• 中国精品科技期刊
  • 《中文核心期刊要目总览》收录期刊
  • RCCSE 中国核心期刊(5/114,A+)
  • Scopus收录期刊
  • 美国《化学文摘》(CA)收录期刊
  • WHO 西太平洋地区医学索引(WPRIM)收录期刊
  • 《中国科学引文数据库(CSCD)》核心库期刊 (C)
  • 中国科技核心期刊
  • 中国科技论文统计源期刊
  • 《日本科学技术振兴机构数据库(中国)》(JSTChina)收录期刊
  • 美国《乌利希期刊指南》(UIrichsweb)收录期刊
  • 中华预防医学会系列杂志优秀期刊(2019年)

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

倾向性评分与疾病风险评分匹配对已测量混杂因素的控制效果评价

卢珍珍 赵恩慧 黄丽红

卢珍珍, 赵恩慧, 黄丽红. 倾向性评分与疾病风险评分匹配对已测量混杂因素的控制效果评价[J]. 中华疾病控制杂志, 2024, 28(2): 241-248. doi: 10.16462/j.cnki.zhjbkz.2024.02.018
引用本文: 卢珍珍, 赵恩慧, 黄丽红. 倾向性评分与疾病风险评分匹配对已测量混杂因素的控制效果评价[J]. 中华疾病控制杂志, 2024, 28(2): 241-248. doi: 10.16462/j.cnki.zhjbkz.2024.02.018
LU Zhenzhen, ZHAO Enhui, HUANG Lihong. Evaluation of the control effect for measured confounders between propensity score matching and disease risk score matching[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(2): 241-248. doi: 10.16462/j.cnki.zhjbkz.2024.02.018
Citation: LU Zhenzhen, ZHAO Enhui, HUANG Lihong. Evaluation of the control effect for measured confounders between propensity score matching and disease risk score matching[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(2): 241-248. doi: 10.16462/j.cnki.zhjbkz.2024.02.018

倾向性评分与疾病风险评分匹配对已测量混杂因素的控制效果评价

doi: 10.16462/j.cnki.zhjbkz.2024.02.018
卢珍珍和赵恩慧为共同第一作者
基金项目: 

国家自然科学基金 82273733

详细信息
    通讯作者:

    黄丽红,E-mail: huang.lihong@zs-hospital.sh.cn

  • 中图分类号: R181.3

Evaluation of the control effect for measured confounders between propensity score matching and disease risk score matching

LU Zhenzhen and ZHAO Enhui contributed equally to this article
Funds: 

National Natural Science Foundation of China 82273733

More Information
  • 摘要:   目的  比较在组间倾向性评分(propensity score, PS)重叠较好和较差的场景下应用PS和疾病风险评分(disease risk score, DRS)进行1∶1匹配的效果,同时探索DRS匹配的最优卡钳值。  方法  设置不同的试验组样本量占比、结局事件发生率和PS重叠情况,模拟6种场景比较PS和DRS匹配前后的协变量均衡性和处理效应估计偏差,并进行实例分析。  结果  PS重叠较好的场景下,DRS重叠也较好,PS匹配优于DRS,PS匹配最优卡钳值为标准差的10%~20%,DRS匹配相对最优卡钳值为标准差的0.5%。在PS重叠较差的场景中,DRS重叠也变差,但DRS匹配优于PS,DRS匹配最优卡钳值为标准差的15%~20%。此外,PS和DRS匹配对协变量均衡性的改善效果与处理效应估计偏差相一致。  结论  当PS重叠较好时,优选PS匹配;当PS重叠较差时,可选DRS,其最优卡钳值为标准差的15%~20%。在实际应用中,可根据匹配前后组间协变量均衡性指标的改善情况评价匹配效果。
  • 图  1  不同场景设置下组间PS和DRS分布情况

    PS:倾向性评分;DRS:疾病风险评分;A: 场景A1组间PS分布;B: 场景A1组间DRS分布;C: 场景A2组间PS分布;D: 场景A2组间DRS分布;E: 场景A3组间PS分布;F: 场景B3组间DRS分布;G: 场景B1组间PS分布;H: 场景B1组间DRS分布;I: 场景B2组间PS分布;J: 场景B2组间DRS分布;K: 场景B3组间PS分布;L: 场景B3组间DRS分布。

    Figure  1.  Distribution of PS and DRS between groups under different scenarios

    PS: propensity score; DRS: disease risk score; A: PS distribution between groups in scenario A1; B: DRS distribution between groups in scenario A1; C: PS distribution between groups in scenario A2; D: DRS distribution between groups in scenario A2; E: PS distribution in scenario A3; F: DRS distribution between groups in scenario B3; G: PS distribution between groups in scenario B1; H: DRS distribution between groups in scenario B1; I: PS distribution between groups in scenario B2; J: DRS distribution between groups in scene B2; K: PS distribution in scenario B3; L: DRS distribution between groups in scenario B3.

