通用双变伯努利模型依赖性测试与具有相互作用效应的逻辑回归模型之间的等价性
Kazi Md Farhad Mahmud1, Yanming Li1, Devin C Koestler1
1Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.
Statistics in medicine
|September 8, 2025
概括
一个具有相互作用项的标准后勤回归模型相当于通用双变贝努利模型 (GBBM) 依赖性测试. 这种可访问的替代方案增强了生物医学研究中二元二元数据的分析.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 在健康研究中常见的双变二进制数据,使用通用双变伯努利模型 (GBBM) 进行分析.
- GBBM允许基于基线结果的共变量依赖关联测试,但由于缺乏软件实施,其利用率不足.
- 以前将GBBM与回归逻辑回归进行的比较有局限性.
研究的目的:
- 为GBBM依赖性测试提供一个标准的逻辑回归模型与交互术语作为实用的替代方案.
- 为了证明这种物流回归模型与GBBM方法的等价性.
- 将GBBM依赖性测试的功率与替代方法进行比较.
主要方法:
- 一个标准的逻辑回归模型与一个交互术语被提出并在概念上,理论上和经验上得到验证.
- 模拟对比了GBBM依赖性测试的功率和1型错误率与:回归后勤模型,与相互作用的后勤回归和Pearson Chi-square测试.
- 这些方法应用于来自孟加拉国人口和健康调查的婴儿死亡率数据.
主要成果:
- 具有相互作用的后勤回归模型在各种效果和样本大小中展示了与GBBM依赖性测试相当的功率和1型错误率.
- 这种等价性与以前的GBBM比较中使用的回归逻辑回归模型的性能形成鲜明对比.
- 拟议的物流回归模型为分析双变二进制数据提供了更容易获得和更灵活的方法.
结论:
- 一个带有相互作用项的标准后勤回归模型作为GBBM依赖性测试的实用和可访问的替代方案.
- 这一发现增强了对生物医学研究中双变二进制结果的依赖性分析的有用性.
- 这种方法可以更容易地扩展到更复杂的纵向二进制数据结构.
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