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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

许毛毛 高绒绒 高倩 王菊平 王佳乐 王彤

许毛毛, 高绒绒, 高倩, 王菊平, 王佳乐, 王彤. 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[J]. 中华疾病控制杂志, 2024, 28(1): 69-74. doi: 10.16462/j.cnki.zhjbkz.2024.01.011
引用本文: 许毛毛, 高绒绒, 高倩, 王菊平, 王佳乐, 王彤. 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[J]. 中华疾病控制杂志, 2024, 28(1): 69-74. doi: 10.16462/j.cnki.zhjbkz.2024.01.011
XU Maomao, GAO Rongrong, GAO Qian, WANG Juping, WANG Jiale, WANG Tong. A simulation comparison and application study of extreme weighting methods in propensity score inverse probability weighting[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(1): 69-74. doi: 10.16462/j.cnki.zhjbkz.2024.01.011
Citation: XU Maomao, GAO Rongrong, GAO Qian, WANG Juping, WANG Jiale, WANG Tong. A simulation comparison and application study of extreme weighting methods in propensity score inverse probability weighting[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(1): 69-74. doi: 10.16462/j.cnki.zhjbkz.2024.01.011

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

doi: 10.16462/j.cnki.zhjbkz.2024.01.011
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

详细信息
    通讯作者:

    王彤,E-mail: tongwang@sxmu.edu.cn

  • 中图分类号: R195.1

A simulation comparison and application study of extreme weighting methods in propensity score inverse probability weighting

Funds: 

National Natural Science Foundation of China 82073674

National Natural Science Foundation of China 82204163

Basic Research Project of Shanxi Province 202203021212382

More Information
  • 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。
  • 图  1  加权前及加权后2组个体各基线协变量均衡性情况

    IPW, 逆概率加权法;EB, 熵均衡法;SBW, 稳定权重值均衡法;OW, 重叠权重法;oCBPS, 最优协变量均衡法。

    Figure  1.  Balance of baseline covariates of individuals in the two groups before and after weighting

    IPW, inverse probability weight; EB, entropy balancing; SBW, stable weights that balance covariates; OW, overlap weights; oCBPS, optimal covariate balancing.

    表  1  IPW及替代方法在不同重叠度下的模拟表现

    Table  1.   Simulation performance of IPW and alternative methods under different overlap degrees

    重叠度
    Degree of overlap
    方法
    Method
    偏倚
    Bias
    均方根误差
    RMSE
    s $ s_{\bar{x}}$ 95% CI覆盖率
    Coverage probability
    好Good IPW 0.17 9.06 9.06 8.55 0.95
    剪切法Trimming 0.06 7.72 7.73 7.66 0.95
    OW 0.14 6.90 6.90 6.66 0.93
    oCBPS -0.02 7.34 7.34 6.95 0.93
    EB 0.12 7.00 7.00 19.20 1.00
    SBW 0.16 6.95 6.95 18.26 1.00
    中Middle IPW 1.08 25.09 25.08 17.90 0.89
    剪切法Trimming 0.24 9.44 9.44 9.37 0.95
    OW 0.21 7.52 7.52 7.36 0.94
    oCBPS 0.36 9.49 9.49 15.15 0.98
    EB 0.19 8.17 8.17 22.90 1.00
    SBW 0.33 7.88 7.87 19.48 1.00
    差Poor IPW 7.68 42.25 41.57 27.94 0.76
    剪切法Trimming 0.40 10.13 10.13 10.26 0.96
    OW 0.32 8.05 8.05 8.15 0.95
    oCBPS 1.55 13.01 15.40 76.23 1.00
    EB 0.45 9.36 12.92 26.79 1.00
    SBW 0.55 8.66 9.35 20.84 1.00
    注: IPW, 逆概率加权法; OW, 重叠权重法; oCBPS, 最优协变量均衡法; EB, 熵均衡法; SBW, 稳定权重值均衡法; RMSE, 均方根误差。
    Note : IPW, inverse probability weight; OW, overlap weights; oCBPS, optimal covariate balancing; EB, entropy balancing; SBW, stable weights that balance covariates; RMSE, root mean square error.
    下载: 导出CSV

    表  2  IPW及替代方法在模型正确指定和错误指定下的模拟表现

    Table  2.   Simulation performance of IPW and alternative methods under correct and incorrect model settings

    模型设定
    Model settings
    方法
    Method
    偏倚
    Bias
    均方根误差
    RMSE
    s $ s_{\bar{x}}$ 95% CI覆盖率
    Coverage probability
    正确Correct IPW 1.08 25.09 25.08 17.90 0.89
    剪切法Trimming 0.24 9.44 9.44 9.37 0.95
    OW 0.21 7.52 7.52 7.36 0.94
    oCBPS 0.36 9.49 9.49 15.15 0.98
    EB 0.19 8.17 8.17 22.90 1.00
    SBW 0.33 7.88 7.87 19.48 1.00
    错误Incorrect IPW 2.74 25.88 25.75 20.53 0.86
    剪切法Trimming 0.43 9.22 9.22 9.36 0.95
    OW 0.41 7.33 7.33 7.51 0.96
    oCBPS 1.23 9.63 9.55 9.88 0.95
    EB 0.22 7.76 7.76 23.25 1.00
    SBW 0.35 7.55 7.55 18.34 1.00
    注: IPW, 逆概率加权法; OW, 重叠权重法; oCBPS, 最优协变量均衡法; EB, 熵均衡法; SBW, 稳定权重值均衡法; RMSE: 均方根误差。
    Note : IPW, inverse probability weight; OW, overlap weights; oCBPS, optimal covariate balancing; EB, entropy balancing; SBW, stable weights that balance covariates; RMSE, root mean square error.
    下载: 导出CSV

    表  3  IPW及替代方法的效应估计

    Table  3.   Effect estimates of IPW and alternative methods

    方法Method 平均处理效应ATE $s_{\bar{x}} $ 95% CI P值value
    未加权 0.117 6 0.049 8 0.019 9~0.215 2 0.018
    IPW 0.200 0 0.054 6 0.093 1~0.307 0 < 0.001
    剪切法Trimming 0.187 4 0.055 7 0.078 3~0.296 6 < 0.001
    OW 0.208 6 0.054 0 0.102 7~0.314 4 < 0.001
    oCBPS 0.700 9 0.056 9 0.589 4~0.812 4 0.003
    EB 0.206 6 0.055 2 0.098 4~0.314 8 < 0.001
    SBW 0.225 2 0.055 7 0.116 0~0.334 4 < 0.001
    注: IPW, 逆概率加权法;OW, 重叠权重法;oCBPS, 最优协变量均衡法;EB, 熵均衡法;SBW, 稳定权重值均衡法。
    Note : IPW, inverse probability weight; OW, overlap weights; oCBPS, optimal covariate balancing; EB, entropy balancing; SBW, stable weights that balance covariates.
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-05-11
  • 修回日期:  2023-08-26
  • 网络出版日期:  2024-02-05
  • 刊出日期:  2024-01-10

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