用集成嵌套拉普拉斯近似方法对多变量纵向和生存数据的联合模型进行快速和灵活的推断
Denis Rustand1, Janet van Niekerk1, Elias Teixeira Krainski1
1Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
Biostatistics (Oxford, England)
|August 2, 2023
概括
本研究引入了使用R-INLA对纵向和生存数据的联合建模的贝叶斯近似. 这种方法显著减少了计算时间,并改善了复杂的健康研究数据集的参数估计.
科学领域:
- 生物统计学 生物统计学
- 健康 数据科学 数据科学
- 计算统计学 计算统计学
背景情况:
- 联合建模纵向和生存数据提供了诸如处理测量错误和预测事件风险等优势.
- 传统的联合模型面临着计算挑战,限制其应用到复杂的多变量健康数据.
研究的目的:
- 在R-INLA中使用集成嵌套拉普拉斯近似 (INLA) 算法引入贝叶斯近似,以实现高效的联合建模.
- 减轻多变量关节模型的计算负担,并扩大其适用性.
主要方法:
- 通过集成嵌套拉普拉斯近似 (INLA) 算法使用贝叶斯近似.
- 利用模拟研究来比较R-INLA与其他估计策略.
- 应用该方法来分析五个纵向标记物和在一次初级胆道胆炎临床试验中竞争的风险.
主要成果:
- 与替代方法相比,R-INLA大大减少了计算时间.
- 通过R-INLA方法,参数估计的可变性降低.
- 成功应用于一个复杂的数据集,具有多个纵向标记和相互竞争的风险.
结论:
- 对于复杂的多变量关节模型,R-INLA提供了一种计算效率高,可靠的推断技术.
- 这种方法促进了在健康研究中应用联合模型,克服了以前的局限性.
- 能够对纵向和生存数据关联以及风险预测进行可靠的分析.
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