{
  "abstract": "Early-phase combination oncology trials increasingly seek the optimal biological dose combination (OBDC) rather than a maximum tolerated dose combination, in line with the FDA's Project Optimus initiative. Existing Bayesian OBDC designs span rule-based, model-assisted, and model-based paradigms, but no unified framework exists for choosing among them, and none supports borrowing information across indications sharing a combination regimen. We propose a corrected three-by-two taxonomy of OBDC designs by mechanism and objective, a rule-based design (Ji3+3-Comb) closing a documented gap in transparent, model-free combination dose-finding, and a Bayesian Hierarchical Utility-based Cross-indication (BHUC) design that borrows information across indications via a robust mixture prior while discounting non-exchangeable information. We derive the shared utility function as the Bayes-optimal decision under a linear clinical loss, and prove that BHUC's mixture-prior posterior weight automatically vanishes as two indications' true rates become discordant, formalizing its robustness to non-exchangeable borrowing, and complement this asymptotic guarantee with an exact, sample-size-free ceiling on borrowed influence that holds even before any own-indication data accrue. A roadmap links trial features to design choice, and a 5000-replication simulation study, sensitivity analyses, and a case study built from a published phase Ib trial show that model-assisted designs offer the most robust safety-efficacy trade-off, while BHUC improves correct selection over independent per-indication designs even under discordance. We conclude with concrete, protocol-actionable recommendations for pharmaceutical and trial biostatisticians, and directions for future methodological and computational development.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.23798",
  "author_total": 3,
  "authors": [
    {
      "name": "Ayon Mukherjee"
    },
    {
      "name": "Kentaro Takeda"
    },
    {
      "name": "James M. S. Wason"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/765162j2mt19x.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.23798",
  "fulltext_url": null,
  "id": "765162j2mt19x",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.23798",
  "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"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Statistical Methods in Clinical Trials"
    ]
  ],
  "openalex": "W7214053843",
  "pdf_url": "https://arxiv.org/pdf/2609.23798",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/765162j2mt19x.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:33Z",
      "native_id": "2609.23798",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.23798"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Optimal Biological Dose Combination Finding: A Design Roadmap and Robust Cross-Indication Bayesian Borrowing",
  "title_text": "Optimal Biological Dose Combination Finding: A Design Roadmap and Robust Cross-Indication Bayesian Borrowing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:33Z",
  "url": "https://research.draconitic.com/paper/765162j2mt19x",
  "venue": "arXiv",
  "versions": []
}