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Bayes factors for two-group comparisons in Cox regression with an application for reverse-engineering raw data from summary statistics

Author

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  • Maximilian Linde
  • Jorge N. Tendeiro
  • Don van Ravenzwaaij

Abstract

The use of Cox proportional hazards regression to analyze time-to-event data is ubiquitous in biomedical research. Typically, the frequentist framework is used to draw conclusions about whether hazards are different between patients in an experimental and a control condition. We offer a procedure to compute Bayes factors for simple Cox models, both for the scenario where the full data are available and for the scenario where only summary statistics are available. The procedure is implemented in our ‘baymedr’ R package. The usage of Bayes factors remedies some shortcomings of frequentist inference and has the potential to save scarce resources.

Suggested Citation

  • Maximilian Linde & Jorge N. Tendeiro & Don van Ravenzwaaij, 2025. "Bayes factors for two-group comparisons in Cox regression with an application for reverse-engineering raw data from summary statistics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 52(13), pages 2413-2437, October.
    • Handle: RePEc:taf:japsta:v:52:y:2025:i:13:p:2413-2437
    DOI: 10.1080/02664763.2025.2472150
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