贝叶斯对多变量纵向和生存结果的联合建模,使用高斯配方
Seoyoon Cho1, Matthew A Psioda2, Joseph G Ibrahim1
1Department of Biostatistics, University of North Carolina, McGavran-Greenberg Hall, CB#7420, Chapel Hill, NC 27599, United States.
Biostatistics (Oxford, England)
|April 26, 2024
概括
这项研究引入了一种新的高斯铜关节模型,用于分析纵向和生存数据. 建议的结构化分解提高了效率,并减少了统计分析的复杂性.
科学领域:
- 生物统计学 生物统计学
- 统计建模 统计建模
- 生存分析的分析.
背景情况:
- 联合模型分析了纵向和生存数据.
- 随机效应模型带来了实施挑战.
- 科普拉斯为联合建模提供了一个灵活的替代方案.
研究的目的:
- 开发一个联合模型,使用高斯偶数来计算多变量纵向和生存结果.
- 提出一个新的分解对的相关性结构.
- 提高效率并降低计算复杂度.
主要方法:
- 用于联合建模的高斯方针.
- 结构化的相关性分解 (例如,自动回归).
- 马尔科夫链蒙特卡洛 (MCMC) 用于参数估计.
主要成果:
- 拟议的结构化分解提供了效率的提高.
- 与非结构化模型相比,减少了计算复杂性.
- 在模拟研究和真实世界乳腺癌试验数据中成功应用.
结论:
- 新的高斯合体关节模型与结构化分解是有效的.
- 这种方法为分析复杂的纵向和生存数据提供了有价值的工具.
- 在生物统计研究和临床试验分析中证明有用.
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