The use of bootstrapping when using propensity-score matching without replacement: A simulation study

Statistics in Medicine

Volumn 33, Issue 24, 2014, Pages 4306-4319

  Austin, Peter C ^a,b,c   Small, Dylan S ^d  

^a INSTITUTE FOR CLINICAL EVALUATIVE SCIENCES   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

^c Canada   (Canada)

^d UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Bootstrap; Matching; Monte Carlo simulations; Propensity score; Propensity score matching; Variance estimation

Indexed keywords

ALGORITHM; ARTICLE; BOOTSTRAPPING; DATA ANALYSIS; HAZARD RATIO; K NEAREST NEIGHBOR; MATHEMATICAL MODEL; MONTE CARLO METHOD; NORMAL DISTRIBUTION; PROBABILITY; PROPENSITY SCORE; SAMPLING; STATISTICAL SIGNIFICANCE; SURVIVAL; TREATMENT OUTCOME; VALIDATION PROCESS; COMPUTER SIMULATION; CONFIDENCE INTERVAL; HUMAN; OBSERVATIONAL STUDY; PROCEDURES; STATISTICAL ANALYSIS;

COMPUTER SIMULATION; CONFIDENCE INTERVALS; DATA INTERPRETATION, STATISTICAL; HUMANS; MONTE CARLO METHOD; OBSERVATIONAL STUDIES AS TOPIC; PROPENSITY SCORE;

EID: 84908056108     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.6276     Document Type: Article

References (35)

1. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70:41-55.
2. Austin PC. A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Statistics in Medicine 2008; 27(12):2037-2049.
3. Austin PC. A report card on propensity-score matching in the cardiology literature from 2004 to 2006: a systematic review and suggestions for improvement. Circulation: Cardiovascular Quality and Outcomes 2008; 1:62-67.
4. Austin PC. Propensity-score matching in the cardiovascular surgery literature from 2004 to 2006: a systematic review and suggestions for improvement. Journal of Thoracic and Cardiovascular Surgery 2007; 134(5):1128-1135.
5. Rosenbaum PR, Rubin DB. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician 1985; 39:33-38.
6. Schafer JL, Kang J. Average causal effects from nonrandomized studies: a practical guide and simulated example. Psychological Methods 2008; 13(4):279-313.
7. Gayat E, Resche-Rigon M, Mary JY, Porcher R. Propensity score applied to survival data analysis through proportional hazards models: a Monte Carlo study. Pharmaceutical Statistics 2012; 11(3):222-229.
8. Austin PC. Type I error rates, coverage of confidence intervals, and variance estimation in propensity-score matched analyses. International Journal of Biostatistics 2009; 5(1).
9. Austin PC. Comparing paired vs non-paired statistical methods of analyses when making inferences about absolute risk reductions in propensity-score matched samples. Statistics in Medicine 2011; 30(11):1292-1301.
10. Austin PC. The performance of different propensity score methods for estimating marginal hazard ratios. Stastisics in Medicine 2013; 32(16):2837-2849.
11. Efron B, Tibshirani RJ. An Introduction to the Bootstrap. Chapman & Hall: New York, NY, 1993.
12. Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharmaceutical Statistics 2010; 10:150-161.
13. Rosenbaum PR. Observational Studies. Springer-Verlag: New York, NY, 2002.
14. Agresti A, Min Y. Effects and non-effects of paired identical observations in comparing proportions with binary matchedpairs data. Statistics in Medicine 2004; 23(1):65-75.
15. Lin DY, Wei LJ. The robust inference for the proportional hazards model. Journal of the American Statistical Association 1989; 84:1074-1078.
16. Austin PC. A data-generation process for data with specified risk differences or numbers needed to treat. Communications in Statistics - Simulation and Computation 2010; 39:563-577.
17. Bender R, Augustin T, Blettner M. Generating survival times to simulate Cox proportional hazards models. Statistics in Medicine 2005; 24(11):1713-1723.
18. Austin PC, Stafford J. The performance of two data-generation processes for data with specified marginal treatment odds ratios. Communications in Statistics - Simulation and Computation 2008; 37:1039-1051.
19. Austin PC, Grootendorst P, Anderson GM. A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study. Statistics in Medicine 2007; 26(4): 734-753.
20. Rosner B. Fundamentals of Biostatistics. Duxbury Press: Belmont, CA, 1995.
21. Rosenbaum PR. Model-based direct adjustment. Journal of the American Statistical Association 1987; 82:387-394.
22. Abadie A, Imbens GW. Matching on the estimated propensity score. National Bureau of Economic Research 2009; NBER Working Paper Series(15301).
23. Imbens GW. Nonparametric estimation of average treatment effects under exogeneity: a review. Review of Economics and Statistics 2004; 86:4-29.
24. Abadie A, Imbens GW. Notes and comments on the failure of the bootstrap for matching estimators. Econometrica 2008; 76(6):1537-1557.
25. Austin PC. Double propensity-score adjustment: A solution to bias due to incomplete matching. Statistical Methods in Medical Research In-press. DOI: 10.1177/0962280214543508.
26. Hill J, Reiter JP. Interval estimation for treatment effects using propensity score matching. Statistics in Medicine 2006; 25(13):2230-2256.
27. Gu XS, Rosenbaum PR. Comparison of multivariate matching methods: structures, distances, and algorithms. Journal of Computational and Graphical Statistics 1993; 2:405-420.
28. Austin PC. A comparison of 12 algorithms for matching on the propensity score. Statisics in Medicine 2013; 33(6): 1057-1069.
29. Lee BK, Lessler J, Stuart EA. Improving propensity score weighting using machine learning. Statistics in Medicine 2010; 29(3):337-346.
30. Setoguchi S, Schneeweiss S, Brookhart MA, Glynn RJ, Cook EF. Evaluating uses of data mining techniques in propensity score estimation: a simulation study. Pharmacoepidemiolgy and Drug Safety 2008; 17(6):546-555.
31. Austin PC, Grootendorst P, Normand SL, Anderson GM. Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study. Statistics in Medicine 2007; 26(4):754-768.
32. Austin PC. The performance of different propensity score methods for estimating marginal odds ratios. Statistics in Medicine 2007; 26(16):3078-3094.
33. Austin PC. The performance of different propensity-score methods for estimating relative risks. Journal of Clinical Epidemiology 2008; 61(6):537-545.
34. Austin PC. The performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies. Statistics in Medicine 2010; 29(20):2137-2148.
35. Hansen BB. Full matching in an observational study of coaching for the SAT. Journal of the American Statistical Association 2004; 99(467):609-618.