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Updated: Jan 16, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Bayes factors for two-group comparisons in Cox regression with an application for reverse-engineering raw data from
Maximilian Linde1,2, Jorge N Tendeiro3, Don van Ravenzwaaij2
1Department of Computational Social Science, GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany.
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.
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