    图  2  不同场景匹配前后组间各协变量标准化差异绝对值

    PS:倾向性评分,DRS:疾病风险评分,SD:标准化差异;场景A1~A3中PS卡钳值设置为标准差的20%,DRS卡钳值设置为标准差的0.05%;场景B1~B3中PS和DRS卡钳值均设置为标准差的20%。

    Figure  2.  Absolute value of standardized difference of covariables between two groups before and after matching under different scenarios

    PS: propensity score, DRS: disease risk score, SD: standardized difference; In the scenario A1-A3, the caliper width of PS matching is set to 20% of the standard deviation, and the caliper width of DRS is set to 0.05% of the standard deviation; both the caliper widths of PS matching and DRS matching in the scene B1-B3 are set to 20% of the standard deviation.

    图  3  PS与DRS分布及匹配效果

    PS:倾向性评分;DRS:疾病风险评分;APACHE Ⅲ: 急性生理与慢性健康Ⅲ评分;A: 匹配前组间DRS分布;B: 匹配前组间DRS分布;C: 匹配前后组间各协变量标准化差异绝对值。

    Figure  3.  Distribution and matching effect of PS and DRS

    PS: propensity score, DRS: disease risk score; A: distribution of PS between group; APACHE Ⅲ: acute physiology and chronic health evaluation Ⅲ; B: distribution of DRS between group; C: absolute value of standardized difference of covariables between two groups before and after matching.

    表  1  协变量与分组因素和结局的关联

    Table  1.   The correlation with smoking and outcome

    与吸烟关联
    Association with smoking
    与某疾病关联
    Association with a certain disease
    有关
    Associate
    无关
    Not associate
    有关  Associate x1, x2, x3, b1, b2, b3, b4, b5 x5, b6, b7
    无关  Not associate x4, b8, b9 x6, b10
    下载: 导出CSV

    表  2  模拟试验场景参数设置

    Table  2.   Parameter setting of simulation scenarios

    场景
    Scenarios
    组间PS分布重叠
    PS overlap
    发生疾病的比例/%
    Disease incident /%
    分组模型系数设置
    Grouping model coefficient setting
    c(α0, α1, α2, α3, α4, α5, α6, α7, α8, α9, α10, α11)
    结局模型系数设置
    Outcome model coefficient setting
    c(β0, β1, β2, β3, β4, β5, β6, β7, β8, β9, β10, β11)
    A1 较好  Good 30 c(-1.5, -0.6, 0.5, 0.5, -0.3, 0.6, -0.2, 0.2, 0.3, 0.5, -0.5, -0.2) c(-1.5, 0.5, -0.5, 0.5, -0.2, -0.5, 0.6, 0.5, 0.2, -0.5, 0.5, -0.2)
    A2 较好  Good 20 c(-1.5, -0.6, 0.5, 0.5, -0.3, 0.6, -0.2, 0.2, 0.3, 0.5, -0.5, -0.2) c(-2.1, 0.5, -0.5, 0.5, -0.2, -0.5, 0.6, 0.5, 0.2, -0.5, 0.5, -0.2)
    A3 较好  Good 10 c(-1.5, -0.6, 0.5, 0.5, -0.3, 0.6, -0.2, 0.2, 0.3, 0.5, -0.5, -0.2) c(-3, 0.5, -0.5, 0.5, -0.2, -0.5, 0.6, 0.5, 0.2, -0.5, 0.5, -0.2)
    B1 较差  Poor 30 c(-1.5, 1.5, -1.2, 1.5, -1.1, -1.2, 0.5, -1, 1.5, -1.2, 1.6, -1.1) c(-1.5, 0.5, -0.5, 0.5, -0.2, -0.5, 0.6, 0.5, 0.2, -0.5, 0.5, -0.2)
    B2 较差  Poor 20 c(-1.5, 1.5, -1.2, 1.5, -1.1, -1.2, 0.5, -1, 1.5, -1.2, 1.6, -1.1) c(-2.2, 0.5, -0.5, 0.5, -0.2, -0.5, 0.6, 0.5, 0.2, -0.5, 0.5, -0.2)
    B3 较差  Poor 10 c(-1.5, 1.5, -1.2, 1.5, -1.1, -1.2, 0.5, -1, 1.5, -1.2, 1.6, -1.1) c(-3, 0.5, -0.5, 0.5, -0.2, -0.5, 0.6, 0.5, 0.2, -0.5, 0.5, -0.2)
    注:PS,倾向性评分。
    Note: PS, propensity score.
    下载: 导出CSV

