受到惩罚的贝叶斯前延续比率模型,适用于具有离散生存结果的高维数据
Anna Eames Seffernick1,2, Kellie J Archer2
1Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, United States of America.
PloS one
|March 28, 2024
概括
我们为离散生存数据开发了贝叶斯模型,在变量选择中表现优于频率主义方法. 这种方法可以确定急性髓性白血病等疾病的关键基因组特征.
科学领域:
- 生物统计学 生物统计学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 时间到事件数据通常是连续的,但在某些情况下,离散的生存数据可能更合适.
- 现有的离散生存模型包括前进延续比率模型,这与Cox的比例危险模型有关.
- 之前在高维设置中的实现使用了频率算法,限制了可变选择能力.
研究的目的:
- 为离散的生存数据提出贝叶斯惩罚的前进延续比率模型.
- 探索在高维环境中使用不同的先验来进行变量选择和规范化.
- 评估模型的性能与现有的频率主义方法相比,并将其应用于现实世界的数据集.
主要方法:
- 开发了一种贝叶斯惩罚前进延续比率模型,配有补充的日志-日志链接.
- 调查了变量选择的各种先前包含概率 (1%,10%,50%).
- 将模型应用于公开可用的急性髓性白血病数据集,以识别基因组特征.
主要成果:
- 与频率主义方法相比,拟议的贝叶斯模型显示出优越的变量选择性能.
- 10%的先前包含概率在模拟中产生了比1%或50%更好的结果.
- 确定了9个基因组特征,映射到10个基因,其中5个先前与白血病有关.
结论:
- 贝叶斯惩罚前延续比率模型为离散生存数据分析提供了灵活性.
- 该模型允许同时进行变量选择和不确定性量化.
- 这种方法对于识别与疾病相关的基因组特征是有效的,正如急性髓性白血病研究所显示的那样.
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