倾向性评分权重分析与复杂的调查数据用于估计人口水平治疗对生存的影响:一个模拟研究
Lihua Li1,2,3,4, Chen Yang1,2, Wei Zhang1,2
1Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, One Gustave L Levy Place, Box 1077, New York, NY 10029, USA.
Health services & outcomes research methodology
|December 15, 2025
概括
考虑复杂的调查设计的倾向性得分权重 (PSW) 方法可以改善对生存结果的治疗效应估计. 将调查设计纳入结果建模对于准确的人口水平结果至关重要.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 调查方法 调查方法
背景情况:
- 倾向性评分权重 (PSW) 用于在观察性研究中估计治疗效果.
- 将PSW应用于具有生存结果的复杂调查数据的最佳实践尚不清楚.
- 复杂的调查数据具有设计特征,如分层,集群和采样重量.
研究的目的:
- 探索将PSW与复杂的调查数据集成为不受偏见的人口水平生存结果估计.
- 根据他们对调查设计特征的核算,评估三个PSW方法.
- 为了比较估计绝对和相对治疗效果的性能.
主要方法:
- 模拟复杂的调查数据与各种场景下的生存结果.
- 评估了三种方法:I (没有设计调整),II (结果模型调整),III (两种模型调整).
- 使用平均相对偏差,平均绝对偏差和覆盖概率的比较方法.
主要成果:
- 调查加权方法II和III的表现优于未加权方法I,尤其是在真实治疗效果方面.
- 方法II和III在各种具有挑战性的场景 (例如,信息审查,异常值,不响应) 中显示了相似的性能.
- 在结果模型中考虑调查设计是最关键的.
结论:
- 在PSW中的两个建模阶段都应该包含复杂的调查数据的调查设计.
- 在结果模型中优先考虑调查设计对于准确的人口水平治疗效果估计至关重要.
- 应用于国家健康访谈调查 (NHIS) 数据的方法,以研究癌症诊断后的戒烟和生存率.
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