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Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
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Bayesian federated inference for survival models.

Hassan Pazira1, Emanuele Massa2, Jetty A M Weijers3

  • 1Research Institute for Medical Innovation, Science department IQ Health, Research & Education group Biostatistics, Radboud University Medical Center, Nijmegen, Netherlands.

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|February 6, 2026
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Summary
This summary is machine-generated.

Bayesian Federated Inference (BFI) extends to survival models, enabling accurate parameter estimation without merging sensitive data. This method combines local results for robust survival analysis, overcoming privacy and logistical hurdles.

Keywords:
62F0762F1562N0262P1091G70Decentralized datadistributed inferencefederated learningone-shot algorithmrare cancer

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Area of Science:

  • Biostatistics
  • Medical Informatics

Background:

  • Accurate survival prediction models require sufficient events per parameter.
  • Merging data across medical centers is often infeasible due to privacy and logistical issues.

Purpose of the Study:

  • To generalize Bayesian Federated Inference (BFI) methodology to survival models.
  • To evaluate the performance of BFI for survival data analysis.

Main Methods:

  • Extended the Bayesian Federated Inference (BFI) strategy from generalized linear models to survival models.
  • Conducted simulation studies and analyzed real-world data to validate the approach.

Main Results:

  • BFI methodology demonstrated excellent performance in survival data analysis.
  • Results from BFI closely matched those obtained from analyzing merged datasets.
  • An R package is available for implementing BFI for survival models.

Conclusions:

  • Bayesian Federated Inference is a viable and effective method for survival data analysis.
  • BFI overcomes data merging limitations, preserving privacy and simplifying logistics.
  • The generalized BFI approach provides accurate parameter estimation for survival prediction models.