Trials",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:35Z",
  "url": "https://research.draconitic.com/paper/48fj0fgbfvak7",
  "venue": "arXiv",
  "versions": []
}