倾向性评分逆概率加权中极端权重处理方法的模拟比较及应用研究
A simulation comparison and application study of extreme weighting methods in propensity score inverse probability weighting
许毛毛 1高绒绒 1高倩 1王菊平 1王佳乐 1王彤1
作者信息
- 1. 山西医科大学公共卫生学院卫生统计学教研室,煤炭环境致病与防治教育部重点实验室,太原 030001
- 折叠
摘要
目的 通过模拟研究比较倾向性评分逆概率加权法(inverse probability weighting,IPW)及其5种替代方法在有限重叠和倾向评分模型错误指定下的性能,并应用这些方法探讨血清总25-羟基维生素D[25-hydroxyvitamin D,25(OH)D]缺乏与成年人睡眠时间的关系.方法 通过蒙特卡洛模拟,设置不同样本量、倾向性评分重叠度、模型指定情况的模拟场景,比较6种方法的统计性能.结果 模拟结果显示,IPW对重叠度差和模型错误指定比较敏感,其替代方法可表现出更强的稳定性和更高的效率,其中重叠权重(overlap weights,OW)法可提供最好的效应估计.血清总25(OH)D缺乏者睡眠时间比血清总25(OH)D充足者少(P＜0.001).结论 OW可作为存在极端权重时IPW方法的最优替代,血清总25(OH)D缺乏会减少成年人的睡眠时间.
Abstract
Objective This paper proposes a simulation study to compare the performance of propensity score inverse probability weighting(IPW)and its five alternatives under limited overlap and propensity score model misspecification.Additionally,it seeks to employ these methods to explore the relationship between serum total 25-hydroxyvitamin D[25(OH)D]deficiency and sleep duration in adults.Methods The statistical performance of these six methods was compared through Monte Carlo simulations by setting up simulation scenarios with different sample sizes,propensity score overlap,and model-specified cases.Results Simulation results showed that IPW is particularly sensitive to differences in overlap and model misspecification,while alternative methods showed greater stability and higher efficiency.Notably,the overlap weights(OW)method provided the most accurate effect estimates.It was observed that adults with total serum 25(OH)D deficiency have shorter sleep duration compared to those with adequate total serum 25(OH)D(P＜0.001).Conclusions The OW method can be used as an optimal alternative to the IPW method in the presence of extreme weights.The study also concludes that serum total 25(OH)D deficiency reduces sleep duration in adults.
关键词
倾向性评分/逆概率加权/极端权重/25-羟基维生素D/睡眠时间Key words
Propensity score/Inverse probability weighting/Extreme weights/25-hydroxyvita-min D/Sleep duration引用本文复制引用
基金项目
国家自然科学基金(82073674)
国家自然科学基金(82204163)
山西省基础研究计划资助项目(202203021212382)
出版年
2024
NETL
NSTL
万方数据