Bayes factors for two-group comparisons in Cox regression
Bayes factors for two-group comparisons in Cox regression
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DOI：
10.1101/2022.11.02.22281762
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
M. Linde;J. Tendeiro;D. Ravenzwaaij
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文献类型：
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作者：
M. Linde;J. Tendeiro;D. Ravenzwaaij
在生物医学研究中，使用COX比例风险回归分析时间到事件数据是普遍存在的。通常，频率论框架被用来得出结论，即在实验条件和对照条件下，患者之间的风险是否不同。我们提供了一个计算简单COX模型的贝叶斯因子的程序，既适用于完整数据可用的情景，也适用于仅有汇总统计数据的情景。该过程在我们的“baymedr”R包中实现。贝叶斯因子的使用弥补了频域推理的一些不足，具有节约稀缺资源的潜力。
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 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.