References
- Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med. 2009;28(25):3083–3107.
- Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399–424.
- Benson K, Hartz AJ. A comparison of observational studies and randomized controlled trials. New Engl J Med. 2000;342(25):1878–1886.
- Bloch DA, Segal MR. Empirical comparison of approaches to forming strata: using classification trees to adjust for covariates. J Am Stat Assoc. 1989;84(408):897–905.
- Breslow NE, Day NE. Statistical Methods in Cancer Research. Vol 1. The Analysis of Case-Control Studies. Lyon, France: IARC Scientific Publication 32; 1980.
- Campbell MJ. What is propensity score modelling? Emerg Med J. 2017;34(3):129–131.
- Cochran WG. The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics. 1968;24(2):295–313.
- Curtis LH, Hammill BG, Eisenstein EL, Kramer JM, Anstrom KJ. Using inverse probability-weighted estimators in comparative effectiveness analyses with observational databases. Med Care. 2007;45(10 Suppl 2):S103–S107.
- D’Agostino RB Jr. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med. 1998;17(19):2265–2281.
- D’Agostino RB Jr. Propensity scores in cardiovascular research. Circulation. 2007;115(17):2340–2343.
- Dehejia RH, Wahba S. Causal effects in nonexperimental studies: reevaluating the evaluation of training programs. J Am Stat Assoc. 1999;94(448):1053–1062.
- Drake C. Effects of misspecification of the propensity score on estimators of treatment effect. Biometrics. 1993;49(4):1231–1236.
- Faries DE, Leon AC, Haro JM, Obenchain RL. Analysis of Observational Health Care Data using SAS®. Cary, NC: SAS Institute Inc.; 2010.
- Fiebach NH, Cook EF, Lee TH, et al. Outcomes in patients with myocardial infarction who are initially admitted to stepdown units: data from the Multicenter Chest Pain Study. Am J Med. 1990;89(1):15–20.
- Hansen BB. Full matching in an observational study of coaching for the SAT. J Am Stat Assoc. 2004;99(467):609–618.
- Helmreich JE, Pruzek RM. PSAgraphics: an R package to support propensity score analysis. J Stat Softw. 2009;29(6):1–23.
- Hill J, Reiter JP. Interval estimation for treatment effects using propensity score matching. Stat Med. 2006;25(13):2230–2256.
- Hirano K, Imbens GW, Ridder G. Efficient estimation of average treatment effects using the estimated propensity score. Econometrica. 2003;71(4):1161–1189.
- Ho DE, Imai K, King G, Stuart EA. MatchIt: nonparametric preprocessing for parametric causal inference. J Stat Softw. 2011;42(8):1–28.
- Hsieh FY, Bloch DA, Larsen MD. A simple method of sample size calculation for linear and logistic regression. Stat Med. 1998;17(14):1623–1634.
- Imbens GW. Nonparametric estimation of average treatment effects under exogeneity: a review. Rev Econ Stat. 2004;86(1):4–29.
- Lunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat Med. 2004;23(19):2937–2960.
- Morgan SL, Winship C. Counterfactuals and Causal Inference: Methods and Principles for Social Research. New York: Cambridge University Press; 2007.
- Muller JE, Turi ZG, Stone PH, et al; MILIS Study Group. Digoxin therapy and mortality after myocardial infarction. Experience in the MILIS Study. N Engl J Med. 1986;314(5):265–271.
- Myers WO, Gersh BJ, Fisher LD, et al. Time to first new myocardial infarction in patients with mild angina and three-vessel disease comparing medicine and early surgery: a CASS registry study of survival. Coronary Artery Surgery Study. Ann Thorac Surg. 1987;43(6):599–612.
- Robins JM, Greenland S. The role of model selection in causal inference from nonexperimental data. Am J Epidemiol. 1986;123(3):392–402.
- Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11(5):550–560.
- Rosenbaum PR. Model-based direct adjustment. J Am Stat Assoc. 1987;82(398):387–394.
- Rosenbaum PR. A characterization of optimal designs for observational studies. J R Stat Soc Series B (Methodol). 1991;53(3):597–610.
- Rosenbaum PR. Observational Studies. 2nd ed. New York: Springer-Verlag; 2002.
- Rosenbaum PR. Design of Observational Studies. New York: Springer; 2009.
- Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41–55.
- Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc. 1984;79(387):516–524.
- Rubin DB, Thomas N. Combining propensity score matching with additional adjustments for prognostic covariates. J Am Stat Assoc. 2000;95(450):573–585.
- Samet JH, Larson MJ, Horton NJ, Doyle K, Winter M, Saitz R. Linking alcohol and drug-dependent adults to primary medical care: a randomized controlled trial of a multi-disciplinary health intervention in a detoxification unit. Addiction. 2003;98(4):509–516.
- Sekhon JS. Multivariate and propensity score matching software with automated balance optimization: the matching package for R. J Stat Softw. 2011;42(7):1–52.
- Vansteelandt S, Daniel RM. On regression adjustment for the propensity score. Stat Med. 2014;33(23):4053–4072.
- von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med. 2007;147(8):573–577.