Introduction of propensity score weighting methods and implementation by R software_Chinese Journal of Evidence-Based Medicine

Authors：

TU Boxiang ^1,2 ,  QIN Yingyi ¹ , WU Cheng ¹ , XU Xiao ³ ,  ZHAO Yanfang ¹

1. Department of Military Health Statistics, Naval Medical University, Shanghai 200433, P. R. China;
2. Troops 73056 of PLA, Jiangsu 232000, P. R. China;
3. School of Medicine, Tongji University, Shanghai 200092, P. R. China;

Corresponding author：

QIN Yingyi, Email: zhyf715@163.com; ZHAO Yanfang, Email: zhyf715@163.com

Keywords：

Propensity score; Inverse probability weight; Overlap weighting

DOI：

10.7507/1672-2531.202111101

Video：

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This study introduced the inverse probability weight and overlap weight by propensity score and how to test the balance and estimate the effect after weighting. Four R packages that can be used for propensity score weight analysis were introduced and compared.

Citation： TU Boxiang, QIN Yingyi, WU Cheng, XU Xiao, ZHAO Yanfang. Introduction of propensity score weighting methods and implementation by R software. Chinese Journal of Evidence-Based Medicine, 2022, 22(3): 365-372. doi: 10.7507/1672-2531.202111101 Copy

1.	韩锋, 隋福民. 因果推断中平均处理效应的估计研究. 数理统计与管理, 2015, 34(3): 427-433.
2.	Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika, 1983, 70(1): 41-55.
3.	王永吉, 蔡宏伟, 夏结来, 等. 倾向指数第一讲倾向指数的基本概念和研究步骤. 中华流行病学杂志, 2010, 31(3): 347-348.
4.	吴美京, 吴骋, 王睿, 等. 倾向性评分法中评分值的估计方法及比较. 中国卫生统计, 2013, 30(3): 440-444.
5.	王永吉, 蔡宏伟, 夏结来, 等. 倾向指数第二讲倾向指数常用研究方法. 中华流行病学杂志, 2010, 31(5): 584-5.
6.	李智文, 刘建蒙, 任爱国. 基于个体的标准化法——倾向评分加权. 中华流行病学杂志, 2010, 31(2): 223-226.
7.	Li F, Morgan KL, Zaslavsky AM. Balancing covariates via propensity score weighting. J Am Stat Assoc, 2018, 113(521): 390-400.
8.	Crump RK, Hotz VJ, Imbens GW, et al. Dealing with limited overlap in estimation of average treatment effects. Biometrika, 2009, 96(1): 187-99.
9.	Desai RJ, Franklin JM. Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners. BMJ, 2019, 367: l5657.
10.	Mlcoch T, Hrnciarova T, Tuzil J, et al. Propensity score weighting using overlap weights: a new method applied to regorafenib clinical data and a cost-effectiveness analysis. Value Health, 2019, 22(12): 1370-1377.
11.	秦宇辰, 郭威, 阮一鸣, 等. 重叠加权法在医学研究混杂因素控制中的应用. 中国卫生统计, 2020, 37(3): 363-366.
12.	Austin PC. The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies. Med Decis Making, 2009, 29(6): 661-677.
13.	Schober P, Mascha EJ, Vetter TR. Statistics from A (agreement) to Z (z score): a guide to interpreting common measures of association, agreement, diagnostic accuracy, effect size, heterogeneity, and reliability in medical research. Anesth Analg, 2021, 133(6): 1633-1641.
14.	Zhang Z, Kim HJ, Lonjon G, et al. Balance diagnostics after propensity score matching. Ann Transl Med, 2019, 7(1): 16.
15.	王永吉, 蔡宏伟, 夏结来, 等. 倾向指数第三讲应用中的关键问题. 中华流行病学杂志, 2010, 31(7): 823-825.
16.	胡克震. Kolmogorov—Smirnov检验法的应用. 中国卫生统计, 1985, (3): 12-17.
17.	McCaffrey DF, Griffin BA, Almirall D, et al. A tutorial on propensity score estimation for multiple treatments using generalized boosted models. Stat Med, 2013, 32(19): 3388-3414.
18.	McCaffrey DF, Ridgeway G, Morral AR. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychol Methods, 2004, 9(4): 403-425.
19.	金勇进, 张喆. 抽样调查中的权数问题研究. 统计研究, 2014, 31(9): 79-84.
20.	Kereiakes DJ, Obenchain RL, Barber BL, et al. Abciximab provides cost-effective survival advantage in high-volume interventional practice. Am Heart J, 2000, 140(4): 603-610.
21.	Mao H and Li L. PSW: propensity score weighting methods for dichotomous treatments. R package version 1.1-3. Avaiable at: https://CRAN.R-project.org/package=PSW.
22.	Zhou T, Tong G, Li F, et al. PSweight: propensity score weighting for causal inference with observational studies and randomized trials. R package version 1.1. 5. Avaiable at: https://CRAN.R-project.org/package=PSweight.
23.	Cefalu M, Ridgeway G, McCaffrey D, et al. (2021). twang: toolkit for weighting and analysis of nonequivalent groups. R package version 2.5. Avaiable at: https://CRAN.R-project.org/package=twang.
24.	Ridgeway G, Mccaffrey D, Morral A, et al. Toolkit for weighting and analysis of nonequivalent groups: a guide to the twang package. Avaiable at: https://cran.r-project.org/web/packages/twang/vignettes/twang.pdf.
25.	David H. hrIPW: hazard ratio estimation using Cox model weighted by the estimated propensity score. R package version 0.1. 3. Avaiable at: https://CRAN.R-project.org/package=hrIPW.

