对逻辑回归模型的差异估计器进行比较,用概括估计方程 (GEE) 进行估计,用于观察性医疗服务研究
Peter C Austin1,2,3
1ICES, Toronto, Ontario, Canada.
Statistics in medicine
|October 31, 2024
概括
对于集群数据分析,根据集群计数选择差异估计器. 小集群 (<15) 需要考尔曼-卡罗尔;中等 (15-40) 使用费-格劳巴德;大 (>40) 可以使用梁泽格或费-格劳巴德.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 生物统计学 生物统计学
- 统计建模 统计建模
背景情况:
- 一般化估计方程 (GEE) 在卫生服务研究中广泛用于集群数据.
- 在GEE中的Liang-Zeger差异估计器可以产生偏向的低标准误差,只有少数集群.
- 关于替代差异估计器的现有研究主要考虑使用更简单模型集群随机试验.
研究的目的:
- 评估多重共变量和集群数据的后勤回归模型中集群级共变量的首选差异估计器.
- 为了确定在这些设置中对梁泽格差异估计器可接受的性能所需的最小集群数量.
主要方法:
- 在不同集群大小下,模拟研究比较了各种差异估计器 (Liang-Zeger,Kauermann-Carroll,Fay-Graubard,Mancl-DeRouen) 的性能.
- 专注于物流回归模型,包括个人和集群级别的共变量.
- 评估标准误差偏差和置信区间覆盖范围.
主要成果:
- 对于<15个集群的标准误差估计,建议使用考尔曼-卡罗尔估计器.
- 对于15-40个星团,Fay-Graubard估计器是最好的.
- 对于>40个星团,Liang-Zeger或Fay-Graubard估计器是合适的. 对于置信区间,建议使用带有t分布的Mancl-DeRouen估计器.
结论:
- 在GEE中选择差异估计器至关重要,并且取决于集群的数量.
- 为不同的集群大小范围提供了具体建议,以确保准确的标准错误和置信区间估计.
- 这些发现有助于研究人员选择适当的统计方法来分析复杂的集群医疗服务数据.
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