    表  3  不同场景下模拟试验结果

    Table  3.   Simulation results under different scenarios

    场景
    Scenario
    匹配方法
    Matching method
    卡钳值/%
    Caliper width/%
    匹配比例/%
    Matching ratio /%
    RB/% MSE/% 95% CI覆盖率/%
    95% CI coverage rate/ %
    检验效能/%
    Power/%
    场景A1  Scenario A1 PS法  PS method 30 97.0 10.9 6.1 72.8 99.5
    25 95.2 10.2 5.8 76.2 99.8
    20 93.1 9.4 5.4 79.6 99.9
    15 90.8 8.7 5.1 82.9 99.9
    10 88.5 8.1 4.9 85.5 99.9
    5 86.5 7.6 4.8 87.0 99.9
    DRS法  DRS method 30 100.0 13.6 7.1 58.7 99.7
    25 100.0 13.6 7.1 58.7 99.7
    20 100.0 13.6 7.1 58.7 99.7
    15 100.0 13.6 7.1 58.7 99.7
    10 100.0 13.6 7.1 58.7 99.7
    5 99.9 13.6 7.1 58.7 99.7
    0.5 99.8 13.6 7.1 59.1 99.6
    0.05 93.8 13.1 6.9 66.6 99.4
    0.01 61.7 12.0 8.1 81.0 92.7
    场景A2  Scenario A2 PS法 PS method 30 94.3 12.6 7.6 69.6 97.6
    25 91.2 11.3 7.1 76.3 98.2
    20 87.6 9.8 6.5 79.0 98.5
    15 84.0 8.5 6.1 84.1 99.1
    10 80.6 7.3 5.9 87.8 99.1
    5 78.1 6.4 5.8 90.1 99.6
    DRS法  DRS method 30 100.0 13.6 7.1 58.7 99.7
    25 100.0 13.6 7.1 58.7 99.7
    20 100.0 13.6 7.1 58.7 99.7
    15 100.0 13.6 7.1 58.7 99.7
    10 100.0 13.6 7.1 58.7 99.7
    5 99.9 14.0 7.9 61.3 98.4
    0.5 99.7 13.6 6.3 62.1 98.4
    0.05 94.4 13.3 7.8 68.0 97.8
    0.01 68.2 12.3 9.3 81.2 88.5
    场景A3  Scenario A3 PS法  PS method 30 94.3 10.3 10.1 84.0 86.2
    25 91.2 9.0 9.8 86.9 87.8
    20 87.6 7.5 9.7 89.7 89.5
    15 84.0 6.0 9.8 92.1 89.9
    10 80.6 4.7 10.0 92.0 90.5
    5 78.1 3.9 10.2 93.2 91.0
    DRS法  DRS method 30 100.0 13.6 7.1 58.7 99.7
    25 100.0 13.6 7.1 58.7 99.7
    20 100.0 13.6 7.1 58.7 99.7
    15 100.0 13.6 7.1 58.7 99.7
    场景A3  Scenario A3 DRS法  DRS method 10 100.0 13.6 7.1 58.7 99.7
    5 99.9 12.3 9.7 82.4 85.3
    0.5 99.8 12.1 9.7 82.7 85.8
    0.05 97.0 11.4 10.0 85.1 83.4
    0.01 81.8 10.4 12.5 87.7 72.1
    场景B1  Scenario B1 PS法 PS method 30 99.9 54.5 88.8 0.0 100.0
    25 90.1 40.9 52.7 0.9 100.0
    20 76.7 25.7 24.6 25.7 100.0
    15 65.1 13.5 11.3 75.3 100.0
    10 55.6 4.3 6.6 94.2 100.0
    5 48.7 2.5 6.1 94.5 99.1
    DRS法  DRS method 30 92.3 9.4 6.0 81.7 100.0
    25 88.7 3.5 3.6 95.0 100.0
    20 84.8 2.4 3.0 96.6 100.0
    15 80.7 7.7 4.2 84.9 100.0
    10 76.8 12.2 6.5 66.5 99.9
    5 73.8 15.2 8.6 52.7 99.0
    0.5 71.9 16.4 9.7 45.3 97.5
    0.05 63.5 16.3 9.9 51.9 95.3
    0.01 40.0 16.2 11.4 70.9 76.0
    场景B2  Scenario B2 PS法  PS method 30 99.9 60.4 109.1 0.0 100.0
    25 90.1 45.7 66.2 1.5 100.0
    20 76.7 29.8 32.6 23.8 100.0
    15 65.1 17.2 16.0 68.9 100.0
    10 55.6 7.2 8.9 92.9 99.8
    5 48.7 0.4 7.7 95.9 98.2
    DRS法  DRS method 30 92.6 13.5 9.4 71.1 100.0
    25 89.4 7.3 5.4 92.4 100.0
    20 85.7 0.8 3.5 97.6 100.0
    15 81.6 5.4 3.8 94.4 100.0
    10 77.5 10.7 5.9 80.6 99.2
    5 74.1 14.3 8.3 65.7 97.1
    0.5 72.0 15.7 9.4 59.9 94.7
    0.05 64.3 15.6 10.0 65.8 88.2
    0.01 44.1 15.2 12.5 75.1 68.0
    场景B3  Scenario B3 PS法  PS method 30 99.9 68.9 148.0 0.1 100.0
    25 90.1 52.7 92.7 4.0 100.0
    20 76.7 35.5 49.6 31.5 100.0
    15 65.1 22.0 27.6 70.8 99.9
    10 55.7 11.1 17.2 90.2 98.9
    5 48.7 2.7 14.2 95.4 91.3
    DRS法  DRS method 30 95.7 25.1 26.3 45.4 100.0
    25 93.3 18.7 17.4 68.8 100.0
    20 90.1 11.1 10.1 87.6 100.0
    15 86.0 2.7 6.1 96.5 100.0
    10 81.0 5.3 5.8 95.0 98.5
    5 75.9 11.9 8.3 84.6 89.5
    0.5 72.6 14.5 10.2 76.0 80.9
    0.05 66.8 14.0 10.7 80.6 76.1
    0.01 51.0 13.7 14.6 85.8 55.5
    注:PS,倾向性评分;DRS,疾病风险评分;RB,相对偏倚;MSE,均方误差。
    Note:PS,propensity score;DRS,disease risk score;RB,relative bias;MSE,mean-square error.
    下载: 导出CSV

