对异步纵向数据的半参数混合回归使用多变量功能主要成分分析
Ruihan Lu1, Yehua Li2, Weixin Yao2
1Office of Biostatistics, Food and Drug Administration, 10903 New Hampshire Avenue, Sliver Spring, MD 20993, United States.
Biostatistics (Oxford, England)
|March 22, 2025
概括
这项研究使用先进的统计方法确定了更年期期间女性的不同子组. 了解这些荷尔蒙变化的模式是妇女长期健康的关键.
科学领域:
- 生殖内分泌学和妇女健康研究.
- 在纵向研究中的统计建模和数据分析.
- 生物标记分析和功能数据分析.
背景情况:
- 更年期涉及荷尔蒙波动影响妇女的长期健康.
- 全国妇女健康研究 (SWAN) 收集了关于妇女健康的纵向数据.
- 在SWAN中,荷尔蒙生物标志物与其他共变量进行异步评估,这带来了分析挑战.
研究的目的:
- 使用SWAN数据分析老龄化女性人口中的子组结构.
- 探索激素反应和共变量之间的关系在子组之间如何不同.
- 开发用于处理异步的统计方法,容易出错的共变量数据在纵向研究.
主要方法:
- 采用半参数混合回归模型进行子组分析.
- 模拟异步,时间变化的协变轨迹作为使用卡鲁宁-洛埃夫扩展和支线的功能数据.
- 利用预期最大化算法来适应荷尔蒙反应和功能主要成分得分的联合模型,将子组成员身份视为缺失数据.
- 应用数据驱动的方法来确定最佳的子组数量.
主要成果:
- 在SWAN研究中确定了老龄化女性人口中显著的子组结构.
- 证明了功能数据建模和混合物回归用于分析复杂的纵向健康数据的实用性.
- 根据荷尔蒙形状和共变量,突出了经历更年期的女性之间的明显模式和区别.
结论:
- 分析揭示了更年期期间妇女健康的重要亚组变化.
- 先进的统计方法有效地揭示了纵向健康数据中隐藏的结构.
- 这些发现有助于更好地了解更年期过渡及其对妇女福祉的影响.
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