{
  "abstract": "Stepped-wedge cluster-randomized trials (SW-CRTs) randomly assign clusters to sequences that determine when each cluster switches from control to intervention. Conventional Bayesian analyses of SW-CRTs report treatment effect coefficients from a working outcome model, but under model misspecification or informative cluster-period sizes, such coefficients can lack a clear causal interpretation. In this work, we develop Bayesian methods targeting weighted average treatment effect estimands in SW-CRTs, including novel subgroup-specific causal estimands defined by baseline characteristics. We compare model-based g-computation with model-robust standardization, implementing both with linear mixed models and mixed-effects Bayesian additive regression trees as working outcome models, and propose a calibrated variance estimator that augments posterior parameter uncertainty with cluster-level resampling to better capture sampling variability. In simulations with outcome model misspecification and informative cluster-period sizes, model-robust standardization with calibrated inference exhibits negligible bias and near-nominal frequentist coverage for overall and subgroup-specific causal effects, whereas g-computation and uncalibrated intervals become insufficient. We illustrate the proposed methods with a reanalysis of the Trauma Survivors Outcomes and Support trial.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.20858",
  "author_total": 5,
  "authors": [
    {
      "name": "Ruyi Liu"
    },
    {
      "name": "Yuki Ohnishi"
    },
    {
      "name": "Xi Fang"
    },
    {
      "name": "Michael J. Daniels"
    },
    {
      "name": "Fan Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d08nf4ezhtgw1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.20858",
  "fulltext_url": null,
  "id": "d08nf4ezhtgw1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.20858",
  "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": null,
  "pdf_url": "https://arxiv.org/pdf/2610.20858",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d08nf4ezhtgw1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:00Z",
      "native_id": "2610.20858",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.20858"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Calibrated Bayesian inference for overall and subgroup-specific causal effects in stepped-wedge cluster-randomized trials",
  "title_text": "Calibrated Bayesian inference for overall and subgroup-specific causal effects in stepped-wedge cluster-randomized trials",
  "type": "preprint",
  "updated_at": "2026-10-30T12:30:00Z",
  "url": "https://research.draconitic.com/paper/d08nf4ezhtgw1",
  "venue": "arXiv",
  "versions": []
}