    表  4  不同匹配法的分析结果

    Table  4.   Analysis results of different matching methods

    方法 Methods 匹配比例/%
    Matching ratio/%
    60 d死亡人数(占比/%)
    Number of deaths within 60 days (proportion/%)
    OR值  value
    (95% CI)
    P值  value
    非血小板减少组
    Non-thrombocytopenic group
    血小板减少组
    Thrombocytopenia group
    PS匹配  PS matching 82.3 27(30.3) 42(47.2) 2.05(1.12~3.82) 0.022
    DRS匹配  DRS matching 92.0 39(37.5) 53(50.9) 1.73(0.99~3.02) 0.051
    注:PS,倾向性评分;DRS,疾病风险评分。
    Note: PS, propensity score;DRS, disease risk score.
    下载: 导出CSV
  • [1] Paul, R, Rosenbaum, et al. The central role of the propensity score in observational studies for causal effects [J]. Biometrika, 1983, 70(1): 41-55. DOI: 10.1093/biomet/70.1.41.
    [2] Miettinen OS. Stratification by a multivariate confounder score [J]. Am J Epidemiol, 1976, 104(6): 609-620. DOI: 10.1080/0002889768507553.
    [3] Arbogast PG, Ray WA. Performance of disease risk scores, propensity scores, and traditional multivariable outcome regression in the presence of multiple confounders [J]. Am J Epidemiol, 2011, 174(5): 613-620. DOI: 10.1093/aje/kwr143.
    [4] Wang Y, Cai H, Li C, et al. Optimal caliper width for propensity score matching of three treatment groups: a Monte Carlo study [J]. PLoS One, 2013, 8(12): e81045. DOI: 10.1371/journal.pone.0081045.
    [5] Elze MC, Gregson J, Baber U, et al. Comparison of propensity score methods and covariate adjustment [J]. J Am Coll Cardiol, 2017, 69(3): 345-347. DOI: 10.1016/j.jacc.2016.10.060.
    [6] Benedetto U, Head SJ, Angelini GD, et al. Statistical primer: propensity score matching and its alternatives [J]. Eur J Cardiothorac Surg, 2018, 53(6): 1112-1117. DOI: 10.1093/ejcts/ezy167.
    [7] Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies [J]. Pharm Stat, 2011, 10(2): 150-161. DOI: 10.1002/pst.433.
    [8] 黄丽红, 王永吉, 王素珍, 等. 倾向性评分方法及其规范化应用的统计学共识CSCO生物统计学专家委员会RWS方法学组[J]. 中国卫生统计, 2020, 37(6): 952-958. DOI: 10.3969/j.issn.1002-3674.2020.06.041.

