Optimal caliper width for propensity score matching of three treatment groups: A Monte Carlo study

PLoS ONE

Volumn 8, Issue 12, 2013, Pages

  Wang, Yongji ^a,b   Cai, Hongwei ^b   Li, Chanjuan ^b   Jiang, Zhiwei ^b   Wang, Ling ^b   Song, Jiugang ^a   Xia, Jielai ^b  

^a 309TH HOSPITAL OF PLA   (China)

^b FOURTH MILITARY MEDICAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CALIPER; CLINICAL STUDY; CONTROLLED STUDY; MATCHING RATIO; MEAN SQUARED ERROR; MEASUREMENT; MONTE CARLO METHOD; MULTIPLE TREATMENT GROUP; OPTIMAL CALIPER WIDTH; PROPENSITY SCORE; STATISTICAL BIAS; STATISTICAL CONCEPTS; THREE TREATMENT GROUP; TWO TREATMENT GROUP;

HUMANS; MODELS, STATISTICAL; MODELS, THEORETICAL; MONTE CARLO METHOD; PROPENSITY SCORE;

EID: 84892607349     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0081045     Document Type: Article

References (19)

1. Joffe MM, Rosenbaum PR (1999) Invited commentary: propensity scores. Am J Epidemiol 150:327-33.
2. D'Agostino RB (1998) Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 17:2265-81.
3. Austin PC (2008) A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Stat Med 27:2037-49.
4. Rosenbaum PR, Rubin DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70:41-55.
5. Katherine HH, Thomas AL (2002) Propensity score modeling strategies for the causal analysis of observational data. Biostatistics 2:179-93.
6. AustinPC (2007) Propensity-score matching in the cardiovascular surgery literature from 2004 to 2006: Asystematic review and suggestions for improvement. J Thorac Cardiovasc Surg134:1128-35.
7. Imbens GW (2000) The role of the propensity score in estimating dose-responsefunctions. Biometrika 87:706-10.
8. Wang JX, Donnan PT, Steinke D, MacDonald TM (2001) The multiple propensity scorefor analysis of dose-response relationships in drug safety studies. Pharmacoepidemiol Drug Saf 10:105-11.
9. Spreeuwenberg MD, Bartak A, Croon MA, Hagenaars JA, Busschbach JJV, et al (2010) The multiple propensity score as control for bias in the comparison of more than two treatment arms: an introduction from a case study in mental health. Med Care 48:166-74.
10. Austin PC (2009) Some methods of propensity-score matching had superior performance to others: results of an empirical investigation and Monte Carlo simulations. Biom J 51: 171-84.
11. Ho DE, Imai K, King G, Stuart EA (2007) Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis 15:199-236.
12. Rosenbaum PR (1989) Optimal matching for observational studies. J Am Stat Assoc 84:1024-32.
13. CochranWG, RubinDB (1973) Controlling bias in observational studies. Sankhya 35:417-46.
14. Imbens, Guido W (2004) Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and statistics 86:4-29.
15. Flury BK, Reidwyl H (1986) Standard distance in univariate and multivariate analysis. Am Statist40:249-51.
16. Austin PC, Mamdani1 MM (2006) A comparison of propensity score methods: A case-study estimating the effectiveness of post-AMI statin use. Stat Med 25:2084-106.
17. Ekundayo OJ, Adamopoulos C, Ahmed MI, Pitt B, Young JB, et al (2010) Oral potassium supplement use and outcomes in chronic heart failure: a propensitymatched study. Int J Cardiol141:167-74.
18. Austin PC, Grootendorst P, Anderson GM (2007) A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study. Stat Med 26:734-53.
19. Imai K, King G, Stuart EA (2008) Misunderstandings among experimentalists and observationalists about causal inference. J Roy Statist Soc Ser A Stat 171: 481-502.