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Bayesian Federated Inference for regression models based on non-shared medical center data.

Marianne A Jonker1, Hassan Pazira1, Anthony C C Coolen2,3

  • 1Research Institute for Medical Innovation, Science Department IQ Health, Section Biostatistics, Radboud University Medical Center, Nijmegen, Netherlands.

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Summary
This summary is machine-generated.

Bayesian Federated Inference (BFI) allows combining separate statistical results from different data centers. This method overcomes data limitations and privacy issues, improving regression model predictions for new patients.

Keywords:
Federated Learningdata integrationdecentralized datadistributed inferenceone-shot algorithm

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Machine Learning

Background:

  • Regression models require sufficient sample size for accurate parameter estimation.
  • Lack of data leads to overfitting and unreliable predictions in medical settings.
  • Pooling data across centers is often infeasible due to privacy and logistical constraints.

Purpose of the Study:

  • To introduce Bayesian Federated Inference (BFI) as a method to combine statistical results from decentralized data.
  • To enable accurate regression model analysis without pooling sensitive data.
  • To provide a practical solution for improving predictive accuracy in data-scarce environments.

Main Methods:

  • Bayesian Federated Inference (BFI) methodology is applied to analyze local data separately.
  • Statistical inference results from individual centers are combined.
  • The approach accounts for both homogeneity and heterogeneity across populations in different centers.

Main Results:

  • The proposed BFI methodology demonstrates excellent performance in combining statistical inferences.
  • The method effectively computes results as if analysis was performed on combined data.
  • An R-package has been developed to facilitate these calculations.

Conclusions:

  • Bayesian Federated Inference (BFI) offers a viable solution for regression modeling with distributed and private data.
  • This approach enhances predictive reliability for new patients despite data limitations.
  • The developed R-package supports the practical implementation of BFI in biostatistics and medical research.