References
- Austin, Peter C. 2009. “Balance Diagnostics for Comparing the Distribution of Baseline Covariates between Treatment Groups in Propensity-Score Matched Samples.” Statistics in Medicine 28 (25): 3083–3107. doi: https://doi.org/10.1002/sim.3697.
- Austin, Peter C. 2011. “An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies.” Multivariate Behavioral Research 46 (3): 399–424. doi: https://doi.org/10.1080/00273171.2011.568786.
- Austin, Peter C., Paul Grootendorst, and Geoffrey M. Anderson. 2007. “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 26 (4): 734–753. doi: https://doi.org/10.1002/sim.2580.
- Austin, Peter C., Muhammad M. Mamdani, Therese A. Stukel, and Geoffrey M. Anderson, and Jack V. Tu. 2005. “The Use of the Propensity Score for Estimating Treatment Effects: Administrative versus Clinical Data.” Statistics in Medicine 24 (10): 1563–1578. doi: https://doi.org/10.1002/sim.2053.
- Austin, Peter C., and Elizabeth A. Stuart. 2015. “Moving towards Best Practice When Using Inverse Probability of Treatment Weighting (IPTW) Using the Propensity Score to Estimate Causal Treatment Effects in Observational Studies.” Statistics in Medicine 34 (28): 3661–3679. doi: https://doi.org/10.1002/sim.6607.
- Austin, Peter C., and Elizabeth A. Stuart. 2017. “The Performance of Inverse Probability of Treatment Weighting and Full Matching on the Propensity Score in the Presence of Model Misspecification When Estimating the Effect of Treatment on Survival Outcomes.” Statistical Methods in Medical Research 26 (4): 1654–1670. SAGE Publications Ltd STM:doi: https://doi.org/10.1177/0962280215584401.
- Bang, Heejung, and James M. Robins. 2005. “Doubly Robust Estimation in Missing Data and Causal Inference Models.” Biometrics 61 (4): 962–973. doi: https://doi.org/10.1111/j.1541-0420.2005.00377.x.
- Bender, Ralf, Thomas Augustin, and Maria Blettner. 2005. “Generating Survival Times to Simulate Cox Proportional Hazards Models.” Statistics in Medicine 24 (11): 1713–1723. doi: https://doi.org/10.1002/sim.2059.
- Greifer, Noah, and Elizabeth A. Stuart. 2021. “Choosing the Estimand When Matching or Weighting in Observational Studies.” ArXiv:2106.10577 [Stat] June. http://arxiv.org/abs/2106.10577.
- Hernán, Miguel A, and James M. Robins. Causal Inference: What If. Boca Raton: Chapman & Hall/CRC; 2020.
- Hirano, Keisuke, and Guido W. Imbens. 2001. “Estimation of Causal Effects Using Propensity Score Weighting: An Application to Data on Right Heart Catheterization.” Health Services and Outcomes Research Methodology 2 (3/4): 259–278. doi: https://doi.org/10.1023/A:1020371312283.
- Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth A. Stuart. 2007. “Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference.” Political Analysis 15 (3): 199–236. doi: https://doi.org/10.1093/pan/mpl013.
- Kang, Joseph D. Y., and Joseph L. Schafer. 2007. “Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data.” Statistical Science 22 (4): 523–539. doi: https://doi.org/10.1214/07-STS227.
- Kurth, Tobias, Alexander M. Walker, Robert J. Glynn, K. Arnold Chan, J. Michael Gaziano, Klaus Berger, and James M. Robins. 2006. “Results of Multivariable Logistic Regression, Propensity Matching, Propensity Adjustment, and Propensity-Based Weighting under Conditions of Nonuniform Effect.” American Journal of Epidemiology 163 (3): 262–270. doi: https://doi.org/10.1093/aje/kwj047.
- Lee, Brian K., Justin Lessler, and Elizabeth A. Stuart. 2011. “Weight Trimming and Propensity Score Weighting.” Plos ONE 6 (3): e18174. doi: https://doi.org/10.1371/journal.pone.0018174.
- Lunceford, Jared K., and Marie Davidian. 2004. “Stratification and Weighting via the Propensity Score in Estimation of Causal Treatment Effects: A Comparative Study.” Statistics in Medicine 23 (19): 2937–2960. doi: https://doi.org/10.1002/sim.1903.
- Lunt, Mark, Daniel Solomon, Kenneth Rothman, Robert Glynn, Kimme Hyrich, Deborah P. M. Symmons, and Til Stürmer, and the British Society for Rheumatology Biologics Register Control Centre Consortium the British Society for Rheumatology Biologics Register. 2009. “Different Methods of Balancing Covariates Leading to Different Effect Estimates in the Presence of Effect Modification.” American Journal of Epidemiology 169 (7): 909–917. doi: https://doi.org/10.1093/aje/kwn391.
- Otsuru, Mitsunobu, Yoshihide Ota, Souichi Yanamoto, Masaya Okura, Masahiro Umeda, Tadaaki Kirita, Hiroshi Kurita, et al. 2019. “A Multicenter Retrospective Study of Elective Neck Dissection for T1-2N0M0 Tongue Squamous Cell Carcinoma: Analysis Using Propensity Score-Matching.” Annals of Surgical Oncology 26 (2): 555–563. doi: https://doi.org/10.1245/s10434-018-07089-7.
- Pirracchio, R., M. Carone, M. Resche Rigon, E. Caruana, A. Mebazaa, and S. Chevret. 2016. “Propensity Score Estimators for the Average Treatment Effect and the Average Treatment Effect on the Treated May Yield Very Different Estimates.” Statistical Methods in Medical Research 25 (5): 1938–1954. doi: https://doi.org/10.1177/0962280213507034.
- Rosenbaum, Paul R., and Donald B. Rubin. 1983. “The Central Role of the Propensity Score in Observational Studies for Causal Effects.” Biometrika 70 (1): 41–55. doi: https://doi.org/10.1093/biomet/70.1.41.
- Rosenbaum, Paul R., and Donald B. Rubin. 1984. “Reducing Bias in Observational Studies Using Subclassification on the Propensity Score.” Journal of the American Statistical Association 79 (387): 516–524. [American Statistical Association, Taylor & Francis, Ltd.]: doi: https://doi.org/10.2307/2288398.
- Schafer, Joseph L., and Joseph Kang. 2008. “Average Causal Effects from Nonrandomized Studies: A Practical Guide and Simulated Example.” Psychological Methods 13 (4): 279–313. doi: https://doi.org/10.1037/a0014268.
- Stuart, Elizabeth A. 2010. “Matching Methods for Causal Inference: A Review and a Look Forward.” Statistical Science: A Review Journal of the Institute of Mathematical Statistics 25 (1): 1–21. doi: https://doi.org/10.1214/09-STS313.
- Westreich, Daniel, Stephen R. Cole, Michele Jonsson Funk, M. Alan Brookhart, and Til Stürmer. 2011. “The Role of the C-Statistic in Variable Selection for Propensity Score Models.” Pharmacoepidemiology and Drug Safety 20 (3): 317–320. doi: https://doi.org/10.1002/pds.2074.