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.	韓鋒, 隋福民. 因果推斷中平均處理效應的估計研究. 數理統計與管理, 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, 蔡宏偉, 夏結來, 等. 傾向指數第一講傾向指數的基本概念和研究步驟. 中華流行病學雜志, 2010, 31(3): 347-348.
4.	吳美京, 吳騁, 王睿, 等. 傾向性評分法中評分值的估計方法及比較. 中國衛生統計, 2013, 30(3): 440-444.
5.	王永吉, 蔡宏偉, 夏結來, 等. 傾向指數第二講傾向指數常用研究方法. 中華流行病學雜志, 2010, 31(5): 584-5.
6.	李智文, 劉建蒙, 任愛國. 基于個體的標準化法——傾向評分加權. 中華流行病學雜志, 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, 阮一鳴, 等. 重疊加權法在醫學研究混雜因素控制中的應用. 中國衛生統計, 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, 蔡宏偉, 夏結來, 等. 傾向指數第三講應用中的關鍵問題. 中華流行病學雜志, 2010, 31(7): 823-825.
16.	胡克震. Kolmogorov—Smirnov檢驗法的應用. 中國衛生統計, 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.	金勇進, 張喆. 抽樣調查中的權數問題研究. 統計研究, 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. 韓鋒, 隋福民. 因果推斷中平均處理效應的估計研究. 數理統計與管理, 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, 蔡宏偉, 夏結來, 等. 傾向指數第一講傾向指數的基本概念和研究步驟. 中華流行病學雜志, 2010, 31(3): 347-348.
4. 吳美京, 吳騁, 王睿, 等. 傾向性評分法中評分值的估計方法及比較. 中國衛生統計, 2013, 30(3): 440-444.
5. 王永吉, 蔡宏偉, 夏結來, 等. 傾向指數第二講傾向指數常用研究方法. 中華流行病學雜志, 2010, 31(5): 584-5.
6. 李智文, 劉建蒙, 任愛國. 基于個體的標準化法——傾向評分加權. 中華流行病學雜志, 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, 阮一鳴, 等. 重疊加權法在醫學研究混雜因素控制中的應用. 中國衛生統計, 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, 蔡宏偉, 夏結來, 等. 傾向指數第三講應用中的關鍵問題. 中華流行病學雜志, 2010, 31(7): 823-825.
16. 胡克震. Kolmogorov—Smirnov檢驗法的應用. 中國衛生統計, 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. 金勇進, 張喆. 抽樣調查中的權數問題研究. 統計研究, 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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