1. 韩锋, 隋福民. 因果推断中平均处理效应的估计研究. 数理统计与管理, 2015, 34(3): 427-433.
2. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika, 1983, 70(1): 41-55.
3. 王永吉, 蔡宏伟, 夏结来, 等. 倾向指数第一讲倾向指数的基本概念和研究步骤. 中华流行病学杂志, 2010, 31(3): 347-348.
4. 吴美京, 吴骋, 王睿, 等. 倾向性评分法中评分值的估计方法及比较. 中国卫生统计, 2013, 30(3): 440-444.
5. 王永吉, 蔡宏伟, 夏结来, 等. 倾向指数第二讲倾向指数常用研究方法. 中华流行病学杂志, 2010, 31(5): 584-5.
6. 李智文, 刘建蒙, 任爱国. 基于个体的标准化法——倾向评分加权. 中华流行病学杂志, 2010, 31(2): 223-226.
7. Li F, Morgan KL, Zaslavsky AM. Balancing covariates via propensity score weighting. J Am Stat Assoc, 2018, 113(521): 390-400.
8. Crump RK, Hotz VJ, Imbens GW, et al. Dealing with limited overlap in estimation of average treatment effects. Biometrika, 2009, 96(1): 187-99.
9. Desai RJ, Franklin JM. Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners. BMJ, 2019, 367: l5657.
10. Mlcoch T, Hrnciarova T, Tuzil J, et al. Propensity score weighting using overlap weights: a new method applied to regorafenib clinical data and a cost-effectiveness analysis. Value Health, 2019, 22(12): 1370-1377.
11. 秦宇辰, 郭威, 阮一鸣, 等. 重叠加权法在医学研究混杂因素控制中的应用. 中国卫生统计, 2020, 37(3): 363-366.
12. Austin PC. The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies. Med Decis Making, 2009, 29(6): 661-677.
13. Schober P, Mascha EJ, Vetter TR. Statistics from A (agreement) to Z (z score): a guide to interpreting common measures of association, agreement, diagnostic accuracy, effect size, heterogeneity, and reliability in medical research. Anesth Analg, 2021, 133(6): 1633-1641.
14. Zhang Z, Kim HJ, Lonjon G, et al. Balance diagnostics after propensity score matching. Ann Transl Med, 2019, 7(1): 16.
15. 王永吉, 蔡宏伟, 夏结来, 等. 倾向指数第三讲应用中的关键问题. 中华流行病学杂志, 2010, 31(7): 823-825.
16. 胡克震. Kolmogorov—Smirnov检验法的应用. 中国卫生统计, 1985, (3): 12-17.
17. McCaffrey DF, Griffin BA, Almirall D, et al. A tutorial on propensity score estimation for multiple treatments using generalized boosted models. Stat Med, 2013, 32(19): 3388-3414.
18. McCaffrey DF, Ridgeway G, Morral AR. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychol Methods, 2004, 9(4): 403-425.
19. 金勇进, 张喆. 抽样调查中的权数问题研究. 统计研究, 2014, 31(9): 79-84.
20. Kereiakes DJ, Obenchain RL, Barber BL, et al. Abciximab provides cost-effective survival advantage in high-volume interventional practice. Am Heart J, 2000, 140(4): 603-610.
21. Mao H and Li L. PSW: propensity score weighting methods for dichotomous treatments. R package version 1.1-3. Avaiable at: https://CRAN.R-project.org/package=PSW.
22. Zhou T, Tong G, Li F, et al. PSweight: propensity score weighting for causal inference with observational studies and randomized trials. R package version 1.1. 5. Avaiable at: https://CRAN.R-project.org/package=PSweight.
23. Cefalu M, Ridgeway G, McCaffrey D, et al. (2021). twang: toolkit for weighting and analysis of nonequivalent groups. R package version 2.5. Avaiable at: https://CRAN.R-project.org/package=twang.
24. Ridgeway G, Mccaffrey D, Morral A, et al. Toolkit for weighting and analysis of nonequivalent groups: a guide to the twang package. Avaiable at: https://cran.r-project.org/web/packages/twang/vignettes/twang.pdf.
25. David H. hrIPW: hazard ratio estimation using Cox model weighted by the estimated propensity score. R package version 0.1. 3. Avaiable at: https://CRAN.R-project.org/package=hrIPW.

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