高维的高效计算 处罚 零碎 恒定的危险 随机效应模型
Hillary M Heiling1, Naim U Rashid1,2, Quefeng Li1
1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Statistics in medicine
|March 10, 2025
概括
本研究引入了一种新的生存模型,以简化复杂的比例危险混合效应模型 (PHMMs). 该方法可以同时选择固定和随机效应的变量,从而改善对高维生物医学数据的分析.
科学领域:
- 生物统计学 生物统计学
- 基因组学就是基因组学.
- 生存分析的分析.
背景情况:
- 在生物医学研究中,比例危险混合效应模型 (PHMMs) 对于分析具有集群相关性的时间到事件数据至关重要.
- 高维数据在PHMM中指定和计算处理固定和随机效应时存在挑战.
研究的目的:
- 在高维存数据中开发一种计算效率高的变量选择方法.
- 为了将PHMM与更容易处理的零碎常数危险混合效应生存模型相近.
- 为了能够同时选择重要的固定和随机效应.
主要方法:
- 采用零件式恒定危险混合效应生存模型对PHMMs的近似计算.
- 通过修改的蒙特卡洛预期条件最小化 (MCECM) 算法进行参数估计.
- 纳入因子模型分解随机效应以增强可扩展性.
主要成果:
- 提出的方法有效地对固定和随机效应进行同时变量选择.
- 因子模型分解有助于将变量选择扩展到更高的维度.
- 通过模拟和应用于胰腺癌基因表达数据集的应用,证明了效用.
结论:
- 开发的方法为高维生存分析中的变量选择提供了可扩展和有效的解决方案.
- 该方法有助于识别影响生存结果的关键特征,特别是在复杂的数据集中,如基因表达研究.
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