Advances in propensity score analysis

Ko DT Mamdani M Alter DA. Lipid-lowering therapy with statins in high-risk elderly patients: the treatment-risk paradox. J Am Med Assoc 2004; 291: 1864–1870.

Lee DS Tu JV Juurlink DN , et al. Risk-treatment mismatch in the pharmacotherapy of heart failure. J Am Med Assoc 2005; 294: 1240–1247.

Rosenbaum PR Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70: 41–55.

Rosenbaum PR Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Med Assoc 1984; 79: 516–524.

Austin PC Mamdani MM Stukel TA , et al. The use of the propensity score for estimating treatment effects: administrative versus clinical data. Stat Med 2005; 24: 1563–1578.

Austin PC. An introduction to propensity-score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res 2011; 46: 399–424.

Senn S. Testing for baseline balance in clinical trials. Stat Med 1994; 13: 1715–1726.

Rubin DB. The use of matched sampling and regression adjustment to remove bias in observational studies. Biometrics 1973; 29: 185–203.

Rubin DB. Using multivariate matched sampling and regression adjustment to control bias in observational studies. J Am Med Assoc 1979; 74: 318–328.

Rubin DB Thomas N. Combining propensity score matching with additional adjustments for prognostic covariates. J Am Med Assoc 2000; 95: 573–585.

Rubin DB. The use of matched sampling and regression adjustment in observational studies. Cambridge, MA: Harvard University, 1970.

Imbens GW. Nonparametric estimation of average treatment effects under exogeneity: a review. Rev Econ Stat 2004; 86: 4–29.