    Huang LH, Wang YJ, Wang SZ, et al. Statistical consensus on propensity score method and its standardized application [J]. Chinese Journal of Health Statistics, 2020, 37(6): 952-958. DOI: 10.3969/j.issn.1002-3674.2020.06.041.
    [9] Connolly JG, Gagne JJ. Comparison of calipers for matching on the disease risk score [J]. Am J Epidemiol, 2016, 183(10): 937-948. DOI: 10.1093/aje/kwv302.
    [10] 黄丽红, 赵杨, 魏永越, 等. 如何控制观察性疗效比较研究中的混杂因素: (一)已测量混杂因素的统计学分析方法[J]. 中华流行病学杂志, 2019, 40(12): 1645-1649. DOI: 10.3760/cma.j.issn.0254-6450.2019.10.024.

    Huang LH, Zhao Y, Wei YY, et al. Confounder adjustment in observational comparative effectiveness researches: (1) statistical adjustment approaches for measured confounder [J]. Chin J Epidemiol, 2019, 40(12): 1645-1649. DOI: 10.3760/cma.j.issn.0254-6450.2019.10.024.
    [11] Zhang D, Kim J. Use of propensity score and disease risk score for multiple treatments with time-to-event outcome: a simulation study [J]. J Biopharm Stat, 2019, 29(6): 1103-1115. DOI: 10.1080/10543406.2019.1584205.
    [12] Wyss R, Ellis AR, Brookhart MA, et al. Matching on the disease risk score in comparative effectiveness research of new treatments [J]. Pharmacoepidemiol Drug Saf, 2015, 24(9): 951-961. DOI: 10.1002/pds.3810.
    [13] Desai RJ, Glynn RJ, Wang S, et al. Performance of disease risk score matching in nested case-control studies: a simulation study [J]. Am J Epidemiol, 2016, 183(10): 949-957. DOI: 10.1093/aje/kwv269.
    [14] Li Y, Li L. Propensity score analysis methods with balancing constraints: a monte c arlo study [J]. Stat Methods Med Res, 2021, 30(4): 1119-1142. DOI: 10.1177/0962280220983512.
    [15] 黄丽红, 陈峰. 倾向性评分方法及其应用[J]. 中华预防医学杂志, 2019, 53(7): 752-756. DOI: 10.3760/cma.j.issn.0253-9624.2019.07.017.

    Huang LH, Chen F. The propensity score method and its application [J]. Chin J Prev Med, 2019, 53(7): 752-756. DOI: 10.3760/cma.j.issn.0253-9624.2019.07.017.
    [16] Reiffel JA. Propensity-score matching: the "devil is in the details" where more may be hidden than you know [J]. Am J Med, 2020, 133(2): 178-181. DOI: 10.1016/j.amjmed.2019.08.055.
  • 加载中
图(3) / 表(4)
计量
  • 文章访问数:  25
  • HTML全文浏览量:  26
  • PDF下载量:  4
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-03-22
  • 修回日期:  2023-07-04
  • 网络出版日期:  2024-03-30
  • 刊出日期:  2024-02-10

目录

    /

    返回文章
    返回