一种新的块坐标梯度下降算法,用于在联合建模中同时分组选择固定和随机效应
Shuyan Chen1, Zhiqing Fang2, Zhong Li3
1School of Management, University of Science and Technology of China, Anhui, China.
Statistics in medicine
|August 15, 2024
概括
这项研究引入了一种新的算法,用于纵向和时间到事件数据的联合建模,有效地选择重要的固定和随机效应,以改进复杂健康结果的分析.
科学领域:
- 生物统计学 生物统计学
- 纵向数据分析 纵向数据分析
- 生存分析的分析.
背景情况:
- 联合模型整合了纵向和时间到事件数据,以探索关联.
- 现有的方法在同时有效选择固定和随机效应时面临挑战.
- 需要强大的方法来处理复杂的共同变量选择在联合模型中.
研究的目的:
- 提出一种新的算法,用于在联合模型中同时选择固定和随机效应.
- 解决在联合建模框架内的高效和有效的共变量选择方面的研究缺口.
- 通过改进共变量识别来增强纵向和时间到事件数据的分析.
主要方法:
- 开发了一个区块坐标梯度下降 (BCGD) 算法用于共同变量选择.
- 采用线性混合效应模型用于随机拦截和斜率的纵向过程.
- 在时间到事件子模型中使用比例危险模型,并对概率估计进行惩罚.
主要成果:
- 拟议的BCGD方法有效地选择了重要的固定和随机效应共变量.
- 证明了出色的选择能力,并提供了对效应的准确经验估计.
- 模拟研究证实了该方法的卓越性能和有效性.
结论:
- 在联合模型中,BCGD算法为共变量选择提供了高效和有效的解决方案.
- 成功应用于现实世界的数据,识别心脏门结果和初级胆道胆道炎的危险因素.
- 突出了先进的统计方法在发现复杂的健康相关风险因素的有用性.
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