{
  "abstract": "Questionnaire-based patient-reported outcomes (PROs) are discrete, bounded and overdispersed, yet joint models relating them to survival may ignore these features or estimate both processes sequentially. We propose a Bayesian joint model combining a beta-binomial mixed-effects submodel with a Weibull proportional hazards submodel, linked through the subject-specific response probability. Simulations show that simultaneous estimation reduces bias in the longitudinal slope and yields practically unbiased association estimates, unlike two-stage estimation. In a cohort of 543 patients with chronic obstructive pulmonary disease, the model identified associations for all eight SF-36 dimensions and for two of three SGRQ dimensions, including several associations not detected by the two-stage approach, and provided dynamic survival predictions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.30188",
  "author_total": 4,
  "authors": [
    {
      "name": "Cristina Galán-Arcicollar"
    },
    {
      "name": "Danilo Alvares"
    },
    {
      "name": "Josu Najera-Zuloaga"
    },
    {
      "name": "Dae-Jin Lee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4ee9yec9enkw5.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.30188",
  "fulltext_url": null,
  "id": "4ee9yec9enkw5",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.30188",
  "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.AP"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Bayesian Inference"
    ]
  ],
  "openalex": "W7214330437",
  "pdf_url": "https://arxiv.org/pdf/2609.30188",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4ee9yec9enkw5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:21Z",
      "native_id": "2609.30188",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.30188"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Bayesian joint modeling of longitudinal patient-reported outcomes and survival: an application to chronic obstructive pulmonary disease",
  "title_text": "Bayesian joint modeling of longitudinal patient-reported outcomes and survival: an application to chronic obstructive pulmonary disease",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:21Z",
  "url": "https://research.draconitic.com/paper/4ee9yec9enkw5",
  "venue": "arXiv",
  "versions": []
}