{
  "abstract": "Time-to-event analyses on individual patient data are commonly performed with Cox or Poisson regression. The statistical power of epidemiological findings from such analyses is often sufficient when a large amount of data is analyzed, especially when studying rare outcomes or rare exposures. The increase of sample size can be achieved by co-analyzing data collected from different cohorts and hosted at different locations. For co-analyzing data located at different centers, the common options are either (i) to physically transfer and pool the data into a single dataset and analyze them jointly or (ii) to conduct a two-step meta-analysis where a separate analysis is done at each center and the center-specific results are then pooled and combined into a single estimate. DataSHIELD offers a third option where individual patient data from different locations are analyzed in a federated manner without transporting any data. In this paper, we describe how a federated Poisson regression can be conducted on time-to-event data using this infrastructure, providing relevant estimates.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Surgical Sciences, Medical Epidemiology, Uppsala University, Uppsala, Sweden.",
    "Department of Medicine, Clinical Epidemiology Division, Karolinska Institute, Stockholm, Sweden.",
    "Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.",
    "Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark."
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Bodil Svennblad",
      "orcid": "0000-0001-6393-3439"
    },
    {
      "affs": [
        1
      ],
      "name": "Benedicte Delcoigne",
      "orcid": "0000-0002-2716-5679"
    },
    {
      "affs": [
        2
      ],
      "name": "Paul Burton"
    },
    {
      "affs": [
        3
      ],
      "name": "Demetris Avraam",
      "orcid": "0000-0001-8908-2441"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2mk43g7nar07s.bib",
  "cited_by": 0,
  "doi": "10.1002/sim.70789",
  "fulltext_url": "https://europepmc.org/articles/PMC13678026",
  "id": "2mk43g7nar07s",
  "issns": [
    "0277-6715",
    "1097-0258"
  ],
  "landing_url": "https://europepmc.org/article/MED/42894295",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Privacy-Preserving Technologies in Data"
    ]
  ],
  "openalex": "W7222941841",
  "pdf_url": "https://europepmc.org/articles/PMC13678026?pdf=render",
  "pmcid": "PMC13678026",
  "pmid": "42894295",
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2mk43g7nar07s.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:37Z",
      "native_id": "MED:42894295",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42894295"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "journal-openalex:2613|Statistical Methods in Clinical Trials",
      "subfield": "statistics"
    }
  ],
  "title": "Federated Time-to-Event Data Analysis Using Piecewise Exponential Regression in DataSHIELD.",
  "title_text": "Federated Time-to-Event Data Analysis Using Piecewise Exponential Regression in DataSHIELD.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:37Z",
  "url": "https://research.draconitic.com/paper/2mk43g7nar07s",
  "venue": "Statistics in medicine",
  "versions": []
}