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Peer-reviewedCC BYStatistics in medicine

Federated Time-to-Event Data Analysis Using Piecewise Exponential Regression in DataSHIELD.

  • Bodil Svennblad1
  • Benedicte Delcoigne2
  • Paul Burton3
  • Demetris Avraam4
4 affiliations
  1. 1Department of Surgical Sciences, Medical Epidemiology, Uppsala University, Uppsala, Sweden.
  2. 2Department of Medicine, Clinical Epidemiology Division, Karolinska Institute, Stockholm, Sweden.
  3. 3Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.
  4. 4Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
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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.

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Cite

BibTeXRIS
BibTeX
@article{svennblad2026federated,
  title = {{Federated Time-to-Event Data Analysis Using Piecewise Exponential Regression in DataSHIELD.}},
  author = {Bodil Svennblad and Benedicte Delcoigne and Paul Burton and Demetris Avraam},
  journal = {Statistics in medicine},
  year = {2026},
  month = oct,
  doi = {10.1002/sim.70789},
  url = {https://europepmc.org/article/MED/42894295},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Federated Time-to-Event Data Analysis Using Piecewise Exponential Regression in DataSHIELD.
AU  - Bodil Svennblad
AU  - Benedicte Delcoigne
AU  - Paul Burton
AU  - Demetris Avraam
PY  - 2026
DA  - 2026/10/01
JO  - Statistics in medicine
DO  - 10.1002/sim.70789
UR  - https://europepmc.org/article/MED/42894295
AB  - 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.
SN  - 0277-6715
SN  - 1097-0258
ER  -