Cox回归中两组比较的贝叶斯因子与来自汇总统计的逆向工程原始数据的应用。

IF 1.1 4区数学 Q2 STATISTICS & PROBABILITY

Journal of Applied Statistics Pub Date : 2025-03-01 eCollection Date: 2025-01-01 DOI:10.1080/02664763.2025.2472150

Maximilian Linde, Jorge N Tendeiro, Don van Ravenzwaaij

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引用次数: 0

摘要

使用Cox比例风险回归分析事件时间数据在生物医学研究中是普遍存在的。通常，频率论的框架被用来得出结论，关于在实验和控制条件下的病人之间的危险是否不同。我们提供了一个程序来计算简单Cox模型的贝叶斯因子，既适用于完整数据可用的场景，也适用于只有摘要统计数据可用的场景。这个过程在我们的‘baymedr’ R包中实现。贝叶斯因子的使用弥补了频率推理的一些不足，并有可能节省稀缺资源。

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

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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来源期刊

Journal of Applied Statistics 数学-统计学与概率论

CiteScore

3.40

自引率

0.00%

发文量

126

审稿时长

6 months

期刊介绍： Journal of Applied Statistics provides a forum for communication between both applied statisticians and users of applied statistical techniques across a wide range of disciplines. These areas include business, computing, economics, ecology, education, management, medicine, operational research and sociology, but papers from other areas are also considered. The editorial policy is to publish rigorous but clear and accessible papers on applied techniques. Purely theoretical papers are avoided but those on theoretical developments which clearly demonstrate significant applied potential are welcomed. Each paper is submitted to at least two independent referees.