使用顺序回归模型分析集群连续响应变量
Yuqi Tian1, Bryan E Shepherd1, Chun Li2
1Department of Biostatistics, Vanderbilt University, Nashville, Tennessee, USA.
Biometrics
|July 17, 2023
概括
这项研究引入了一种新的统计方法,用于分析连续的健康数据,特别是当数据被聚类或重复测量时. 这种方法避免了数据转换,简化了分析,并改善了复杂健康研究的结果解释.
科学领域:
- 生物统计学 生物统计学
- 统计建模 统计建模
- 分析健康数据 分析健康数据
背景情况:
- 连续响应数据通常需要对回归建模进行转换,这可能是任意的,并增加不确定性.
- 变化在不同研究中各不相同,阻碍了结果的合成和解释.
- 聚类或重复测量的连续数据存在挑战,原因是主体内部的相关性.
研究的目的:
- 扩展累积概率模型 (CPM) 用于分析集群,连续响应数据.
- 为了提供模型参数,分布,预期和量值的估计,而不需要预先转换响应变量.
- 开发计算效率高的方法,以配合大量不同响应值的CPM.
主要方法:
- 使用一般化估计方程 (GEE) 进行顺序响应,以适应累积概率模型 (CPM).
- 开发了可行和计算效率高的方法,以在共同的工作相关结构下安装CPM.
- 采用模拟研究来评估拟议估计器的有限样本操作特征.
主要成果:
- 拟议的方法允许直接分析集群的连续响应数据,而无需进行预先转换.
- 可以获得边际模型参数,CDF,预期和依赖共变量的量值的估计.
- 为配备CPM提出了高效的计算方法,解决了许多不同的响应值的挑战.
结论:
- 扩展的CPM为分析集群,连续响应数据提供了一个强大的替代方案.
- 这种方法提高了复杂健康研究结果的解释性和合成性.
- 该方法用艾滋病毒研究和慢性阻塞性肺病研究中的实际例子来说明.
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