ArticleJournal of applied statistics2025
Bayes factors for two-group comparisons in Cox regression with an application for reverse-engineering raw data from summary statistics.
Article in Journal of applied statistics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Data-driven prior elicitation for Bayes factors in Cox regression for nine subfields in biomedicine.PloS one · 2025Article
- Bayes factors for two-group comparisons in Cox regression with an application for reverse-engineering raw data from summary statistics.Journal of applied statistics · 2025Article
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Authors and funding
3 authors.
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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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Registered trials
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