Bayes Factors for Two-group Comparisons in Cox Regression

Abstract

AbstractThe 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 calculate Bayes factors for simple Cox models, both for the scenario where the full data is 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.